Miracle-dz/UniFAR-FTUs huggingface dataset:
Asset Pricing under the Quadratic Class

Abstract We identify and characterize a class of term structure models where bond yields are quadratic functions of the state vector. We label this class the quadratic class and aim to lay a solid theoretical foundation for its future empirical application. We consider asset pricing in general and derivative pricing in particular under the quadratic class. We provide two general transform methods in pricing a wide variety of fixed income derivatives in closed or semi-closed form. We further illustrate how the quadratic model and the transform methods can be applied to more general settings.
Pressing Not Tapping: Comparing a Physical Button with a Smartphone App for Tagging Music in Radio Programmes

A physical hardware prototype--The Button was developed as a research probe to understand how radio audiences could discover, organise and consume music radio content at the touch of a physical button, the only control on a tiny handheld device. The Button allows listeners to tag tracks they like via a simple one-touch interaction method, and save them to a non-commercial online playlist service: BBC Playlister. Users can then export these tags to other music streaming platforms, such as Spotify, Deezer, etc. Following a user-centric design process, a large in-the-wild study was conducted over several weeks to investigate the value of the Button in aiding listeners' discovery of music. One group of participants was given a mobile phone app designed to facilitate tagging music heard on BBC radio stations; two other groups were given both the app and a Button (in one of two hardware versions). The findings revealed that Button users made significantly more tags on average than app users, indicating that a physical device could add significant value for radio listeners who want to tag music. Participants valued the simple one-touch interaction method, especially in situations where their smartphones were out of reach or contextual constraints meant that interaction with a complex device was undesirable or difficult.
Compression of Boolean Functions

We consider the problem of compression for “easy” Boolean functions: given the truth table of an n-variate Boolean function f computable by some unknown small circuit from a known class of circuits, find in deterministic time poly(2) a circuit C (no restriction on the type of C) computing f so that the size of C is less than the trivial circuit size 2/n. We get both positive and negative results. On the positive side, we show that several circuit classes for which lower bounds are proved by a method of random restrictions: • AC, • (de Morgan) formulas, and • (read-once) branching programs, allow non-trivial compression for circuits up to the size for which lower bounds are known. On the negative side, we show that compressing functions from any class C ⊆ P/poly implies superpolynomial lower bounds against C for a function in NEXP; we also observe that compressing monotone functions of polynomial circuit complexity or functions computable by large-size AC circuits would also imply new superpolynomial circuit lower bounds. Finally, we apply the ideas used for compression to get zero-error randomized #SATalgorithms for de Morgan and complete-basis formulas, as well as branching programs, on n variables of about quadratic size that run in expected time 2/2 e , for some e > 0 (dependent on the size of the formula/branching program). ∗Research partially supported by an NSERC Discovery grant. †Research partially supported by an NSERC Discovery grant. ISSN 1433-8092 Electronic Colloquium on Computational Complexity, Report No. 24 (2013)
A potential target gene for the host-directed therapy of mycobacterial infection in murine macrophages

Mycobacterium tuberculosis (MTB), one of the major bacterial pathogens for lethal infectious diseases, is capable of surviving within the phagosomes of host alveolar macrophages; therefore, host genetic variations may alter the susceptibility to MTB. In this study, to identify host genes exploited by MTB during infection, genes were non-selectively inactivated using lentivirus-based antisense RNA methods in RAW264.7 macrophages, and the cells that survived virulent MTB infection were then screened. Following DNA sequencing of the surviving cell clones, 26 host genes affecting susceptibility to MTB were identified and their pathways were analyzed by bioinformatics analysis. In total, 9 of these genes were confirmed as positive regulators of collagen α-5(IV) chain (Col4a5) expression, a gene encoding a type IV collagen subunit present on the cell surface. The knockdown of Col4a5 consistently suppressed intracellular mycobacterial viability, promoting the survival of RAW264.7 macrophages following mycobacterial infection. Furthermore, Col4a5 deficiency lowered the pH levels of intracellular vesicles, including endosomes, lysosomes and phagosomes in the RAW264.7 cells. Finally, the knockdown of Col4a5 post-translationally increased microsomal vacuolar-type H+-ATPase activity in macrophages, leading to the acidification of intracellular vesicles. Our findings reveal a novel role for Col4a5 in the regulation of macrophage responses to mycobacterial infection and identify Col4a5 as a potential target for the host-directed anti-mycobacterial therapy.
Intergiciels et Construction d'Applications Réparties

