Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relative Value Theory

I propose the following theory: rational investors allocate capital to maximize the probability-weighted geometric mean of payoffs. I present the theory’s rationale and investigate its implications. I find that diversification increases cumulative returns and asset values are relative. The theory does not make assumptions about investors’ risk preferences and explains the “risk premium” without using the utility function concept. In a world of rational, risk indifferent investors the market tends to a stable equilibrium characterized by all investors holding the market. I calculate equilibrium prices for a simple model. The theory uses a unique discount rate (to account for time value of money) and ignores the required rate of return concept. The theory addresses some of the CAPM’s inconsistencies. I conclude the theory is coherent and useful in explaining observable finance phenomena.
Business intelligence model for unstructured data management

Business Intelligence plays an important role in the organization for collecting, integrating, analyzing and transforming data to be useful for effective decision making process. Nowadays, organizations are flooded with various kinds of unstructured data such as e-mail, images, reports, maps, charts, publications. An effective and efficient business model of these data could help in decision making. Currently, there is no study done on the business intelligence model for managing unstructured data that can fulfil the organization needs. Therefore, the purpose of this paper is to improve the organization's business intelligence process through the exploitation of unstructured data that is owned by the organization. In this study, unstructured data are classified, enriched and complemented with diversity of data through the process of creating metadata for each unstructured data. Four main processes are proposed to transform unstructured data to structured data which are extraction, classification, storage and mapping of data classes. Each process and its activities are combined to produce an effective and efficient business intelligence model for unstructured data management. This model helps in generating new data and information that is more comprehensive and collective to help business intelligence through advanced analysis, decision-making process and planning new research areas. Output from this study is to make unstructured data as renewable assets that is easily accessible and used as a reference and foundation in business intelligence and decision making process.
Learning about Academic Ability and the College Dropout Decision

Research examining the educational attainment of low-income students has often focused on financial factors such as credit constraints. We use unique longitudinal data to provide direct evidence about a prominent alternative explanation—that departures from school arise as students learn about their academic ability or grade performance. Examining college dropout, we find that this explanation plays a very prominent role; our simulations indicate that dropout between the first and second years would be reduced by 40% if no learning occurred about grade performance/academic ability. The article also contributes directly to the understanding of gender differences in educational attainment.
Wavelet-based denoising of partial discharge signals buried in excessive noise and interference

Achieving acceptable levels of sensitivity during online and/or onsite partial discharge (PD) measurements still continues to remain a very challenging task, primarily due to strong coupling of external (random, discrete spectral and stochastic pulsive) interferences. Many analog and digital approaches have been proposed for suppressing these interferences, and amongst these, rejection of the pulsive type of interferences is known to be very difficult, if not impossible. The time and frequency characteristics of the pulsive interference being very similar to that of the PD pulses is the main reason posing difficulty in their separation. In this paper, a novel, semi-automatic, and empirical wavelet-based method (using multi-resolution signal analysis) is proposed to recover PD pulses, buried in excessive noise/interference comprising of random, discrete spectral, pulsive, and any combination of these interferences occurring simultaneously and overlapping-in-time with the PD pulses. A critical assessment of the proposed method is carried out, by processing both simulated and practically acquired PD signals. The results obtained are also compared with those from the best digital filter (infinite impulse response, IIR and finite impulse response, FIR) method proposed in literature. From the results it emerges that, the wavelet approach is superior and further, has the unique capability of successfully rejecting all the three kinds of interferences, even when PD signals and one or all interferences occur simultaneously and overlap-in-time.
Heckscher-Ohlin and the global increase of skill premia : factor intensity reversals to the rescue

This paper advances the claim that trade liberalization has been a strong force behind the global increase of skill premia, in particular in skill-scarce, developing countries. By introducing skill-intensity reversals, the Heckscher-Ohlin framework captures the stylized facts of the global increase in skill premia, both in developed and in less-developed countries. I support the model by evidence on industrial structure and changes in relative prices. The calibrated model is also successful quantitatively: small changes in relative goodsprices are consistent with much larger increases in skill premia that have been observed in the data. This suggests that tari¤ reductions might have been a strong driving force behind the increase in global inequality and weakens the conclusion that other forces have been dominant. The analysis also suggests an explanation for protection of skill-unintensive sectors both in developedand less-developed countries. Keywords: Heckscher-Ohlin, Factor intensity reversals, Skill premium, Elasticity of substitution, Trade liberalization. JEL Classi cation: F11, F16 I thank Jonathan Eaton and William Easterly for invaluable guidance and discussion. I also thank Jess Benhabib, Boyan Jovanovic, Andrea Ferrero, Ana-Cecilia Lima-Fieler Yaw Nyarko, and Gianluca Violante for useful comments and discussions. Useful comments from participants of the 3rd year seminar at NYU, the macro student seminar at NYU, the Bocconi-NYU workshop in Florence and LACEA 2005 meetings, are also gratefully acknowledged. Corresponding information: ariellr@nyu.edu, +1212-961-6114.
Tobacco carcinogen-detoxifying enzyme UGT1A7 and its association with orolaryngeal cancer risk.

