Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Calculate the Stockholders' Cost of Stock Option Incentive

In this paper we discuss the risk discount and cost of stock option contracts based on theory analysis and arithmetic operation. First the calculation of data shows that expectation value cost increases with the rise of exercise price, with the same result as the previous prediction. Meanwhile, B-S cost can become larger or smaller with higher exercise price, which suggests that the expectation value cost is more reasonable. Then we explore the relationship between the expectation value cost and the B-S cost in stock option contracts based on theory analysis and discover the rational explanations for the above conclusion.
Automatically Mining Specifications from Invocation Traces and Method Invariants

Software library documentation often describes the APIs of individual methods, but does not capture the intended protocols and method interactions. This can cause developers to misuse the library by invoking its methods out of sequence, while also restricting effective runtime detection of protocol violations and automated verification of the client code. Specification mining, if accurate, can help mitigate these problems. There are two classes of specification mining techniques: one that observes library executions, and another that identifies internal-state invariants. Unfortunately, models inferred by these state-of-the-art techniques often suffer from inaccuracies. In this paper, we aim to improve the quality of mined specifications in order to facilitate API understanding, test generation, and verification tasks. To that end, we combine the two classes of techniques in two novel ways, producing a pair of new model-inference techniques. We find that our new techniques improve significantly upon the state-of-the-art when applied to nine off-the-shelf libraries, increasing the precision of the inferred models by 4% and the recall by 41% on average. Additionally, we find that our approach automatically exposes both behavior that avoids raising exceptions and good-practice rules that capture proper library use, while being robust to noisy inputs.
Negativity Effect and the Emergence of Ideologies

`Negativity effect' refers to the psychological phenomenon that people tend to attach greater weight to negative information than to equally extreme and equally likely positive information in a variety of information-processing tasks. Numerous studies of impression formation have found that negative information is weighted more heavily than positive information as impressions of others are formed. There is empirical evidence in political science that shows the importance of the negativity effect in the information processing of the voters. This effect can explain the observed decrease in popularity of a president the longer he is in office. We construct a dynamic model of political competition, incorporating the negativity effect in the decision rule of the voters and allowing their preferences to change over time, according to the past performance of the candidates while in office. Our model may explain the emergence of ideologies out of the competition for votes of myopic candidates freely choosing policy positions. This result gives rise to the formation of political parties, as infinitely-lived agents with a certain ideology. Furthermore, in this model some voters may start out by switching among parties associated with different policies, but find themselves supporting one of the parties from some point on. Thus, the model describes a process by which some voters become identified with a `right' or `left' bloc, while others `swing' between the two parties.
Estimating The Top k Nodes Of A Network

The goal of this paper is to estimate the topk central nodes in a network through parsimonious sampling, i n an online fashion. We consider three centrality metrics: degree, betweenness, and closeness centrality. We identify and inv estigate through simulations the contributions of two sources of error in finding central nodes: (1) sampling (collection) error and (2) identification error. Sampling error occurs when a node i n the top k most central nodes is not sampled by the sampling algorithm. Identification error occurs when a sampled topk node is not identified as such. We observe that among the analyzed traces, random walks yield low sampling error. Using degree centrality as an alias for identifying nodes with high betweenness and closeness centrality, we then show that degree informat ion collected while sampling the network can be used to reduce identification errors. As a consequence of these two observa tions, a random walk using degree to identify the topk betweenness and closeness central nodes performs better, in the analyzed tr aces, for small values of sampled fraction of nodes (1-5%), than mo re complex strategies recently proposed in the literature.
Extending Graph-Cut to Continuous Value Domain Minimization

In this paper we propose two methods for minimizing objective functions of discrete functions with continuous value domain. Many practical problems in the area of computer vision are continuous-valued, and discrete optimization methods of graph-cut type cannot be applied directly. This is different with the proposed methods. The first method is an add-on for multiple-label graph-cut. In the second one, binary graph-cut is firstly used to generate regions of support within different ranges of the signal. Secondly, a robust error minimization is approximated based on the previously determined regions. The advantages and properties of the new approaches are explained and visualized using synthetic test data. The methods are compared to ordinary multi-label graph-cut and robust smoothing for the application of disparity estimation. They show better quality of results compared to the other approaches and the second algorithm is significantly faster than multi-label graph-cut.
Evaluating end-effector modalities for warehouse picking: A vacuum gripper vs a 3-finger underactuated hand

This paper studies two end-effector modalities for warehouse picking: (i) a recently developed, underactuated three-finger hand and (ii) a custom built, vacuum-based gripper. The two systems differ on how they pick objects. The first tool provides increased flexibility, while the vacuum alternative is simpler and smaller. The aim is to show how the end-effector influences the success rate and speed of robotic picking. For the study, the same planning process is followed for known poses of multiple objects with different geometries and characteristics. The resulting trajectories are executed on a real system showing that, under different conditions, different types of end-effectors can be beneficial. This motivates the development of hybrid solutions.
Reliable Multiple Object Tracking under Heavy Occlusions

