Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Libor Modelling and Pricing of Derivative Products

ARBITRAGE-FREE MODELLING OF EFFECTIVE INTEREST RATES Elements of Arbitrage Theory and Derivative Pricing Modelling of Effective Forward Rates Pricing of Caps and Swaptions in Libor and Swap Market Models PARAMETRISATION OF THE LIBOR MARKET MODEL General Volatility Structures (Quasi) Time-Shift Homogeneous Models Parametrisation of Correlation Structures Some Possible Applications of Parametric Structures IMPLIED CALIBRATION OF A LIBOR MARKET MODEL TO CAPS AND SWAPTIONS Orientation and General Aspects Assessment of the Calibration Problem LSq Calibration and Stability Issues in Practice Regularisation via a Collateral Market Criterion Calibration of a Time-Shift Homogeneous LMM PRICING OF EXOTIC EUROPEAN STYLE PRODUCTS Exotic European Style Products Factor Dependence of Exotic Products Case Studies PRICING OF BERMUDAN STYLE LIBOR DERIVATIVES Orientation The Bermudan Pricing Problem Backward Construction of the Exercise Boundary Iterative Construction of the Optimal Stopping Time Duality From Tight Lower Bounds to Tight Upper Bounds Monte Carlo Simulation of Upper Bounds Numerical Evaluation of Bermudan Swaptions by Different Methods Efficient Monte Carlo Construction of Upper Bounds Multiple Callable Structures PRICING LONG DATED PRODUCTS VIA LIBOR APPROXIMATIONS Introduction Different Lognormal Approximations Direct Simulation of Lognormal Approximations Efficiency Gain with Respect to SDE Simulation an Optimal Simulation Program Practical Simulation Examples Summarisation and Final Remarks APPENDIX Glossary of Stochastic Calculus Minimum Search Procedures Additional Proofs REFERENCES INDEX
Neural stem cells, inflammation and NF-κB: basic principle of maintenance and repair or origin of brain tumours?

• Neural Stem Cells ‐ NF‐κB ‐ TNF‐α ‐ NF‐κB activation via TNF‐α • Inflammation, NF‐κB and neural stem cells • Inflammation, NF‐κB and cancer • NSCs and brain cancer • NF‐κB target genes affecting NSCs and tumour formation • Conclusion
Multimodal integration of natural gaze behavior for intention recognition during object manipulation

Naturally gaze is used for visual perception of our environment and gaze movements are mainly controlled subconsciously. Forcing the user to consciously diverge from that natural gaze behavior for interaction purposes causes high cognitive workload and destroys information contained in natural gaze movements. Instead of proposing a new gaze-based interaction technique, we analyze natural gaze behavior during an object manipulation task and show ways how it can be used for intention recognition, which provides a universal basis for integrating gaze into multimodal interfaces for different applications. We propose a model for multimodal integration of natural gaze behavior and evaluate it for two different use cases, namely for improvement of robustness of other potentially noisy input cues and for the design of proactive interaction techniques.
TIF 1 g Suppresses Tumor Progression by RegulatingMitoticCheckpoints andChromosomal Stability

The transcription accessory factor TIF1g/TRIM33/RFG7/PTC7/ Ectodermin functions as a tumor suppressor that promotes development and cellular differentiation. However, its precise function in cancer has been elusive. In the present study, we report that TIF1g inactivation causes cells to accumulate chromosomal defects, a hallmark of cancer, due to attenuations in the spindle assembly checkpoint and the post-mitotic checkpoint. TIF1g deficiency also caused a loss of contact growth inhibition and increased anchorage-independent growth in vitro and in vivo. Clinically, reduced TIF1g expression in human tumors correlated with an increased rate of genomic rearrangements. Overall, our work indicates that TIF1g exerts its tumor-suppressive functions in part by promoting chromosomal stability. Cancer Res; 75(20);
Selecting Skyline Web Services from Uncertain QoS

Quality of service (QoS) has been considered as a significant criterion for selecting among functionally similar Web services. Recent approaches focus on computing the skyline over a set of QoS attributes. This can completely free users from assigning weights to QoS attributes. However, these approaches are not sufficient in a dynamic Web service environment where the delivered QoS by a Web service is inherently uncertain. In this paper, we tackle the problem of skyline on uncertain QoS. We represent each QoS attribute of a Web service using a possibility distribution and introduce two skyline extensions on uncertain QoS called pos-dominant skyline and nec-dominant skyline. We then develop appropriate algorithms to efficiently compute both the pos-dominant skyline and nec-dominant skyline. Finally, we present our experimental results that show both the effectiveness of the introduced skyline extensions and the efficiency of the proposed algorithms.
Learning to grasp familiar objects using object view recognition and template matching

