Miracle-dz/UniFAR-FTUs huggingface dataset:
Black's Model of Interest Rates as Options, Eigenfunction Expansions and Japanese Interest Rates

Black's (1995) model of interest rates as options assumes that there is a shadow instantaneous interest rate that can become negative, while the nominal instantaneous interest rate is a positive part of the shadow rate due to the option to convert to currency. As a result of this currency option, all term rates are strictly positive. A similar model was independently discussed by Rogers (1995) . When the shadow rate is modeled as a diffusion, we interpret the zero‐coupon bond as a Laplace transform of the area functional of the underlying shadow rate diffusion (evaluated at the unit value of the transform parameter). Using the method of eigenfunction expansions, we derive analytical solutions for zero‐coupon bonds and bond options under the Vasicek and shifted CIR processes for the shadow rate. This class of models can be used to model low interest rate regimes. As an illustration, we calibrate the model with the Vasicek shadow rate to the Japanese Government Bond data and show that the model provides an excellent fit to the Japanese term structure. The current implied value of the instantaneous shadow rate in Japan is negative.
Exceptional Model Mining with Tree-Constrained Gradient Ascent

Exceptional Model Mining (EMM) generalizes the wellknown data mining task of Subgroup Discovery (SD). Given a model class of interest, the goal of EMM is to find subgroups of the data for which a model fitted to the subgroup deviates substantially from the global model. In both SD and EMM, heuristic search is often employed to circumvent the problem that exhaustive search of the subgroup description space is in general not feasible. We present a novel heuristic search strategy for EMM called tree-constrained gradient ascent (TCGA). It is designed specifically to exploit information about the influence of individual records on the quality of a subgroup, and guarantee at the same time that the subgroups can be described in the pattern language. By generalizing the notion of subgroups to that of soft subgroups, numerical optimization can be applied to find subgroups of high quality. We introduce a form of constrained gradient ascent that constructs the constraint in parallel with the numerical optimization so as to guarantee that the subgroups can be described in the pattern language, while restricting the numerical optimization as little as possible. We show how TCGA can be applied to EMM with linear regression models and to traditional SD. The proposed algorithm is experimentally evaluated on both synthetic and real world data sets. We compare the results to those obtained with beam search, a heuristic search algorithm commonly employed in SD and EMM.
Local controller synthesis for plug and play processes by decomposition

In this paper a methodology for the design of model based, stabilising controllers for a hierarchical plug and play process control system is presented. This is achieved by letting the local closed loop systems adhere to a specification, i.e., a family of systems given as an uncertain system. A simulation example, in which a supermarket refrigeration system is controlled during plug in of additional display cases is reported to show the potential of this approach for plug and play process control
Kinematical signatures of hidden stellar discs

The deprojection of the surface brightness distribution of an axisymmetric galaxy does not have a unique solution unless the galaxy is viewed precisely edge-on. I present an algorithm that finds the full range of smooth axisymmetric density distributions consistent with a given surface brightness distribution and inclination angle, and use it to investigate the effects of this non-uniqueness on the line-of-sight velocity profiles (VPs) of two-integral models of both real and toy disky galaxies viewed at a range of inclination angles. Photometrically invisible face-on disks leave very clear signatures in the minor-axis VPs of the models (Gauss--Hermite coefficients h_4>0.1), provided the disk-to-bulge ratio is greater than about 3%. I discuss the implications of these hitherto neglected disks for dynamical modelling.
Oxidized Phospholipids Induce Ceramide Accumulation in RAW 264.7 Macrophages: Role of Ceramide Synthases

Oxidized phospholipids (OxPLs), including 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine (PGPC) and 1-palmitoyl-2-oxovaleroyl-sn-glycero-3-phosphocholine (POVPC) are among several biologically active derivatives that are generated during oxidation of low-density lipoproteins (LDLs). These OxPLs are factors contributing to pro-atherogenic effects of oxidized LDLs (OxLDLs), including inflammation, proliferation and death of vascular cells. OxLDL also elicits formation of the lipid messenger ceramide (Cer) which plays a pivotal role in apoptotic signaling pathways. Here we report that both PGPC and POVPC are cytotoxic to cultured macrophages and induce apoptosis in these cells which is associated with increased cellular ceramide levels after several hours. In addition, exposure of RAW 264.7 cells to POVPC and PGPC under the same conditions resulted in a significant increase in ceramide synthase activity, whereas, acid or neutral sphingomyelinase activities were not affected. PGPC is not only more toxic than POVPC, but also a more potent inducer of ceramide formation by activating a limited subset of CerS isoforms. The stimulated CerS activities are in line with the C16-, C22-, and C24:0-Cer species that are generated under the influence of the OxPL. Fumonisin B1, a specific inhibitor of CerS, suppressed OxPL-induced ceramide generation, demonstrating that OxPL-induced CerS activity in macrophages is responsible for the accumulation of ceramide. OxLDL elicits the same cellular ceramide and CerS effects. Thus, it is concluded that PGPC and POVPC are active components that contribute to the capacity of this lipoprotein to elevate ceramide levels in macrophages.
Benefits of the Carbon‐Nutrient Balance Hypothesis