L'acces aux systemes d'information s'appuie aujourd'hui de plus en plus sur des technologies Internet. Les efforts de standardisation dans ce contexte ont accentue l'engouement des personnes et des organisations (aussi bien academiques, qu'industrielles, commerciales, ou institutionnelles) pour l'utilisation de l'Internet et ont permis l'emergence des services Web comme support de developpement des applications accessibles par Internet. Ainsi, les technologies associees aux services Web sont devenues incontournables pour le developpement d'applications interagissant les unes avec les autres par le biais de l'Internet. Nous proposons dans ce chapitre de faire un point sur le sujet des services Web. L'objectif de la discussion est de traiter des aspects conceptuels de la modelisation des services aussi bien que des aspects lies a leur implantation.
A Systematic Analysis of Measurement Selection Algorithms for Fault Isolation in Dynamic Systems

Fault detection and isolation in dynamic physical systems based on analysis of transient system behavior involves monitoring for deviations and linking observed deviations to changes in system parameters. Components linked to the changed parameter values may be faulty. Functional redundancy is exploited in choosing measurements to uniquely isolate all faults. Measurement selection algorithms determine the minimum set of system variables that can uniquely identify all single faults in the system. This paper analyzes a set of measurement selection algorithms in the context of transient analysis in dynamic systems. Results show that an A*-search algorithm provides good results while considerably reducing the computational complexity of the measurement selection process.
The Use of a priori Information in ICA-Based Techniques for Real-Time fMRI: An Evaluation of Static/Dynamic and Spatial/Temporal Characteristics

Real-time brain functional MRI (rt-fMRI) allows in vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitor a target independent component (IC). We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off-line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time, and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability.
Reserve Bank of St . Louis Working Paper Series The Local Effects of Monetary Policy

Previous studies have documented disparities in the regional responses to monetary policy shocks; this variation has been found to depend, in part, on differences in the industrial composition of the regional economies. However, because of computational issues, the literature has often neglected the richest level of disaggregation: the city. In this paper, we estimate the city-level responses to monetary policy shocks in a Bayesian VAR. The Bayesian VAR allows us to model the entire panel of metropolitan areas through the imposition of a shrinkage prior. We then seek the origin of the city-level asymmetric responses. [JEL codes: C32, E32, E52] ∗Heidi Beyer-Powe and Kristie M. Engemann provided research assistance. We thank Adam Zaretsky for providing data; Patrick Brandt, Mark Fisher, Daniel Waggoner, Tao Zha, seminar participants at the Federal Reserve Bank of St. Louis, the Macro-Money seminar at UNC-Chapel Hill, the Society of Nonlinear Dynamics and Econometrics 17th Annual Symposium, and the Western Economic Association International’s 84th Annual Conference for helpful comments and suggestions. The third author completed part of this work as a Dissertation Intern at the Research Division of the Federal Reserve Bank of St. Louis, whose hospitality is gratefully acknowledged. The views expressed herein do not reflect the official position of the Federal Reserve Bank of St. Louis or the Federal Reserve System.
Compression of Multispectral Images

Compression of multispectral images (obtained from sensors that sample in both the spatial and the spectral domains) is important for reducing the transmission and storage requirements of such data. This article describes versatile software developed to simulate a family of compression algorithms. Two algorithms are selected as the most suitable for implementation. The flrst is a moderately high complexity algorithm consisting of the Karhunen{Loµ eve transform (KLT) in the spectral dimension, the discrete cosine transform (DCT) for spatial decorrelation of the resulting bands, and the DCT on the residual. The other is a mediumcomplexity algorithm that uses predictive coding for spectral decorrelation and the DCT for spatial decorrelation. Performance results are given for these algorithms. A low-complexity algorithm is also discussed.
Comparison of superpixel algorithms on hyperspectral images

The hyperspectral images are conventionally classified using spectral information. Spectral information does not include the neighborhood relationship of the pixels. In this paper we classfy the hyperspectral images by using state-of-the-art superpixel methods and compare their performances. Since the superpixels take into account neighborhood relationship of the pixels, we benefit from the cooperation of spatial and spectral information. The superpixel-based classification method outperforms pixel-wise methods.
A Methodology to Handle Social Media Posts in Brazilian Portuguese for Text Mining Applications

Online Social Networks emerged at the beginning of 21st century and give us evidence that they are going to have a long life. Almost two-thirds of overall social media users affirm an everyday usage of a social media website and, therefore, the data volume across this platforms is huge. Natural language processing of social media texts is an attractive topic among researchers of this area. While there are many studies about natural language processing of social media texts for some languages (e.g., English), the researches for Brazilian Portuguese language are still limited. Then, in this paper, a methodology is proposed to deal with peculiarities of the Brazilian Portuguese language in informal, short and noisy texts, where the lack of context poses obstacles in text mining. The proposed methodology has been evaluated in two tasks (Text Categorization and Opinion Mining) and experiments showed that the preprocessing mechanisms included in this methodology were important to achieve better results.
Oncogene-targeted antisense oligonucleotides for the treatment of Ewing sarcoma