Robots are still not able to grasp all unforeseen objects. Finding a proper grasp configuration, i.e. the position and orientation of the arm relative to the object, is still challenging. One approach for grasping unforeseen objects is to recognize an appropriate grasp configuration from previous grasp demonstrations. The underlying assumption in this approach is that new objects that are similar to known ones (i.e. they are familiar) can be grasped in a similar way. However finding a grasp representation and a grasp similarity metric is still the main challenge in developing an approach for grasping familiar objects. In this paper, interactive object view learning and recognition capabilities are integrated in the process of learning and recognizing grasps. The object view recognition module uses an interactive incremental learning approach to recognize object view labels. The grasp pose learning approach uses local and global visual features of a demonstrated grasp to learn a grasp template associated with the recognized object view. A grasp distance measure based on Mahalanobis distance is used in a grasp template matching approach to recognize an appropriate grasp pose. The experimental results demonstrate the high reliability of the developed template matching approach in recognizing the grasp poses. Experimental results also show how the robot can incrementally improve its performance in grasping familiar objects.
Multivariate Spatial Outlier Detection

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.
The Dynamic Properties of Inflation Targeting Under Uncertainty

We study the implications of uncertainty for inflation targeting in a dynamic set-up. Using Svensson's inflation forecast targeting model, we compare the Brainard conservative principle to a more active monetary policy rule, derived from a two-step optimisation procedure. Our analysis points to a trade-o¤ between the ability to control expectations and the introduction of greater variability in the system. We show that Brainard's attenuation principle is optimal only in a backward looking set-up where there is no role for expectations in the determination of inflation equilibrium. On the other hand, we show that in a forward looking model, Brainard's conservative principle may produce instability, because of its inability to control expectations. A more aggressive rule, like the one derived in the paper, can instead provide greater stability because it providesfor a better and more direct management of expectations, despite the uncertainty in the transmission parameters. In that respect, we show that there are conditions under which the bene…ts of tying down expectations, more than compensate the costs of having to overuse the instrument.
The Escherichia coli Translation-Associated Heat Shock Protein YbeY Is Involved in rRNA Transcription Antitermination

A new group of translation-associated heat shock genes has been recently identified. One of these novel genes is ybeY which is highly conserved in bacteria. In Escherichia coli the YbeY protein is important for efficient translation at all temperatures and is essential at high temperatures. Deletion mutants of ybeY are defective in protein translation, due to impaired 30 S ribosomal subunits. Here we provide evidence which tie YbeY to the transcription antitermination process. Thus, in ybeY deletion mutants transcription is significantly inhibited when the "nut like" sequences required for transcriptional antitermination are present, while if these sequences are removed transcription is not affected by the mutation.
Diversity Maximization via Composable Coresets

Given a set S of points in a metric space, and a diversity measure div( ) dened over subsets of S, the goal of the diversity maximization problem is to nd a subset T S of size k that maximizes div(T ). Motivated by applications in massive data processing, we consider the composable coreset framework in which a coreset for a diversity measure is called -composable, if for any collection of sets and their corresponding coresets, the maximum diversity of the union of the coresets approximates the maximum diversity of the union of the sets. We present composable coresets with near-optimal approximation factors for several notions of diversity, including remote-clique, remote-cycle, and remote-tree. We also prove a general lower bound on the approximation factor of composable coresets for a large class of diversity maximization problems.
Privacy- Preserving Keyword-based Semantic Search over Encrypted Cloud Data