The carbon-nutrient balance hypothesis (CNBH) is one of a number of approaches to understanding patterns of resource allocation in plants. Numerous empirical tests of the CNBH's predictions have led to certain key refinements and to the recognition that some of the simplifications inherent in the model limit its utility. However, as long as the model is applied to compounds with large pools, and the biosynthetic pathways of secondary metabolites are considered, the CNBH still serves as a useful guide for ecological research on resource allocation. One of the model's values is that it attempts to explain the plasticity of individuals but does not assume that all responses of individuals are optimal in terms of maximizing fitness.
A Compiler Approach for Exploiting Partial SIMD Parallelism

Existing vectorization techniques are ineffective for loops that exhibit little loop-level parallelism but some limited superword-level parallelism (SLP). We show that effectively vectorizing such loops requires partial vector operations to be executed correctly and efficiently, where the degree of partial SIMD parallelism is smaller than the SIMD datapath width. We present a simple yet effective SLP compiler technique called Paver (PArtial VEctorizeR), formulated and implemented in LLVM as a generalization of the traditional SLP algorithm, to optimize such partially vectorizable loops. The key idea is to maximize SIMD utilization by widening vector instructions used while minimizing the overheads caused by memory access, packing/unpacking, and/or masking operations, without introducing new memory errors or new numeric exceptions. For a set of 9 C/C++/Fortran applications with partial SIMD parallelism, Paver achieves significantly better kernel and whole-program speedups than LLVM on both Intel’s AVX and ARM’s NEON.
Comprehensive ‘atomistic’ simulation of statistical variability and reliability in 14 nm generation FinFETs

In this paper, by using comprehensive statistical device simulation methodology, we investigate the effect of Statistical variability and reliability on the state of the art 14nm FinFET technology on important device figures of merit. Important sources of statistical variability have been considered in all simulation of fresh devices and various degradation levels are included in the reliability simulation cases. The interplay between the initial statistical variability introduced by random discrete dopants, line edge roughness and metal gate granularity and the statistical variability introduced by different level of trapped charges resulting from BTI degradation is studied in details. Results related to the time dependent variability and the correlation of key transistor figures of merit are also presented.
Petri Net Level WCET Analysis

We present an approach for Worst-Case Execution Time (WCET) Analysis of embedded system software that is generated from Petri net specifications. The main characteristic of the approach is that standard Petri net analysis methods are utilized in order to automatically derive additional flow information for WCET analysis. Furthermore, the approach presented in this paper clearly separates the analysis of model behavior from the other WCET analysis phases. The method is compared with similar approaches for WCET analysis on the model level. Furthermore, an application example is presented.
The Construction of an Intrinsic Coordinate System for Fingerprint Matching

In this paper, an intrinsic coordinate system is proposed for fingerprints. First, the singular points are extracted from the directional field of the fingerprint and their orientations are determined. Then, the fingerprint is partitioned in regular regions, which are regions that contain no singular points. In each regular region, the intrinsic coordinate system is defined by the directional field. Parallel flow lines are taken to define one axis and perpendicular flow lines for the other axis. Using the intrinsic coordinate system, minutiae positions are defined with respect to positions in the directional field, instead of using the pixel coordinates. The resulting intrinsic minutiae coordinates are used in a plastic-distortion-invariant fingerprint matching algorithm. The plastic distortions, caused by pressing the 3-dimensional elastic fingerprint surface on a flat sensor, now deform the entire coordinate system, leaving the intrinsic minutiae coordinates unchanged. Therefore, much stricter matching algorithms can be used, thus increasing the matching performance.
Algal evolution in relation to atmospheric CO2: carboxylases, carbon-concentrating mechanisms and carbon oxidation cycles

Oxygenic photosynthesis evolved at least 2.4 Ga; all oxygenic organisms use the ribulose bisphosphate carboxylase-oxygenase (Rubisco)–photosynthetic carbon reduction cycle (PCRC) rather than one of the five other known pathways of autotrophic CO2 assimilation. The high CO2 and (initially) O2-free conditions permitted the use of a Rubisco with a high maximum specific reaction rate. As CO2 decreased and O2 increased, Rubisco oxygenase activity increased and 2-phosphoglycolate was produced, with the evolution of pathways recycling this inhibitory product to sugar phosphates. Changed atmospheric composition also selected for Rubiscos with higher CO2 affinity and CO2/O2 selectivity correlated with decreased CO2-saturated catalytic capacity and/or for CO2-concentrating mechanisms (CCMs). These changes increase the energy, nitrogen, phosphorus, iron, zinc and manganese cost of producing and operating Rubisco–PCRC, while biosphere oxygenation decreased the availability of nitrogen, phosphorus and iron. The majority of algae today have CCMs; the timing of their origins is unclear. If CCMs evolved in a low-CO2 episode followed by one or more lengthy high-CO2 episodes, CCM retention could involve a combination of environmental factors known to favour CCM retention in extant organisms that also occur in a warmer high-CO2 ocean. More investigations, including studies of genetic adaptation, are needed.
An efficient fashion-driven learning approach to model user preferences in on-line shopping scenarios