Tracking multiple objects in surveillance scenarios involves considerable difficulty because of occlusions. We report a novel tracker -- based on reliability tracking -- that demonstrates superior performance under high degrees of occlusion. In our method, distinguishable features between the target and non-target are represented as the object’s reliability. When the selected features are no longer reliable for sake of occlusions, the proposed method should select a new feature with more reliability by its corresponding region’s status. We present results from PETS 2006 dataset with many objects in the scene at any instant. Experimental results show that our method is robust when tracking objects during partial and serious occlusions. The object’s discriminability in appearance model is well maintained when interaction among other objects occurs.
Plasmodium falciparum parasites deploy RhopH2 into the host erythrocyte to obtain nutrients, grow and replicate

Plasmodium falciparum parasites, the causative agents of malaria, modify their host erythrocyte to render them permeable to supplementary nutrient uptake from the plasma and for removal of toxic waste. Here we investigate the contribution of the rhoptry protein RhopH2, in the formation of new permeability pathways (NPPs) in Plasmodium-infected erythrocytes. We show RhopH2 interacts with RhopH1, RhopH3, the erythrocyte cytoskeleton and exported proteins involved in host cell remodeling. Knockdown of RhopH2 expression in cycle one leads to a depletion of essential vitamins and cofactors and decreased de novo synthesis of pyrimidines in cycle two. There is also a significant impact on parasite growth, replication and transition into cycle three. The uptake of solutes that use NPPs to enter erythrocytes is also reduced upon RhopH2 knockdown. These findings provide direct genetic support for the contribution of the RhopH complex in NPP activity and highlight the importance of NPPs to parasite survival. DOI: http://dx.doi. org/10.7554/eLife.23217. 001
Expression of ErbB4 in the neurons of Alzheimer's disease brain and APP/PS1 mice, a model of Alzheimer's disease

Neuregulin-1 (NRG1) plays important roles in the development and plasticity of the brain, and has also been reported to exhibit potent neuroprotective properties. Although ErbB4, a key NRG1 receptor, is expressed in multiple regions in the adult animal brain, little is known about its role in Alzheimer's disease (AD). AD is characterized by progressive impairment of cognition and behavioral disturbance that strongly correlate with degeneration and death of neurons in the cerebral cortex and limbic brain areas, such as the hippocampus and the amygdala. Here, we show that the ErbB4 and phospho-ErbB4 immunoreactivities were higher intensity in the neurons of the CA1-2 transitional field of AD brains as compared to age-matched controls. Also, ErbB4 expression was increased in the neurons of the cortico medial nucleus amygdala, human basal forebrain and superior frontal gyrus of AD brains. In cerebral cortex and hippocampus of amyloid precursor protein/presenilin 1 double transgenic mice, ErbB4 immunoreactivity significantly increased in comparison to age-matched wild type control. These results suggest that up-regulating of ErbB4 immunoreactivity may involve in the progression of pathology of AD.
Forming the COUNCIL Based Clusters in Securing Wireless Ad Hoc Networks

In cluster-based routing protocol (CBRP), two-level hierarchical structure is successfully used to reduce over-flooding in wireless Ad Hoc networks. As it is vulnerable to a single point of failure, we propose a new adaptive distributed threshold scheme to replace the cluster head by a group of cluster heads within each cluster, called COUNCIL, and distribute the service of single cluster head to multiple cluster heads using (k,n) threshold secret sharing scheme. An Ad Hoc network formed by COUNCIL based clusters can work correctly when the number of compromised cluster heads is smaller than k. To implement this adaptive threshold scheme in wireless Ad Hoc Networks, membership of the clusters should be defined in an adaptive way. In this paper, we mainly discuss our algorithm for forming COUNCIL based clusters using the concept of dominating set from graph theory.
Structural and functional characterization of CMP-N-acetylneuraminate synthetase from Vibrio cholerae

CMP-N-acetylneuraminate synthetase (CMAS) is a key enzyme in the sialic acid incorporation pathway and plays a crucial role in the virulence and survival of several pathogenic bacteria. Here, the structural and functional properties of CMAS from the pathogenic bacterium Vibrio cholerae are reported. Upon CDP binding, a partial domain closure is observed that was previously unreported in homologous structures. Kinetic studies reveal that the enzyme shows substrate promiscuity and can activate both Neu5Ac and Neu5Gc.
A "Pivot" XML-Based Architecture for Multilingual, Multiversion Documents: Parallel Monolingual Documents Aligned Through a Central Correspondence Descriptor and Possible Use of UNL