The genetic hallmark of the Ewing sarcoma family of tumours (ESFT) is the presence of the t(11;22)(q24;q12) translocation, present in up to 85% of cases of ESFT, which creates the EWS/FLI1 fusion gene and results in the expression of a chimeric protein regulating many other genes. The inhibition of this protein by antisense strategies has shown its predominant role in the transformed phenotype of Ewing cells. In addition, the junction point at the mRNA level offers a target for short therapeutic nucleic acids that is present only in the cancer cells and not in the normal tissues of a patient. Several teams have, therefore, investigated the activity of antisense oligonucleotides and siRNAs targeted against the junction point in mRNA; thus, inhibiting EWS/FLI1 synthesis. Generally speaking, the molecules induce a cell growth inhibition in culture. Apoptosis has also been reported. One laboratory has reported the in vivo tumour inhibitory effect of phosphorothioate antisense oligonucleotide directed against the EWS part of EWS/FlI1 when injected intratumourally. Independently, a tumour inhibitory effect of oligonucleotides targeting the junction point has been demonstrated provided they are delivered by polymeric nanoparticles through the intratumoural route. Alongside this target, other genes participating to the maintenance of the transformed phenotype of Ewing cells have been downregulated by antisense strategies.
Gene-Trap Mutagenesis Identifies Mammalian Genes Contributing to Intoxication by Clostridium perfringens ε-Toxin

The Clostridium perfringens ε-toxin is an extremely potent toxin associated with lethal toxemias in domesticated ruminants and may be toxic to humans. Intoxication results in fluid accumulation in various tissues, most notably in the brain and kidneys. Previous studies suggest that the toxin is a pore-forming toxin, leading to dysregulated ion homeostasis and ultimately cell death. However, mammalian host factors that likely contribute to ε-toxin-induced cytotoxicity are poorly understood. A library of insertional mutant Madin Darby canine kidney (MDCK) cells, which are highly susceptible to the lethal affects of ε-toxin, was used to select clones of cells resistant to ε-toxin-induced cytotoxicity. The genes mutated in 9 surviving resistant cell clones were identified. We focused additional experiments on one of the identified genes as a means of validating the experimental approach. Gene expression microarray analysis revealed that one of the identified genes, hepatitis A virus cellular receptor 1 (HAVCR1, KIM-1, TIM1), is more abundantly expressed in human kidney cell lines than it is expressed in human cells known to be resistant to ε-toxin. One human kidney cell line, ACHN, was found to be sensitive to the toxin and expresses a larger isoform of the HAVCR1 protein than the HAVCR1 protein expressed by other, toxin-resistant human kidney cell lines. RNA interference studies in MDCK and in ACHN cells confirmed that HAVCR1 contributes to ε-toxin-induced cytotoxicity. Additionally, ε-toxin was shown to bind to HAVCR1 in vitro. The results of this study indicate that HAVCR1 and the other genes identified through the use of gene-trap mutagenesis and RNA interference strategies represent important targets for investigation of the process by which ε-toxin induces cell death and new targets for potential therapeutic intervention.
European Conference on Information Systems ( ECIS ) Summer 10-6-2011 TRANSLATING ES-EMBEDDED INSTITUTIONAL LOGICS THROUGH TECHNOLOGICAL FRAMING : AN INDIAN-BASED CASE EXAMPLE

In this paper we explore how the implementation of an Enterprise System (ES) is related to organizational change, using an institutional theory lens. Our paper responds to recent calls by institutional theorists to first, better understand the ways in which macro, field-level logics of action are framed and applied in micro practices within an organization and second, to understand how material objects contribute to institutional stability and change. Our findings show the interplay between macro logics and the process of local framing through which these logics become locally interpreted, leading eventually to new institutionalized practices. Our study suggests the possibility of co-mingling contrasting and competing logics in the local context. We do this through the interpretive exploration of a rich case study of an ES implementation in India. This is an ideal case to examine because the institutional logic inscribed in the ES is developed within one organizational field, but is applied in a very different organizational field, thus allowing us to explore the macro-micro dynamics as well as the role of technology as a carrier and stabilizer of institutional structures and practices.
Single kernel stack L4

