Lessons Learned and Prospects for Reform

Property Tax in Asia: Policy and Practice. Chapter 3 - Lessons Learned and Prospects for Refor

Current Policies and Practices

Asian jurisdictions tax real property in many different ways: some tax ownership and some land use; some tax land and some land and buildings; and some have no property tax. These broad differences in what they tax lead to important differences in how they tax real property: how they value property, what they do not tax, and how they go about their collections. This chapter compares practices in all 13 jurisdictions with good practices in property taxation to suggest the best way forward. It begins with a review and analysis of how these jurisdictions have structured their property tax bases and rates to mobilize revenues. Most have gone to great lengths to reduce the burden on property taxpayers. The chapter then describes the development of the fiscal cadastre-those factors required for implementation of a property tax system. Here, valuation, billing, collection, and enforcement are compared and discussed in some detail. We also take up the subject of taxes on property transfers and discuss why this has long been a missing link in achieving the goals of better property taxation. The chapter closes with a discussion of how jurisdictions and territories in Asia have attempted to use land and property taxes to influence the distribution of tax burdens and the efficiency of land use

Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation

Self-diffusion coefficients, $D^*$, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean-squared displacements (MSDs) of mobile species. MSDs derived from simulation suffer from statistical noise, which introduces uncertainty in the resulting estimate of $D^*$. An optimal scheme for estimating $D^*$ will minimise this uncertainty, i.e., will have high statistical efficiency, and will give an accurate estimate of the uncertainty itself. We present a scheme for estimating $D^*$ from a single simulation trajectory with high statistical efficiency and accurately estimating the uncertainty in the predicted value. The statistical distribution of MSDs observable from a given simulation is modelled as a multivariate normal distribution using an analytical covariance matrix for an equivalent system of freely diffusing particles, which we parameterise from the available simulation data. We then perform Bayesian regression to sample the distribution of linear models that are compatible with this model multivariate normal distribution, to obtain a statistically efficient estimate of $D^*$ and an accurate estimate of the associated statistical uncertainty

Context and Comparative Analysis

Property taxation is not new to Asia. China has some of the oldest examples of property and land taxes, the Philippine version has been emerging since 1901, and the property tax laws in Hong Kong (a special administrative region, or SAR, of China) were in place in 1845. Some Asian jurisdictions have modernized their property taxes to keep in step with their economic growth, but others have allowed their property taxes to fall into disrepair. This analysis aims to show how to make good practices better and how to put weak practices on a path to improvement. This chapter provides an overview of the jurisdictions chosen for in-depth analysis and examines the extent to which they represent South and East Asia. It also presents a statistical analysis of the determinants of regional variations in property tax revenues and compares property tax performance in Asia with the rest of the world, which helps explain why some jurisdictions and regions use property and land taxes more than others

Inhibition of Poly(ADP-Ribose) polymerase enhances the toxicity of 131I-Metaiodobenzylguanidine/Topotecan combination therapy to cells and xenografts that express the noradrenaline transporter

Targeted radiotherapy using [131I]meta-iodobenzylguanidine ([131I]MIBG) has produced remissions in some neuroblastoma patients. We previously reported that combining [131I]MIBG with the topoisomerase I (Topo-I) inhibitor topotecan induced long-term DNA damage and supra-additive toxicity to NAT-expressing cells and xenografts. This combination treatment is undergoing clinical evaluation. This present study investigated the potential of PARP-1 inhibition, in vitro and in vivo, to further enhance [131I]MIBG/topotecan efficacy

Towards a Classifier to Recognize Emotions Using Voice to Improve Recommendations

