Recognition Rule Research Articles

Does recognition versus disclosure of pension liabilities affect credit ratings? Evidence from Japan

Whether credit market participants process disclosure and recognition of pension information differently has not been fully explored. To fill this gap, this study investigates whether the change in a pension accounting standard related to the recognition rule influences firms’ credit risk in Japan. Statement No. 26, Accounting Standard for Retirement Benefits, stipulates that firms recognize previously disclosed pension information on the balance sheet. Employing the implementation of Statement No. 26, I explore how differences between disclosed and recognized pension liabilities affect credit ratings. I find that off–balance sheet pension liabilities are associated with credit ratings prior to Statement No. 26. I also find similar relations between disclosure versus recognition of pension liabilities and credit ratings. When firms have a small pension plan deficit, off–balance sheet pension liabilities provide risk-relevant information, and the risk relevance of disclosed and recognized pension liabilities is statistically similar. My overall results suggest that mandating pension recognition does not affect the credit risk assessment by credit rating agencies.

Zur Operationalisierung literaturwissenschaftlicher Begriffein der algorithmischen Textanalyse. Eine Annäherung über Norbert Altenhofers hermeneutische Modellinterpretation von KleistsDas Erdbeben in Chili

Zur Operationalisierung literaturwissenschaftlicher Begriffe<b>in der algorithmischen Textanalyse. Eine Annäherung über Norbert Altenhofers hermeneutische Modellinterpretation von Kleists</b><b><i>Das Erdbeben in Chili</i></b>

A Novel Method of Chinese Electronic Medical Records Entity Labeling Based on BIC model

In the field of bio-medicine, mass data are generated every day, such as Chinese electronic medical record (EMR), containing massive medical terminology and specific categories of entities. The way to analyze and obtain effective information from these sparse data is a difficulty in research. As the foundation of analyzing huge amount of biomedical text data, Named Entity Recognition (NER) is essential in Natural Language Processing (NLP) complementing with effective labeling data. One of the two basic sequence labeling methods is rule-based bulk corpus tagging, requiring domain experts to establish targeted recognition rule base. However, in the application field, this method is single, and the portability does not make the expectation, bringing great limitations; The other is complete manual labeling, but it is time-consuming and laborious. Based on Bidirectional Long Short-Term Memory network (BiLSTM), Iterated Dilated Convolution Neural Network (IDCNN) and Conditional Random Field (CRF), we proposed the BIC model. This paper proposes a method for EMR entity labeling based on BIC model, realizing automatic annotation of Chinese EMR data. Machine labeling data can be used after manual review, which will reduce the workload of manual labeling bestially. Compared with other models, F1 value of BIC model reached 91.90% in CCKS2017 dataset, and 78% in PACS report data. Experiments show that our method is superior to the others.

A Survey on Animal Voice Recognition: Mood and Behaviour using Machine Learning Approach

Voice recognition frameworks turned into the fundamental applications for discourse recognition innovation, a creature affirmation framework bolstered creature voice design recognition rule has been created. The proposed creature voice recognition framework uses the zero-cross rate, MelFrequency Cepstral Coefficient and Dynamic-Time wrap joint calculations in light of the fact that the instruments for recollecting the voice of the genuine creature. ZCR is utilized for the begin point recognition of testing voice indicated the commotion might be expelled. MFC is utilized for the strategy for quality extraction wherever an extra consolidated and less excess of the delegate voice might be accumulated from the testing voice. while the voice order will be finished by abuse DT WRAP rule. At that point the voice coordinating is done to distinguish and characterize the creature seen by the framework. The program made and data noted demonstrates the recognition framework works.

Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images

Sparse-representation-based classification (SRC) has been showing a good performance for face recognition in recent years. But SRC is not good at face recognition with low quality images (e.g., disguised, corrupted, occluded, and so on) which often appear in practical applications. To solve the problem, in this paper, we propose a novel SRC-based method for face recognition with low quality images named sparse low-rank component-based representation (SLCR). In SLCR, we utilize low-rank matrix recovery on the training data set to obtain low-rank components and non-low-rank components, which are used to construct the dictionary. The new dictionary is capable of describing facial features better, especially for low quality face samples. Furthermore, the minimum class-wise reconstruction residual is used as the recognition rule, leading to a substantial improvement on the proposed SLCR’s performance. Extensive experiments on benchmark face databases demonstrate that the proposed method is consistently superior to other sparse-representation-based approaches for face recognition with low quality images.

Spatially induced nestedness in a neutral model of phage-bacteria networks.