To protect the privacy, sensitive information has to be encrypted before outsourcing to the cloud. Thus the effective data utilization becomes a significant challenge. Searchable encryption scheme has been developed to conduct retrieval over encrypted data. However, these schemes only support exact keyword search. Recent fuzzy search schemes mainly evaluate the similarity of keywords from the structure but the semantic relatedness is not considered. Our work focuses on realizing secure semantic search through query keyword semantic extension. Based on the co-occurrence probability of terms, the semantic relationship library is constructed to record the semantic similarity between keywords. We exploited architecture of two clouds, namely private cloud and public cloud. The search operation is divided into two steps. The first step expands the query keyword upon SRL stored in the private cloud. The second step uses the extended query keywords set to retrieve the index on public cloud. Finally the matched files are returned in order. Detailed security analysis shows that our solution is privacy-preserving and secure. Experimental evaluation demonstrates the efficiency and effectives of the scheme.
Cylindrical cam mechanism for unlimited subsequent spring recruitment in Series-Parallel Elastic Actuators

Series-Parallel Elastic Actuators (SPEA) enable variable recruitment of parallel springs and variable load cancellation. In previous work, we validated a MACCEPA-based SPEA prototype with a self-closing intermittent mechanism, to reduce motor load and improve energy efficiency. However, the mechanism only allowed for 4 parallel springs and a limited equilibrium angle range, which limits the variable load cancellation and operation range. Therefore, we developed a novel cylindrical cam mechanism for unlimited subsequent spring recruitment. This paper describes and validates the working principle of the cylindrical cam mechanism. Furthermore, the latest MACCEPA-based SPEA is presented with a maximum output torque of 40Nm and variable stiffness. Additive and traditional manufacturing techniques go hand in hand to overcome the actuator's complexity. The experiments endorse the working principle, demonstrate the variable stiffness, and prove the motor torque can be reduced to 5Nm while an output torque of 40Nm can be achieved.
Document representation refinement for precise region description

Precise description of layout entities (content regions on a page) is crucial for all but the most trivial document analysis and recognition applications. The output of layout analysis methods and state-of-the-art OCR systems varies significantly, from bounding boxes (e.g. Tesseract) to stacks of text line rectangles (e.g. ABBYY FineReader). There is a clear need for a consistent and accurate representation of regions (e.g. text paragraphs, graphics entities etc.) for further processing, correction and performance evaluation (comparison of segmentation results with ground truth regions). This paper describes a method for refinement of document representations by fitting polygons around lower-level layout objects (such as text lines, words and glyphs) in a systematic way that reconstructs region outlines and preserves the fine details of complex layouts. Experimental results on a standard dataset demonstrate the validity and usefulness of the proposed approach.
Microchimerism in Graves' Disease

Microchimerism is the presence of cells from one individual in another genetically distinct individual. Pregnancy is the main cause of natural microchimerism through transplacental bidirectional cell trafficking between mother and fetus. The consequences of pregnancy-related microchimerism are under active investigation. However, many authors have suggested a close relationship linking fetal microchimerism and the development of autoimmune diseases. It has been more than ten years now since the demonstration of the presence of a significant high number of fetal microchimeric cells residing in thyroid glands from operated patients with Graves' disease. This intrathyroidal fetal microchimerism is an attractive candidate mechanism for the modulation of Graves' disease in pregnancy and the postpartum period.
Molecular Dynamics study of the traction-displacement relations of epoxy-copper interfaces

The traction-displacement relations of the epoxy-copper interfaces are studied using an atomistic model. The reaction force of the epoxy layer in response to displacement of the interface is calculated during molecular dynamics simulation. A parametric study in terms of displacement rate and the step size of displacement increment has been performed. The traction-displacement relations are found sensitive to the step size of the displacement increment. The traction-displacement relations are better described with a small displacement increment in the initial region where the epoxy-copper interface is in close contact. The interfacial energy as calculated by the traction-displacement model is −0.28 Jm−2 which is comparable to the value obtained from a static model. This calculated value is also close to the thermodynamic work of adhesion (−0.26 Jm−2) of an epoxy-copper system as reported in the literature [1].
Wood defect recognition with self-organizing feature selection

In the modern sawmill industry automatic grading of the products is one of the key issues in increasing the production quality. The surface defects that determine the grading are identified according to the physiological origin of the defect, such as dry, encased or decayed knot. Variations within the classes are large since the knots can have different shapes, sizes and color, and each class has different discriminating features. Classification of the defects using pattern recognition techniques has turned out to be rather difficult, since it is difficult to determine the suitable features that would correlate with the physiological defect types. In this paper we describe a wood defect classification system that is based on self-organizing feature construction and neural network classification. Due to the automatic, unsupervised learning of the features, the system is easily adaptable to different tasks, such as inspection of lumber or veneer, with different tree species and different cutting processes. Performance of the classification system was evaluated with a set of over 400 samples from spruce boards. The knot recognition rate was about 85% with only gray level images, giving about 90% accuracy for the final board grading. Compared to 75 - 80% accuracy that can be maintained by a human inspector, the result can be considered good.
A Convex Lower Bound for the Real ${\bf l}_2$ Parametric Stability Margin of Linear Control Systems With Restricted Complexity Controllers