Operating systems use kernel stacks to support the execution of threads. A typical multi threaded operating system uses one kernel stack per thread. This stack per thread model consumes a significant amount of kernel memory. An alternative model is the use of a single kernel stack that is shared between all threads. This reduces the opersating systems memory consumption. This thesis implements the single stack kernel architecture in the L4 microkernel to evaluate the performance and memory tradeoffs. It is shown that significant memory savings can be achieved without degrading the kernels performance. Preliminary results show improvement in the kernels performance due to the single stack architecure, however more experiments are required to verify this result.
Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing

The optimal mixing evolutionary algorithms (OMEAs) have recently drawn much attention for their robustness, small size of required population, and efficiency in terms of number of function evaluations (NFE). In this paper, the performances and behaviors of convergence in OMEAs are studied by investigating the mechanism of optimal mixing (OM), the variation operator in OMEAs, under two scenarios---one-layer and two-layer masks. For the case of one-layer masks, the required population size is derived from the viewpoint of initial supply, while the convergence time is derived by analyzing the progress of sub-solution growth. NFE is then asymptotically bounded with rational probability by estimating the probability of performing evaluations. For the case of two-layer masks, empirical results indicate that the required population size is proportional to both the degree of cross competition and the results from the one-layer-mask case. The derived models also indicate that population sizing is decided by initial supply when disjoint masks are adopted, that the high selection pressure imposed by OM makes the composition of sub-problems impact little on NFE, and that the population size requirement for two-layer masks increases with the reverse-growth probability.
Boolean Modeling Reveals the Necessity of Transcriptional Regulation for Bistability in PC12 Cell Differentiation

The nerve growth factor NGF has been shown to cause cell fate decisions toward either differentiation or proliferation depending on the relative activity of downstream pERK, pAKT, or pJNK signaling. However, how these protein signals are translated into and fed back from transcriptional activity to complete cellular differentiation over a time span of hours to days is still an open question. Comparing the time-resolved transcriptome response of NGF- or EGF-stimulated PC12 cells over 24 h in combination with protein and phenotype data we inferred a dynamic Boolean model capturing the temporal sequence of protein signaling, transcriptional response and subsequent autocrine feedback. Network topology was optimized by fitting the model to time-resolved transcriptome data under MEK, PI3K, or JNK inhibition. The integrated model confirmed the parallel use of MAPK/ERK, PI3K/AKT, and JNK/JUN for PC12 cell differentiation. Redundancy of cell signaling is demonstrated from the inhibition of the different MAPK pathways. As suggested in silico and confirmed in vitro, differentiation was substantially suppressed under JNK inhibition, yet delayed only under MEK/ERK inhibition. Most importantly, we found that positive transcriptional feedback induces bistability in the cell fate switch. De novo gene expression was necessary to activate autocrine feedback that caused Urokinase-Type Plasminogen Activator (uPA) Receptor signaling to perpetuate the MAPK activity, finally resulting in the expression of late, differentiation related genes. Thus, the cellular decision toward differentiation depends on the establishment of a transcriptome-induced positive feedback between protein signaling and gene expression thereby constituting a robust control between proliferation and differentiation.
Lack of correlation between expression of Epstein-Barr virus (EBV) latent membrane protein and bcl-2 oncoprotein in vivo.

AIMS--To evaluate whether there is any correlation between the expression of Epstein-Barr virus (EBV) latent membrane protein (LMP) and oncoprotein bcl-2 in the lymph node biopsy specimens of a Chinese patient with EBV-related reactive lymphoproliferation who later developed T cell lymphoma after a short period of time. METHODS--Immunohistochemistry, with a standard alkaline phosphatase antialkaline phosphatase (APAAP) method and New Fuchsin as a chromogen, was used for single staining of bcl-2 or LMP. Double immunostaining combining APAAP and indirect immunofluorescence was performed for dual labelling of LMP and bcl-2. RESULTS--bcl-2 was expressed in 10-30% of cells in the first lymph node biopsy specimen (EBV-associated lymphoproliferative disorder) and 30-50% of cells in the second lymph node biopsy specimen (T cell lymphoma). LMP was expressed in the first biopsy specimen but not in the second. Double immunostaining results showed that around 78% of LMP positive cells were bcl-2 negative and 94% bcl-2 positive cells were LMP negative. Among the very small fraction of LMP and bcl-2 double positive cells, the intensity of bcl-2 staining was heterogeneous and was not always stronger than that observed in LMP negative bcl-2 positive cells. CONCLUSIONS--The expression of bcl-2 protein is independent of LMP protein status in vivo. Several mechanisms may be involved in EBV associated lymphomagenesis, and bcl-2 induction may occur independently of LMP expression.