Ecological networks, both displaying mutualistic or antagonistic interactions, seem to share common structural traits: the presence of nestedness and modularity. A variety of model approaches and hypothesis have been formulated concerning the significance and implications of these properties. In phage-bacteria bipartite infection networks, nestedness seems to be the rule in many different contexts. Modeling the coevolution of a diverse virus–host ensemble is a difficult task, given the dimensionality and multi parametric nature of a standard continuous approximation. Here, we take a different approach, by using a neutral, toy model of host–phage interactions on a spatial lattice. Each individual is represented by a bit string (a digital genome) but all strings in each class (i.e. hosts or phages) share the same sets of parameters. A matching allele model of phage-virus recognition rule is enough to generate a complex, diverse ecosystem with heterogeneous patterns of interaction and nestedness, provided that interactions take place under a spatially constrained setting. It is found that nestedness seems to be an emergent property of the co-evolutionary dynamics. Our results indicate that the enhanced diversity resulting from localized interactions strongly promotes the presence of nested infection matrices.

The Economic Consequences of Expanding Accounting Recognition

This article investigates the economic consequences of including more hard-to-measure future activities in a firm’s accounting measurements. Using a simple model of endogenous investment in which payoffs are measured by either a restrictive or expanded recognition rule, we show that, in the process of expanding accounting recognition, firms’ internal investment efficiency and external share-price risk premium may not necessarily be a trade-off. In particular, we show that the consequences of an accounting scope expansion depend on the investment environment (e.g., growth prospects) and the inherent measurement characteristics (e.g., measurement noise). For example, even with a higher measurement noise, an expanded accounting recognition may generate a lower risk premium in the share price. More surprisingly, it may lead to a higher investment efficiency and a lower risk premium at the same time. The underlying driving force is that different accounting regimes can affect the risk premium indirectly through their impacts on the investment level, beyond directly through the different measurement noise levels they bring.

Mutual Recognition, Choice of Forum and Lis Pendens: A Civil Law Threesome Transposed

The area of freedom, security and justice, built on the notion of mutual recognition, operates a hard and fast rule of ne bis in idem in the field of criminal law. This rule is supported by a system of voluntary consultation in case of parallel proceedings, but this system is incomplete. There is no common binding procedure for dealing with lis pendens and related actions. This is all the more problematic as the recognition rule is not supported by a preliminary agreement on the distribution of jurisdiction between the Member States in intra EU cases. The policy options described in the ELI proposal address the latter two shortcomings. In this contribution I use insights from the field of private international law to comment on the current state of the law in criminal law and to discuss the solutions embedded in the proposals. The Brussels I regulation 1215/2012 (Brussels I recast or Brussels Ibis) will function as the main comparator, though at some point references will be made to the wider body of private international law instruments.

IMPACT OF FASB REVENUE RECOGNITION RULE ON FIRM VALUATION

ABSTRACT The paper assesses the potential impact of the amended FASB revenue recognition rule on firm valuation when a firm restates its financial statement. I analyze firm value-relevant information of the amended earnings due to the restatement initiated from the change in revenue recognition rules. I find that the financial markets may not have imputed the restated, non-deferred EPS valuation into firm value. Keywords Revenue recognition, restatement, earnings per share, valuation, financial statements

Application of field asymmetric ion mobility spectrometry in moldy tobacco recognition

Field asymmetric ion mobility spectrometry (FAIMS) was used to distinguish moldy tobacco and normal tobacco. Tobacco ion peak characteristics from FAMIS analysis and a recognition rule were established. The key parameters for the moldy tobacco recognition in FAIMS included compensation voltage (CV), dispersion electric field (DF) and ion current value (ICV). When the DF and CV value were set as the followings:DF=50%, CV=-0.788 V, or DF=55%, CV=-0.882 V, the characteristic ion peaks of moldy tobacco tested under positive mode were observed. ICV values increased with the degree of moldy severity; therefore, the ICV values were used to distinguish moldy tobacco under DF and CV conditions described above. When the ICV values were ICV≥0.13 and 0.20>ICV≥0.11, respectively, samples were determined as mild moldy; when the ICV values were≥0.24 and≥0.20, respectively, samples were determined as severe moldy; when the ICV values were > 0.13 and 0.11, respectively, the moldy condition could not be determined. These rules were applied to distinguish moldy tobacco among 65 types of tobacco samples. Among which, 29 types of normal tobacco, 22 types of mild moldy tobacco and 6 types of severe moldy tobacco were correctly identified, and 4 types of tobacco were not determined. The recognition accuracy rate was 93.4%.