In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text feature into a fashion-driven low dimensional space, compact yet very discriminative. On the learning stage, we describe a two step hybrid learning algorithm, that combines a discriminative model learned off-line over historical data, with an extremely efficient generative model, updated on-line according to the user behavior. Qualitative and quantitative analyses show promising results.
Coping with variability in motion based activity recognition

A key issue in the automatic recognition of human activities with body worn sensors is the variability of human motions and the huge space of possibilities for executing even fairly simple actions. In this article we introduce a new algorithm to address this issue. The core idea is that often even highly variable actions include short more or less invariant parts which are due to hard physical constraints. The aim is to develop a method that can identify such invariants and use them to improve the classification of the respective activities. The method is meant to be combined with existing classification approaches in an ensemble like fashion, being applied only to the classes for which appropriate invariants can be found and leaving the other classes to be handled by classical methods. We compare our method's results to prior publications on two well known data sets and are able to improve the classification in 5 of 23 respectively 4 of 19 classes, in same cases by a large margin (best case is from 27% to 76% in the first and from 50% to 64% in the second). In each data set there is only one class for which we make the recognition worse and in both cases it is one with poor results to start with and a relatively small decrease (from 54% to 45% in the first and from 65% to 62% in the second). The results are achieved for an user independent case.
Interaction Strategies for an Affective Conversational Agent

The development of embodied conversational agents (ECA) as companions brings several challenges for both affective and conversational dialogue. These include challenges in generating appropriate affective responses, selecting the overall shape of the dialogue, providing prompt system response times, and handling interruptions. We present an implementation of such a companion showing the development of individual modules that attempt to address these challenges. Further, to resolve resulting conflicts, we present encompassing interaction strategies that attempt to balance the competing requirements along with dialogues from our working prototype to illustrate these interaction strategies in operation. Finally, we provide the results of an evaluation of the companion using an evaluation methodology created for conversational dialogue and including analysis using appropriateness annotation.
Improving Log-based Field Failure Data Analysis of multi-node computing systems

Log-based Field Failure Data Analysis (FFDA) is a widely-adopted methodology to assess dependability properties of an operational system. A key step in FFDA is filtering out entries that are not useful and redundant error entries from the log. The latter is challenging: a fault, once triggered, can generate multiple errors that propagate within the system. Grouping the error entries related to the same fault manifestation is crucial to obtain realistic measurements. This paper deals with the issues of the tuple heuristic, used to group the error entries in the log, in multi-node computing systems. We demonstrate that the tuple heuristic can group entries incorrectly; thus, an improved heuristic that adopts statistical indicators is proposed. We assess the impact of inaccurate grouping on dependability measurements by comparing the results obtained with both the heuristics. The analysis encompasses the log of the Mercury cluster at the National Center for Supercomputing Applications.
Adaptive fuzzy commitment scheme based on iris-code error analysis

Biometric cryptosystems is a group of emerging technologies that securely bind a digital key to a biometric so that no biometric image or template is stored. Focusing on iris biometrics several approaches have been proposed to bind keys to binary iris-codes where the majority of these approaches are based on the so-called fuzzy commitment scheme. In this work we present a new approach to constructing iris-based fuzzy commitment schemes. Based on intra-class error analysis iris-codes are rearranged in a way that error correction capacities are exploited more effectively. Experimental results demonstrate the worthiness of our approach.
Extracting executable transformations from distilled code changes

Change distilling algorithms compute a sequence of fine-grained changes that, when executed in order, transform a given source AST into a given target AST. The resulting change sequences are used in the field of mining software repositories to study source code evolution. Unfortunately, detecting and specifying source code evolutions in such a change sequence is cumbersome. We therefore introduce a tool-supported approach that identifies minimal executable subsequences in a sequence of distilled changes that implement a particular evolution pattern, specified in terms of intermediate states of the AST that undergoes each change. This enables users to describe the effect of multiple changes, irrespective of their execution order, while ensuring that different change sequences that implement the same code evolution are recalled. Correspondingly, our evaluation is two-fold. Using examples, we demonstrate the expressiveness of specifying source code evolutions through intermediate ASTs. We also show that our approach is able to recall different implementation variants of the same source code evolution in open-source histories.
Noninvasive imaging of immune responses