[EN] The recognition of emotions in tone voice is currently a tool with a high potential when it comes to making recommendations, since it allows to personalize recommendations using the mood of the users as information. However, recognizing emotions using tone of voice is a complex task since it is necessary to pre-process the signal and subsequently recognize the emotion. Most of the current proposals use recurrent networks based on sequences with a temporal relationship. The disadvantage of these networks is that they have a high runtime, which makes it difficult to use in real-time applications. On the other hand, when defining this type of classifier, culture and language must be taken into account, since the tone of voice for the same emotion can vary depending on these cultural factors. In this work we propose a culturally adapted model for recognizing emotions from the voice tone using convolutional neural networks. This type of network has a relatively short execution time allowing its use in real time applications. The results we have obtained improve the current state of the art, reaching 93.6% success over the validation set.This work is partially supported by the Spanish Government project TIN2017-89156-R, GVA-CEICE project PROMETEO/2018/002, Generalitat Valenciana and European Social Fund FPI grant ACIF/2017/085, Universitat Politecnica de Valencia research grant (PAID-10-19), and by the Spanish Government (RTI2018-095390-B-C31).Fuentes-López, JM.; Taverner-Aparicio, JJ.; Rincón Arango, JA.; Botti Navarro, VJ. (2020). Towards a Classifier to Recognize Emotions Using Voice to Improve Recommendations. Springer. 218-225. https://doi.org/10.1007/978-3-030-51999-5_18S218225Balakrishnan, A., Rege, A.: Reading emotions from speech using deep neural networks. Technical report, Stanford University, Computer Science Department (2017)Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)Kerkeni, L., Serrestou, Y., Mbarki, M., Raoof, K., Mahjoub, M.: Speech emotion recognition: methods and cases study, pp. 175–182 (2018)McCluskey, K.W., Albas, D.C., Niemi, R.R., Cuevas, C., Ferrer, C.: Cross-cultural differences in the perception of the emotional content of speech: a study of the development of sensitivity in Canadian and Mexican children. Dev. Psychol. 11(5), 551 (1975)Paliwal, K.K.: Spectral subband centroid features for speech recognition. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 1998 (Cat. No. 98CH36181), vol. 2, pp. 617–620. IEEE (1998)Paulmann, S., Uskul, A.K.: Cross-cultural emotional prosody recognition: evidence from Chinese and British listeners. Cogn. Emot. 28(2), 230–244 (2014)Pépiot, E.: Voice, speech and gender: male-female acoustic differences and cross-language variation in English and French speakers. Corela Cogn. Représent. Lang. (HS-16) (2015)Picard, R.W., et al.: Affective computing. Perceptual Computing Section, Media Laboratory, Massachusetts Institute of Technology (1995)Rincon, J., de la Prieta, F., Zanardini, D., Julian, V., Carrascosa, C.: Influencing over people with a social emotional model. Neurocomputing 231, 47–54 (2017)Russell, J.A., Lewicka, M., Niit, T.: A cross-cultural study of a circumplex model of affect. J. Pers. Soc. Psychol. 57(5), 848 (1989)Schuller, B., Rigoll, G., Lang, M.: Hidden Markov model-based speech emotion recognition, vol. 2, pp. 401–404 (2003)Schuller, B., Villar, R., Rigoll, G., Lang, M.: Meta-classifiers in acoustic and linguistic feature fusion-based affect recognition, vol. 1, pp. 325–328 (2005)Thompson, W., Balkwill, L.-L.: Decoding speech prosody in five languages. Semiotica 2006, 407–424 (2006)Tyagi, V., Wellekens, C.: On desensitizing the Mel-cepstrum to spurious spectral components for robust speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP 2005, vol. 1, pp. I–529. IEEE (2005)Ueda, M., Morishita, Y., Nakamura, T., Takata, N., Nakajima, S.: A recipe recommendation system that considers user’s mood. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services, pp. 472–476. ACM (2016)Zhang, B., Quan, C., Ren, F.: Study on CNN in the recognition of emotion in audio and images. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–5, June 201

Kinisi:Bayesian analysis of mass transport from molecular dynamics simulations

kinisi is a Python package for estimating transport coefficients—e.g., self-diffusion coefficients, ∗—and their corresponding uncertainties from molecular dynamics simulation data. It includes an implementation of the approximate Bayesian regression scheme described in McCluskey etal. (2023), wherein the mean-squared displacement (MSD) of mobile atoms is modelled as a multivariate normal distribution that is parametrised from the input simulation data. kinisi uses Markov-chain Monte Carlo (Foreman-Mackey et al., 2019; Goodman &amp; Weare, 2010) to sample this model multivariate normal distribution to give a posterior distribution of linear model ensemble MSDs that are compatible with the observed simulation data. For each linear ensemble MSD, x(), a corresponding estimate of the diffusion coefficient, ̂∗ is given via the Einstein relation, ̂∗ =1d x() / 6 d where is time. The posterior distribution of compatible model ensemble MSDs calculated by kinisi gives a point estimate for the most probable value of ∗ , given the observed simulation data, and an estimate of the corresponding uncertainty in ̂∗. kinisi also provides equivalent functionality for estimating collective transport coefficients, i.e., jump-diffusion coefficients and ionic conductivities<br/