BACKGROUND UDP-glucuronosyltransferase 1A7 (UGT1A7) detoxifies several tobacco carcinogens. We determined whether UGT1A7 expression is observed in normal orolaryngeal tissue and whether UGT1A7 allelic variations are associated with the risk for orolaryngeal cancer. METHODS UGT1A7 expression in normal orolaryngeal tissue was determined by semiquantitative reverse transcription-polymerase chain reaction (PCR). Buccal cell DNA isolated from 194 case subjects with orolaryngeal cancer and from 388 control subjects who were matched by sex, age, and race was subjected to UGT1A7 genotyping with the use of combined PCR-restriction fragment length polymorphism and allelic discrimination analysis. All statistical tests were two-sided. RESULTS UGT1A7 messenger RNA was expressed at similar levels in the esophagus, tongue, tonsil, floor of the mouth, and larynx. Genotyping revealed the presence of three variant reduced-activity UGT1A7 alleles in both Caucasians and African-Americans. Individuals with any of the predicted low-activity UGT1A7 genotypes had an increased risk of orolaryngeal cancer (odds ratio [OR] = 3.7; 95% confidence interval [CI] = 1.7 to 8.7) relative to subjects with the wild-type genotype. Both Caucasians and African-Americans with the low-activity genotypes had statistically significantly increased orolaryngeal cancer risk compared with Caucasians and African-Americans with the wild-type genotype (OR = 2.8 [95% CI = 1.1 to 7.6] and OR = 6.2 [95% CI = 1.2 to 31], respectively). For subjects with the predicted low-activity genotypes, the risks of oral cavity cancer (OR = 4.2; 95% CI = 1.7 to 10) and laryngeal cancer (OR = 3.7; 95% CI = 0.99 to 14) were similar. There was no association between UGT1A7 genotype and orolaryngeal cancer risk in never smokers, whereas subjects with predicted low-activity UGT1A7 genotypes who were light smokers (OR = 3.7; 95% CI = 1.1 to 12) or heavy smokers (OR = 6.1; 95% CI = 1.5 to 25) had an increased risk. CONCLUSIONS The tissue expression of UGT1A7 is consistent with the possibility of a physiologic role in orolaryngeal cancer. Variations in the UGT1A7 gene that reduce UGT1A7 activity may affect the risk of smoking-related orolaryngeal cancer.
Herbivore‐induced plant volatiles induce an indirect defence in neighbouring plants

1 Many plant species respond to herbivory with increased emission of volatile organic compounds (VOCs): these attract carnivorous arthropods and thereby function as an indirect defence mechanism. Whether neighbouring plants can ‘eavesdrop’ on such airborne cues and tailor their defences accordingly, remains controversial. 2 We used Lima bean plants (Phaseolus lunatus) to investigate whether herbivore‐induced VOCs induce another indirect defence strategy, i.e. the secretion of extrafloral nectar (EFN) in conspecific plant neighbours, and whether this enhances the defence status of the receiving plant under natural conditions. 3 EFN secretion was induced by VOCs released from herbivore‐damaged bean tendrils as well as by a synthetic VOC mixture resembling the natural one. One constituent of the herbivore‐induced blend – the green leaf volatile (3Z)‐hex‐3‐enyl acetate – was sufficient to elicit the defence reaction. 4 A long‐term experiment comparing the defensive effect of EFN alone with the VOC‐mediated effect (EFN induction plus attraction of plant defenders) suggested that Lima bean benefits from both indirect defences. Repeated treatment of tendrils with either an artificial blend of VOCs or with EFN led to the attraction of a higher cumulative number of predatory and parasitoid insects (i.e. ants and wasps) as well as to less herbivore damage and an increased production of inflorescences and leaves. 5 Our results demonstrate that one indirect defence mechanism can induce another one in conspecific plants, and that Lima bean plants can benefit from this VOC‐induced EFN secretion under natural conditions. Both extrafloral nectaries and the capability to release VOCs upon herbivory are present in many plant taxa and airborne signalling may thus represent a common mechanism for regulating the secretion of EFN in plant parts which face an increased risk of herbivory.
Luminescent Conjugated Oligothiophenes for Sensitive Fluorescent Assignment of Protein Inclusion Bodies