We propose a structure for multilingual, multiversion documents, built on the model of the web-oriented, cooperative lexical multilingual data base PAPILLON: a document is represented by a collection of monolingual XML "volumes" interlinked by a central volume of "interlingual links". Here, the links relate subdocuments (XML trees) corresponding to each other in monolingual "volumes". We are developing a Java application to enable direct editing of a multilingual document through the web, at the level of monolingual volumes as well as through bilingual or trilingual interfaces inspired by those of commercial "translation workbenches". Another goal is easy integration with machine translation and multilingual generation tools. For this, we add a spe- cial UNL volume. In a first stage, we split the UNL-xml document in several monolingual documents, again represented by XML files. Each document con- tains the text in a particular language, plus the corresponding UNL graphs, and can be modified independently. The interface is easy to build, but realigning the documents after a series of such modifications is a very difficult task.
Improving surf interest point detection for defocus blur robustness

In this article, we propose a modification to SURF (Speeded Up Robust Features) to make the feature detection invariant to defocus blur. Specifically, SURF's blob detection relies on the determinant of Hessian matrix constructed out of differential responses to the image. Our analysis of blur and its effect on SURF suggests that fourth derivative - and not the usual second derivative - is optimal for detecting the blurred blobs. The proposed defocus blur invariant SURF - which we refer to as DBI-SURF - does not require image deblurring nor blur kernel estimation, meaning that its accuracy does not depend on the quality of image deblurring.
Visual and auditory icons for intelligent building

The current study propose the design and evaluation of visual and auditory icons for a smart house. Scenario based design and hierarchical task analysis were used to derive function names for smart buildings. Participants (in group) are required to design icons in 7 formats (image related, concept related, semiabstract, arbitrary, word, abbreviation, and combined) for useful function names. In total, 315 icons of 45 functions are developed in this study. Ranking test and paired comparison test would be conducted respectively for the evaluation of visual and auditory icons. The results can be applied to the design and evaluation of visual and auditory icons in other contexts.
Lactobacillus paraplantarum 11-1 Isolated from Rice Bran Pickles Activated Innate Immunity and Improved Survival in a Silkworm Bacterial Infection Model

Lactic acid bacteria (LAB) have high immune system-stimulating activity and are considered beneficial for human health as probiotics in the gut. The innate immune system is highly conserved between mammals and insects. Microbe-associated molecular patterns (e.g., peptidoglycan and β-glucan) induce cytokine maturation, which, in silkworm larvae, leads to muscle contraction. The purpose of this study is to find a novel probiotic by using silkworm muscle contraction assay. In the present study, we isolated LAB derived from rice bran pickles. We selected highly active LAB to activate the innate immune system of the silkworm, which was assayed based on silkworm muscle contraction. Of various LAB, L. paraplantarum 11-1 strongly stimulated innate immunity in the silkworm, leading to stronger silkworm contraction than a dairy-based LAB. Silkworms fed a diet containing L. paraplantarum 11-1 exhibited tolerance against the pathogenicity of Pseudomonas aeruginosa. These findings suggest that L. paraplantarum 11-1 could be a useful probiotic for activating innate immunity.
Optical Variability in Active Galactic Nuclei: Starbursts or Disk Instabilities?

Aperiodic optical variability is a common property of active galactic nuclei (AGNs), though its physical origin is still open to question. To study the origin of the optical-ultraviolet variability in AGNs, we compare light curves of two models to observations of quasar 0957+561 in terms of a structure function analysis. In the starburst (SB) model, random superposition of supernovae in the nuclear starburst region produces aperiodic luminosity variations, while in the disk-instability (DI) model, variability is caused by instabilities in the accretion disk around a supermassive black hole. We calculate fluctuating light curves and structure functions, V(τ), by simple Monte Carlo simulations on the basis of the two models. Each resultant V(τ) possesses a power-law portion, [V(τ)]1/2 ∝ τβ, at short time lags (τ). The two models can be distinguished by the logarithmic slope β; β ~ 0.74-0.90 in the SB model and β ~ 0.41-0.49 in the DI model, while the observed light curves exhibit β ~ 0.35. Therefore, we conclude that the DI model is favored over the SB model in explaining the slopes of the observational structure function in the case of 0957+561, though this object is a radio-loud object and thus is not really a fair test for the SB model. In addition, we examine the time asymmetry of the light curves by calculating V(τ) separately for the brightening and the decaying phases. The two models exhibit opposite trends of time asymmetry to some extent, although the present observation is not long enough to test this prediction.
Learning Performance of Networks like Elman ’ s Simple Recurrent Networks but having Multiple State Vectors