Rapid identification of Lonicerae japonicae Flos and Lonicerae Flos by Fourier transform infrared (FT-IR) spectroscopy and two-dimensional correlation analysis

Lonicerae japonicae Flos (LJF) and Lonicerae Flos (LF) are widely-used herbs derived from several plants of the genus Lonicera with similar appearances. LF are usually misused or counterfeited as LJF for economically motivated adulteration. However, the saponins in LF may cause serious side-effects. In this research, the infrared spectroscopic tri-step identification approach is used to develop a simple and rapid method to discriminate LJF and LF to ensure the safety and efficacy of these herbal drugs. In the primary identification by Fourier transform infrared spectra, LJF and LF show different peaks near 1534, 1404, and 781 cm−1. In the secondary identification by the second derivative infrared spectra, LJF and LF show more different peaks near 1078, 1050, 988, 923, 855, 815, and 781 cm−1. In the tertiary identification by the two-dimensional correlation infrared spectra, the differences between LJF and LF are shown more remarkably and convincingly. The results show the potential of the infrared spectroscopic tri-step identification approach in the rapid identification of LJF and LF when the samples are too few to build a statistical recognition rule. This should be very helpful to ensure the quality, safety, and efficacy of LJF and LF for clinical applications.

THE EFFECTS OF FIRM CHARACTERISTICS AND RECOGNITION POLICY ON EMPLOYEE STOCK OPTIONS PRICES AFTER CONTROLLING FOR SELF-SELECTION

We examine whether and how firm characteristics, including firm size and liquidity, affect the relation between employee stock option (ESO) grants (as proxied by disclosed ESO expenses) and firm value. We also investigate how the implementation of a new share-based compensation recognition rule affects the pricing effect of ESOs. Prior studies have provided mixed results concerning how ESOs affect firm value. We argue that their findings could be attributable to self-selection and a non-uniform ESO-share price relation. We use the threshold model to address our research questions after controlling for self-selection bias. We find that markets tend to positively price ESOs in the case of firms characterized by large size and low liquidity. In addition, we find that after the new rule came into effect, ESOs became positively associated with firm value. These results are congruent with ownership and symbolic value theories, the lifecycle stages hypothesis and the contention that an ESO expensing policy enhances the quality of financial statements.

Recognition rules for binding of Zn-Cys2His2 transcription factors to operator DNA

The molecules of Zn-finger transcription factors consist of several similar small protein units. We analyzed the crystal structures 46 basic units of 22 complexes of Zn-Cys2His2 family with the fragments of operator DNA. We showed that the recognition of DNA occurs via five protein contacts. The canonical binding positions of the recognizing α-helix were −1, 3, 6, and 7, which make contacts with the tetra-nucleotide sequence ZXYZ of the coding DNA strand; here the canonical binding triplet is underlined. The non-coding DNA strand forms only one contact at α-helix position 2. We have discovered that there is a single highly conservative contact His7α with the phosphate group of nucleotide Z, which precedes each triplet XYZ of the coding DNA chain. This particular contact is invariant for the all Zn-Cys2His2 family with high frequency of occurrence 83%, which we considered as an invariant recognition rule. We have also selected a previously unreported Zn-Cys2His2-Arg subfamily of 21 Zn-finger units bound with DNA triplets, which make two invariant contacts with residues Arg6α and His7α with the coding DNA chain. These contacts show frequency of occurrence 100 and 90%, and are invariant recognition rule. Three other variable protein-DNA contacts are formed mainly with the bases and specify the recognition patterns of individual factor units. The revealed recognition rules are inherent for the Zn-Cys2His2 family and Zn-Cys2His2-Arg subfamily of different taxonomic groups and can distinguish members of these families from any other family of transcription factors.

Firm Characteristics, Recognition Policy and Pricing of Employee Stock Option

We examine whether and how firm characteristics, including firm size and liquidity and the implementation of a new share-based compensation recognition rule affect the relation between the employee stock option (ESO) grants (as proxied by the disclosed ESO expenses) and firm value. Prior studies provide mixed results concerning how ESOs affect firm value. We argue their findings could be attributable to self-selection and a non-uniform ESO-share price relation. We use the threshold model to address our research questions after controlling for self-selection bias. We find that markets tend to positively price ESOs for firms of large size and those with low liquidity. In addition, we find that after the new rule came into effect, ESOs became positively associated with firm value. These results are congruent with the ownership and symbolic value theory, lifecycle stages hypothesis and the contention that the ESO expensing policy enhances the quality of financial statements.