Small hydrophobic ligands identifying intracellular protein deposits are of great interest, as protein inclusion bodies are the pathological hallmark of several degenerative diseases. Here we report that fluorescent amyloid ligands, termed luminescent conjugated oligothiophenes (LCOs), rapidly and with high sensitivity detect protein inclusion bodies in skeletal muscle tissue from patients with sporadic inclusion body myositis (s‐IBM). LCOs having a conjugated backbone of at least five thiophene units emitted strong fluorescence upon binding, and showed co‐localization with proteins reported to accumulate in s‐IBM protein inclusion bodies. Compared with conventional amyloid ligands, LCOs identified a larger fraction of immunopositive inclusion bodies. When the conjugated thiophene backbone was extended with terminal carboxyl groups, the LCO revealed striking spectral differences between distinct protein inclusion bodies. We conclude that 1) LCOs are sensitive, rapid and powerful tools for identifying protein inclusion bodies and 2) LCOs identify a wider range of protein inclusion bodies than conventional amyloid ligands.
Multiobjective-based concepts to handle constraints in evolutionary algorithms

This paper presents the main multiobjective optimization concepts that have been used in evolutionary algorithms to handle constraints in global optimization problems. A review of some approaches developed under these concepts is provided. Additionally, a comparison of four representative techniques using well-known test functions is shown. Finally, the analysis of the results obtained, based on three main points (quality, consistency and diversity) and some conclusions and future trends are also provided.
Supplementary Information Development of a Mono-promoter-driven Crispr/cas9 System in Mammalian Cells

Supplementary Figure 2: Comparison of gRNA expression level among the U6-gRNA, pCAG-RGR and mono-promoter types vectors. Supplementary Figure 3: T7EI assay of potential off-target loci for HPRT1. Supplementary Figure 4: The eGFP expression of pCAG-RGR-eGFP and pCAG-RGR-IRES-eGFP. (Page 7) Supplementary Figure 5: Comparison of Cas9 expression level between the Cas9 individually-driven CRISPR/Cas9 system and mono-promoter-driven CRISPR/Cas9 system. (Page 8) Supplementary Figure 6: Ubiquitous expression of Cas9 induce the off-doxycycline gene disruption. Supplementary Table 1: Target sequence of human HPRT1 and FAN1 locus and primer sequence for RGR.
Complete convergence for negatively dependent random variables

In this paper, we study the complete convergence for the means 1n∑i=1nXi and 1nα∑k=1nXnk via. exponential bounds, where α>0 and {Xn,n≥1} is a sequence of negatively dependent random variables and {Xnk,1≤k≤n,n≥1} is an array of rowwise pairwise negatively dependent random variables.
A novel WDM-based optical access network with high energy efficiency using Elastic OLT

This paper presents a novel access network architecture that includes the concept of “Elastic OLT” with a view to saving power. This architecture saves power by adapting to the physical rate of the signal. The adaptation policy is based on the full utilization of the power budget, which increases as the physical rate decreases. Making full use of the power budget is based on the idea that surplus power budget constitutes a waste of power. The surplus power budget is exhausted by extending the transmission distance and increasing the splitting ratio, which are both realized by Elastic OLT. Calculation and design for determining the optimal network parameters are performed. The results confirm the validity of the proposed method.
High-performance general solver for extremely large-scale semidefinite programming problems

Semidefinite programming (SDP) is one of the most important problems among optimization problems at present. It is relevant to a wide range of fields such as combinatorial optimization, structural optimization, control theory, economics, quantum chemistry, sensor network location and data mining. The capability to solve extremely large-scale SDP problems will have a significant effect on the current and future applications of SDP. In 1995, Fujisawa et al. started the SDPA(Semidefinite programming algorithm) Project aimed at solving large-scale SDP problems with high numerical stability and accuracy. SDPA is one of the main codes to solve general SDPs. SDPARA is a parallel version of SDPA on multiple processors with distributed memory, and it replaces two major bottleneck parts (the generation of the Schur complement matrix and its Cholesky factorization) of SDPA by their parallel implementation. In particular, it has been successfully applied to combinatorial optimization and truss topology optimization. The new version of SDPARA (7.5.0-G) on a large-scale supercomputer called TSUBAME 2.0 at the Tokyo Institute of Technology has successfully been used to solve the largest SDP problem (which has over 1.48 million constraints), and created a new world record. Our implementation has also achieved 533 TFlops in double precision for large-scale Cholesky factorization using 2,720 CPUs and 4,080 GPUs.
A Linear-Time Graph Kernel

The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays and characterize each node using logical operations on the label set of the connected nodes. Our kernel has a linear time complexity with respect to the number of nodes times the average number of neighboring nodes in the given graphs. The experimental result shows that the proposed kernel performs comparable and much faster than a state-of-the-art graph kernel for benchmark data sets and shows high scalability for new applications with large graphs.
S100 proteins expressed in phagocytes: a novel group of damage‐associated molecular pattern molecules