Target Papers: • William H. Wilson, A comparison of architectural alternatives for recurrent networks, Proceedings of the Fourth Australian Conference on Neural Networks, ACNN’93, Melbourne, 13 February 1993, 189-192. ftp://ftp.cse. unsw.edu. au/pub/users/billw/wilson.recurrent. ps.Z • William H. Wilson, Stability of learning in classes of recurrent and feedforward networks, in Proceedings of the Sixth Australian Conference on Neural Networks, ACNN’95, edited by Margaret Charles & Cyril Latimer, Sydney, 6-8 February 1995, 142-145. ISBN 0-909391-03-3 ftp://ftp.cse. unsw.edu. au/pub/users/billw/wilson.stability. ps.Z
Surface Realisation from Knowledge-Bases

Natural Language Generation (NLG) is the task of automatically producing natural language text to describe information present in non-linguistic data. It involves three main subtasks: (i) selecting the relevant portion of input data; (ii) determining the words that will be used to verbalise the selected data; and (iii) mapping these words into natural language text. The latter task is known as Surface Realisation (SR). In my thesis, I study the SR task in the context of input data coming from Knowledge Bases (KB). I present two novel approaches to surface realisation from knowledge bases: a supervised approach and a weakly supervised approach. In the first, supervised, approach, I present a corpus-based method for inducing a Feature Based Lexicalized Tree Adjoining Grammar (FB-LTAG) from a parallel corpus of text and data. The resulting grammar includes a unification based semantics and can be used by an existing surface realiser to generate sentences from test data. I show that the induced grammar is compact and generalises well over the test data yielding results that are close to those produced by a handcrafted symbolic approach and which outperform an alternative statistical approach. In the weakly supervised approach, I explore a method for surface realisation from KB data which uses a supplied lexicon but does not require a parallel corpus. Instead, I build a corpus from heterogeneous sources of domain-related text and use it to identify possible lexicalisations of KB symbols (classes and relations) and their verbalisation patterns (frames). Based on the observations made, I build different probabilistic models which are used for selection of appropriate frames and syntax/semantics linking while verbalising KB inputs. I evaluate the output sentences and analyse the issues relevant to learning from non-parallel corpora. In both these approaches, I use the data derived from an existing biomedical ontology as a reference input. The proposed methods are generic and can be easily adapted for input from other ontologies for which a parallel/non-parallel corpora exists.
Dynamics of Intra-EMS Interest Rate Linkages

Anumber of previous studies have questioned the dominant role of Germany within the European Monetary System (EMS). These conclusions are often based on empirical findings that the interest rates of EMS member countries are not affected by German interest rates, even in the long run. In this study, we demonstrate that intra-EMS interest rate differentials (vis-à-vis Germany) exhibit mean-reverting behavior characterized by long-memory dynamics. In a system incorporating six EMS countries and one non-EMS country (the U.S.A.), estimates from a fractional error correction model suggest the presence of short-run intra-EMS monetary-policy interdependencies but validate the German Dominance Hypothesis in the long run.
Enforcing Architectural Styles in Presence of Unexpected Distributed Reconfigurations

Architectural Design Rewriting (ADR, for short) is a rule-based formal framework for modelling the evolution of architectures of distributed systems. Rules allow ADR graphs to be refined. After equipping ADR with a simple logic, we equip rules with pre- and post-conditions; the former constraints the applicability of the rules while the later specifies properties of the resulting graphs. We give an algorithm to compute the weakest pre-condition out of a rule and its post-condition. On top of this algorithm, we design a simple methodology that allows us to select which rules can be applied at the architectural level to reconfigure a system so to regain its architectural style when it becomes compromised by unexpected run-time reconfigurations.
Optical density analysis of standard phantom for mammography quality control

Background/Objectives: The objective of this study was to analyze optic density of dummy lesions in breast phantom by mammography and understand whether the objectivity of visual inspections on dummy lesions upon appropriate and inappropriate decisions was correct quantitatively. Methods/Statistical analysis: Study subjects were 165 phantom images in 74 hospitals nationwide that passed the test of special medical equipment by Korean Institute for Accreditation of Medical Image from May 2016 to April 2017. Min (A), Max (B), Mean (C), and StdDev (D) were measured using color level information. Fibers, specks, and masses divided dummy lesions determined as appropriate or inappropriate in 165 images. For each divided dummy lesion, Min, Max, Mean concentration, and concentration difference summarized it. Findings: There was a significant difference in Max concentration (B) between dummy lesions of 9 (specks 3) and 10 (specks 4). In dummy lesion 12 (mass 1), there was no significant difference by each step, although the deviation between black and white was high since the scope of lesion was big. Upon analysis of optic density divided by appropriate and inappropriate decisions, there were significant differences in concentration difference (D) for fibers, Min (A) and Max (B) concentrations as well as concentration difference (D) for specks, and Max (B) and Mean (C) concentrations with concentration difference (D) for masses. Improvements/Applications: Visual inspections appeared to have difficulty in analyzing lesions due to the ambiguity of quantitative differences. Further developments of quantitative programs are needed to replace visual inspection for breast phantom lesions in mammography.