Intra-party decision making, party formation, and moderation in multiparty systems

We establish coalitional stable party structures of a party formation game in an elected assembly. Farsighted political players can commit to form parties and to vote on policies according to the party position which is determined by intra-party majority rule. Parties may form governments and block proposals by a randomly selected member of the government. If the government recognition rule allows for the formation of multiparty governments, the median parliamentarian either realizes her ideal point or a policy lottery which she strictly prefers to the status quo. This outcome is enforced by the threat of forming a moderating centre party.

Accounting for Past and Future Actions

This paper investigates the economic consequences of including more hard-to-measure future activities in a firm's accounting measurements. Using a simple model of endogenous investment in which payoffs are measured by either a restrictive or expanded recognition rule, we show that, in the process of expanding accounting recognition, firms' internal investment efficiency and external share-price risk premium may not necessarily be a trade-off. In particular, we show that the consequences of moving from Partial to Full accounting depend on the investment environment (e.g., growth prospects) and the inherent measurement characteristics (e.g., measurement noise). For example, even with a higher measurement noise, Full accounting may generate a lower risk premium in the firm's share price than Partial accounting. More surprisingly, an expanded recognition may lead to a higher investment efficiency and a lower risk premium at the same time. The underlying driving force is that endogenous investment choices make endogenous the total uncertainty of the firm's cash flows and the resolution of the uncertainty due to the accounting report.

The dynamics of distributive politics

We study dynamic committee bargaining over an infinite horizon with discounting. In each period, a committee proposal is generated by a random recognition rule, the committee chooses between the proposal and a status quo by majority rule, and the voting outcome in period t becomes the status quo in period t + 1. We study symmetric Markov equilibria of the resulting game and conduct an experiment to test hypotheses generated by the theory for pure distributional (divide-the-dollar) environments. In particular, we investigate the effects of concavity in the utility functions, the existence of a Condorcet winning alternative, and the discount factor (committee “impatience”). We report several new findings. Voting behavior is selfish and myopic. Status quo outcomes have great inertia. There are strong treatment effects that are in the direction predicted by the Markov equilibrium. We find significant evidence of concave utility functions.

Immune algorithm with dynamic environments and its application to greenhouse control

A novel immune algorithm with dynamic environments (IADE), suitable for time-varying single-objective optimization problems with the static or variable dimension of design space, is proposed based on the immune response principle. Several immune operators, relying upon the functions or metaphors of somatic maturation, immune memory and immune cells, are designed to adapt the changing environment and track the location of the optimum. Especially, the environmental recognition rule and memory pool are established to speed up to search the optimum of the environment. Several algorithms reported are participated in comparison against IADE through using theoretical test problems and a practical greenhouse control problem. Preliminary experiments show that IADE can not only obtain great superiority, but also track rapidly time-varying environments. Comparative analysis illustrates IADE’s potential value.

Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control

A novel multiobjective optimization immune algorithm in dynamic environments, as associated with Pareto optimality and immune metaphors of germinal center in the immune system, is proposed to deal with a class of dynamic multiobjective optimization problems which the dimension of the objective space may change over time. Several immune operators, depending on both somatic maturation and T-cell regulation, are designed to adapt to the changing environment so that the algorithm can achieve a reasonable tradeoff between convergence and diversity of population, among which an environmental recognition rule related to the past environmental information is established to identify an appearing environment. Preliminary experiments show that the proposed algorithm cannot only obtain great superiority over two popular algorithms, but also continually track the time-varying environment. Comparative analysis and practical application illustrate its potential.

Accounting Treatment of Inherent versus Incentive Uncertainties and the Capital Structure of the Firm

This paper studies the accounting treatment of uncertainty and how it affects a firm's capital structure. We distinguish two sources of uncertainty that raise reliability concerns: inherent uncertainty and incentive uncertainty. By inherent uncertainty, we refer to uncertainty about the quality of raw information regarding future cash flows. By incentive uncertainty, we refer to uncertainty about the quality of accounting numbers conveying the raw information. We explore features of accounting that can effectively deal with these two types of uncertainties in order to aid in the debt-equity decision of the firm. To handle inherent uncertainty, preferable accounting involves flexible revenue/expense recognition rules that recognize more profit when the uncertainty level is low. To deal with incentive uncertainty, a stringent revenue/expense recognition rule may be desirable to fend off management's opportunistic reporting behavior. Inflexible accounting rules cause a firm's financing choices to deviate from what would hold with complete information. Given any accounting rule, an information environment with a lower (higher) uncertainty regarding future cash inflows leads to higher (lower) expected debt financing. This is because assessed default risk is increasing in the uncertainty of future cash inflows, holding the uncertainty of the outflows constant.