In this note the problem of restricted complexity stability margin maximization (RCSMM) for single-input-single-output (SISO) plants affected by rank one real perturbations is considered. This problem amounts to maximizing the real l2 parametric stability margin over an assigned class of restricted complexity controllers, which are described by rational transfer functions of fixed order with coefficients depending affinely on some free parameters. It is shown that the RCSMM problem, which is nonconvex in general, can be approached by means of convex optimization methods. Specifically, a lower bound of the stability margin, whose maximization can be accomplished via linear matrix inequality (LMI) techniques, is developed
Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry.
Effective Demand and Endogenous Money in a Path-Dependent Economy: Towards a ‘Moorian’ Credit Supply Curve – and a Reconciliation between Horizontalists and Structuralists?

During a distinguished career, Basil Moore has made numerous important contributions to macroeconomics and monetary economics, and is renowned as the progenitor of the ‘horizontalist’ analysis of endogenous money. More recently, he has embraced complexity theory as part of an ongoing effort to understand macroeconomics as an evolving, path-dependent process. This book celebrates and explores Basil Moore’s interests in and contributions to monetary and macroeconomic theory.
Enabling Interface Validation through Text Generation

To obtain the information encoded in software it is necessary to master both the implementation languages and the tools. This is not only a problem for managers and user groups who have a claim in the outcome but not the necessary training in software development to decode the implementation our case study shows that it is also a problem for the software developers. In contrast, text can be understood by everyone. The generation of textual summaries from software artifacts enables a more accessible format for validating software. Based on findings from interviewing practitioners in industry, we have developed a prototype for generating natural language summaries from component interfaces for validation purposes. Keywords—Natural language processing; Reverse engineering; Software components;
WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data

We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM. WISDOM X provides numerous possible answers including unpredictable ones to widely diverse natural language questions to provide deep insights about a broad range of issues. DISAANA and D-SUMM enable us to assess the damage caused by large-scale disasters in real time using Twitter as an information source.
The design of architecture, workflow, algorithm on grid system for Social Network context prediction analysis

In Telecom Social Network, the Context-based Prediction Analysis is an important topic, and computing of some context parameters using great amount of data is a problem so far. Architecture of DMG (Data Mining Grid) is proposed and prototype of MDG is designed to solve the computing problem. Two of the important issues in DMG, which are the design of the workflow service in DMG and the distributed data mining algorithm, are investigated. Finally a sample application in telecom field Customer Churn Context Prediction Analysis is investigated and illustrated. In the sample application, Centrals, which is computed by parallel algorithm on DMG, is an important measure in Telecom Social Network. And it is an important context parameter proved by the experiment, which benefits to enhance the precise of context-based prediction of the customer churn in telecom social network.
Identifying Market Preferences for High Selenium Beef

Selenium is an element found in relatively high concentrations in crops and livestock raised on high-selenium soils located in North and South Dakota. Evidence suggests that a high-selenium diet such as would be obtained from consuming these products can reduce the risk of certain cancers. The region's livestock and grain producers are exploring potential high-selenium product marketing opportunities. A choice experiment was conducted to identify preferred attributes for a high-selenium beef product and the characteristics of potential market segments. In a national survey, participants chose between different levels of health claim approval and research, prices, and selenium origin. A multinomial logit regression model was estimated. Labeling reflecting scientific support linking selenium and reduced cancer risk, and natural-source selenium was ineffective. Marketing opportunities identified are consistent with existing functional food market segments and include consumers with higher income and education, 45 to 55 years of age, and with children.
High sensitivity interferometer for on-axis detection of AFM cantilever deflection

A homodyne path stabilised Michelson based interferometer displacement sensor was developed. This sensor achieved a noise floor of 100 fm/√Hz, for frequencies higher than 100 KHz. A prototype AFM that integrated this sensor was developed. Using tapping mode, topography maps of an AFM test grid were produced.