Significance Tumors are often surrounded and invaded by bone marrow-derived cells. Imaging the infiltration of such immune cells into tumors may therefore be an attractive means of detecting tumors or of tracking the response to anticancer therapy. We show that it is possible to detect these cells noninvasively by positron emission tomography (PET) via the surface markers displayed by them. The ability to monitor the immune response in the course of therapy will enable early determination of the efficacy of treatment and will inform decisions as to whether treatment should be stopped or continued. Noninvasive monitoring could therefore change how therapies are applied and assessed, to the benefit of many patients. At their margins, tumors often contain neutrophils, dendritic cells, and activated macrophages, which express class II MHC and CD11b products. The interplay between stromal cells, tumor cells, and migratory cells such as lymphocytes creates opportunities for noninvasive imaging of immune responses. We developed alpaca-derived antibody fragments specific for mouse class II MHC and CD11b products, expressed on the surface of a variety of myeloid cells. We validated these reagents by flow cytometry and two-photon microscopy to obtain images at cellular resolution. To enable noninvasive imaging of the targeted cell populations, we developed a method to site-specifically label VHHs [the variable domain (VH) of a camelid heavy-chain only antibody] with 18F or 64Cu. Radiolabeled VHHs rapidly cleared the circulation (t1/2 ≈ 20 min) and clearly visualized lymphoid organs. We used VHHs to explore the possibility of imaging inflammation in both xenogeneic and syngeneic tumor models, which resulted in detection of tumors with remarkable specificity. We also imaged the infiltration of myeloid cells upon injection of complete Freund’s adjuvant. Both anti-class II MHC and anti-CD11b VHHs detected inflammation with excellent specificity. Given the ease of manufacture and labeling of VHHs, we believe that this method could transform the manner in which antitumor responses and/or infectious events may be tracked.
Anatomy : Privacy and Correlation Preserving Publication

This article presents the anatomytechnique for anonymized publication of sensitive data. An atomy releases all the quasi-identifier and sensitive values dire ctly in two separate tables. Combined with a grouping mechanism, this approach effectively protects pr ivacy, and captures a large amount of correlation in the microdata. We propose an efficient algorithm for c mputing anatomized tables that fulfill the l-diversity anonymity requirement, and minimize the error o f econstructing the microdata, according to anyLp norm, the KL-divergence, and the discernability metrics. T he algorithm is accompanied by optional heuristics that continuously enhance the data uti lity of anatomy, until a user-specified time limit has been reached. We also provide detailed explanations abo ut how to leverage anatomized tables to understand the characteristics of the microdata. Extensiv experiments confirm that anatomy allows significantly more accurate data analysis than conventional an onymization methods based on generalization and data swapping. The short version of this article appeared in VLDB 06. The cur rent submission improves our preliminary work by (i) including a thorough discussion of the previous m ethods, (ii) extending the analysis of anatomy to several other metrics of information loss (i.e., generic Lp norm, KL-divergence, and discernability), (iii) elaborating how to deploy the anonymized data for statistic al studies, (iv) presenting a new algorithm for computing anatomized tables, and (v) featuring a more compr ehensive experimental evaluation.
Interframe compression with selective update framework of local features for mobile augmented reality

Mobile augmented reality (MAR) systems utilize local features to represent structures such as corners or blobs in query videos and database images. Local features are compressed and sent to servers to perform query-database matching. The compression efficiency is important for the server-based MAR systems in terms of network latency. Conventional compression methods handle video frames independently. In recent years, an interframe compression technology, which exploits temporal correlation over frames, has been proposed. It propagates local features temporally and realizes significant bitrate reduction without degradation in matching performance. However, this system requires a large amount of information when inserting independently compressed frames to update the status of local features. In this paper, we propose the selective update framework for local features and give simple algorithms to make use of the framework. Experimental results show that our proposal achieves gradual refreshes of local features, avoiding transmission of large amounts of data.
Stability of switched systems with average dwell-time

It is shown that switching among stable linear systems results in a stable system provided that switching is "slow-on-the-average". In particular, it is proved that exponential stability is achieved when the number of switches in any finite interval grows linearly with the length of the interval, and the growth rate is sufficiently small. Moreover, the exponential stability is uniform over all switchings with the above property. For switched systems with inputs this guarantees that several input-to-state induced norms are bounded uniformly over all slow-on-the-average switchings. These results extend to classes of nonlinear switched systems that satisfy suitable uniformity assumptions. In this paper it is also shown that, in a supervisory control context, scale-independent hysteresis can produce switching that is slow-on-the-average and therefore the results mentioned above can be used to study the stability of hysteresis-based adaptive control systems.