Damage‐associated molecular pattern (DAMP) molecules have been introduced as important proinflammatory factors of innate immunity. One example known for many years to be expressed in cells of myeloid origin are phagocytic S100 proteins, which mediate inflammatory responses and recruit inflammatory cells to sites of tissue damage. An emerging concept of pattern recognition involves the multiligand receptor foradvanced glycation end products (RAGE) and Toll‐like receptors (TLRs) in sensing not only pathogen‐associated molecular patterns (PAMPs) but also endogenous DAMPs, including S100 proteins. S100A8, S100A9, and S100A12 are found at high concentrations in inflamed tissue, where neutrophils and monocytes belong to the most abundant cell types. They exhibit proinflammatory effects in vitro at concentrations found at sites of inflammation in vivo. Although S100A12 binds to RAGE, at least part of the proinflammatory effects of the S100A8/S100A9 complex depend upon interaction with other receptors. Because of the divergent expression patterns, the absence of S100A12 in rodents, the different interaction partners described, and the specific intracellular and extracellular effects reported for these proteins, it is important to differentiate between distinct S100 proteins rather than subsuming them with the term “S100/calgranulins. ” Analyzing the molecular basis of the specific effects exhibited by these proteins in greater detail bears the potential to elucidate important mechanisms of innate immunity, to establish valid biomarkers of phagocytic inflammation, and eventually to reveal novel targets for innovative anti‐inflammatory therapies.
Motion Planning of Autonomous Vehicles on a Dual Carriageway without Speed Lanes

The problem of motion planning of an autonomous vehicle amidst other vehicles on a straight road is considered. Traffic in a number of countries is unorganized, where the vehicles do not move within predefined speed lanes. In this paper, we formulate a mechanism wherein an autonomous vehicle may travel on the “wrong” side in order to overtake a vehicle. Challenges include assessing a possible overtaking opportunity, cooperating with other vehicles, partial driving on the “wrong” side of the road and safely going to and returning from the “wrong” side. The experimental results presented show vehicles cooperating to accomplish overtaking manoeuvres.
Relación entre gobierno corporativo y desempeño financiero: evidencia para las empresas colombianas durante el periodo de 2006 a 2013

Los atributos de gobierno corporativo de una empresa son de gran importancia para sus inversionistas, debido a la relación que estos guardan con el desempeño financiero. Este trabajo busca soportar la evidencia internacional sobre tal relación y justifica la implementación de buenas prácticas de gobierno corporativo en las empresas colombianas que cotizan en el mercado de valores. Para ello, se implementa conjuntamente un análisis no lineal de componentes principales y el método generalizado de momentos. En este ejercicio, se extraen las complejas dimensiones de gobierno corporativo a través de sus relaciones locales y, luego, se evalúa su relación con los indicadores de desempeño financiero: ROA y Q de Tobin.
Evolutionary Tabu Search for Geometric Primitive Extraction

Many problems in computer vision can be formulated as an optimization problem. De-velopping the eecient global optimizational technique adaptive to the vision proplem becomes more and more important. In this paper, we present a geometric primitive extraction method, which plays a crucial role in content-based image retrieval and other vision problems. We formulate the problem as a cost function minimization problem and we present a new optimization technique called Evolutionary Tabu Search (ETS). Genetic algorithm and Tabu Search Algorithm are combined in our method. Speciicly, we incorporates "the survival of strongest" idea of evolution algorithm into tabu search. In experiments, we use our method for shape extraction in images and compare our method with other three global optimization methods including genetic algorithm, simulated An-nealing and tabu search. The results show that the new algorithm is a practical and eeective global optimization method, which can yield good near-optimal soultions and has better convergence speed.
The Predictive Validity of Subjective Probabilities of Survival

Although expectations (or subjective probability distributions) play a prominent role in models of decision-making under uncertainty, we have very little data on them and are instead forced to base our models on unverifiable assumptions. Macroeconomic models often assume rational expectations, and microeconomic models base estimation on observable population probabilities. An alternative to these assumptions is to query individuals directly about their subjective probabilities, and to use the responses as measures of expectations. Prior research on subjective survival probabilities in the Health and Retirement Study has shown that reported probabilities aggregate closely to life table values and covary appropriately with known risk factors. This paper uses panel data to study the evolution of subjective survival probabilities and their ability to predict actual mortality. We find that respondents modify appropriately their survival probabilities based on new information. The onset of a new disease condition or the death of a parent between the waves is associated with a reduction in survival probabilities. The subjective survival probabilities also predict actual survival. Those who survived in our panel reported probabilities approximately 50 percent greater at baseline than those who died. Although more needs to be learned about properties of subjective probabilities we conclude that they show considerable promise for estimating models of decision-making under uncertainty.