Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Policies in Asia and Europe in the Twenty-First Century

This article attempts to "globalize" the social policy literature, inspired by Ian Gough's argument that there are analytical lessons from the Organization for Economic Cooperation and Development (OECD) welfare state literature, and the rise of social policy analysis elsewhere. This explores the nature of social policy in three contexts, in the old OECD (the Netherlands, in particular) and in the emerging economies of East Asia (China) and South Asia (India). The comparison focuses on how economic contexts, politics and institution, and ideas and ideologies structure social policies. This shows that the constellations of and changes in social policy across those very different contexts can be characterized on two axes: a productivism-welfarism distinction, and Esping-Andersen's triad of universalism, corporatism, and liberalism. Moreover, the comparison shows that economic globalization has led to some degree of convergence—however, different politics and ideologies in which social policies are embedded, and not just levels of development, continue to result in social policy divergence.
Patterns of grassland productivity, composition and seed abundance, and the diet of the flock bronzewing pigeon Phaps histrionica at one site in northern Australia over a period of marked seasonal change

Abstract Context. Multiple scales of research are needed to understand the ecology and conservation requirements of species whose ecology is characterised by marked spatial and temporal dynamism. The flock bronzewing pigeon may provide a model for the conservation management of species with similar dispersive ecologies. Aims. This study aimed to document the species composition and its variability in seeds consumed by flock bronzewing pigeons across a period of contrasting seasonal conditions, and to relate this diet to variation in food resource availability. Methods. The diet of the flock bronzewing pigeon was described by analysis of the crop contents of samples collected over the period from June 2006 to September 2007 at one pastoral property on the Barkly Tablelands, Northern Territory. Variation in food resource availability was assessed using data from remote sensing, grassland community structure, and direct measurement of soil seed density. Multivariate statistical methods were used to test variation in plant community structure between years and among land units; generalised linear modelling was used to examine inter-annual variation in the abundance of key food plant species and seasonal variation in seed abundance. Key results. Across the period of this study, the diet of flock bronzewing pigeons on the Barkly Tableland was largely restricted to seeds of a small number of plant species within Mitchell grasslands. Dietary patterns varied between years; evidence from remote sensing, grassland community structure, and seed density was consistent with these dietary patterns. Conclusions. Flock bronzewing pigeons appear to be adapted to exploiting rare, episodic events, leading to high seed production by the ephemeral or annual component of perennial tussock grasslands. Key food plant species include the forbs Wedelia asperrima, Trichodesma zeylanicum and Phyllanthus lacerosus and the large-seeded annual grass Chionachne hubbardiana. These species may not be those that provide critical resources during unfavourable periods. Implications. Conservation management of flock bronzewing pigeons will entail strategies to maintain key food species in grazed landscapes, and to ensure replenishment of seed reserves of annual and ephemeral plant species. Management practices to achieve these goals may include rotational wet season spelling of paddocks. More information is required on the focal areas for persistence within these black-soil grassland landscapes.
Fatty acid composition of the longissimus dorsi muscle in crossbred steers fed different sources of fatty acids

The objective of this study was to evaluate the fatty acid composition of the longissimus dorsi muscle in carcasses of ¾ Bos taurus taurus ¼ Bos taurus indicus steers fed different sources of fatty acids. Thirty-six steers aged 14 months, with a mean live weight of 320 kg, were fed the following diets for 96 days: 1) control diet, containing no supplemental fat source; 2) CaSFA, diet containing 50 g calcium salts of fatty acids per kg total dry matter; 3) CS diet, containing 210 g cottonseed per kg total dry matter. The fatty acid composition of the longissimus dorsi muscle was determined by gas chromatography. No difference in slaughter weight, carcass weight, backfat thickness, or longissimus dorsi muscle area was observed between animals receiving the diets CaSFA and CS. Animals consuming the two fat-supplemented diets presented higher concentrations of oleic (C18:1), palmitic (C16:0) and stearic (C18:0) acids, corresponding to an average 80.76% of total fatty acids, and higher concentrations of vaccenic acid (C18:1 t11) in the muscle when compared with the control group. Supplementation of the diet of feedlot crossbred steers with CaSFA or cottonseed did not promote significant alterations in the lipid composition of the longissimus dorsi muscle.
Book Reviews: The Generative Lexicon

The past decade has seen a tremendous explosion in interest both in practical structures for lexicons and in theories of lexical semantics. Pustejovsky's book represents an interesting attempt at providing a new and powerful lexical semantic theory, taking as its starting point a serious view of the generative nature of lexical compositionality. The heart of the attempt lies in treating word senses in context as the result of complex but well-defined functions of relatively simple and open basic lexical meanings. The author borrows views from traditional linguistics and from philosophy (in some cases, reaching as far back as Aristotle) to find categories that motivate different forms of combination in differing cases, attempting to provide a wide range of rich interpretations from a relatively simple and narrow set of initial constructs. The strength of the work lies in its combination of broad principles for interpretation with very specific details and mechanisms. The book suffers, however, from a number of weaknesses. The first, and most obvious, lies in the dismissive treatment of modern lexical semantics. The author argues over and over that the standard approach to lexical semantics consists of a simple enumeration of meanings, with mechanisms to select among them. This simply is not true, and has not been for a very long time. To cite a single example, the extensive work in the theory of lexical relations, in several of its incarnations, uses far more sophisticated and layered representations. Indeed, in a number of areas (for instance, his discussion of merology and the difficulties of part-whole relations, as well as all his discussions of taxonomic and inheritance issues), it would appear that work within the community that has been concentrating on lexical relations could contribute substantially to this effort. There is, ultimately, no need for every book to acknowledge every other theory going. But when a volume chooses to characterize file current state of the field, it should at least mention all the dominant theories of recent years. At a different level, the early chapters abound with examples of legal/illegal sentence pairs, intended to be motivating. Such pairs are, of course, common throughout the literature. Unfortunately, in this case, a large proportion of the "illegal" examples looked fine to me. This may seem like picking an unnecessary nit--and normally, I would reject it as such--but when the contrasts are presented as motivating fundamental parts of a theory of word meaning, it is particularly important that they be convincing, or that, at the least, they be presented with some evidence that some particular population found them convincing. A great many of Pustejovsky's arguments throughout rest on face validity. It is troubling, therefore, that his data do not always seem valid on their face. A separate problem, less to do with content than with style, lies in the volume's uncomfortable relationship with its audience. It is hard to see whom the author had in mind as he wrote. In the early pages, Pustejovsky discusses at tedious length such
Japanese Morphological Analyzer using Word Co-occurrence – JTAG–

We developed a Japanese morphological analyzer that uses the co-occurrence of words to select the correct sequence of words in an unsegmented Japanese sentence. The co-occurrence information can be obtained from cases where the system incorrectly analyzes sentences. As the amount of information increases, the accuracy of the system increases with a small risk of degradation. Experimental results show that the proposed system assigns the correct phonological representations to unsegmented Japanese sentences more precisely than do other popular systems.
Enriching Cold Start Personalized Language Model Using Social Network Information

Personalized language models are useful in many applications, such as personalized search and personalized recommendation. Nevertheless, it is challenging to build a personalized language model for cold start users, in which the size of the training corpus of those users is too small to create a reasonably accurate and representative model. We introduce a generalized framework to enrich the personalized language models for cold start users. The cold start problem is solved with content written by friends on social network services. Our framework consists of a mixture language model, whose mixture weights are estimated with a factor graph. The factor graph is used to incorporate prior knowledge and heuristics to identify the most appropriate weights. The intrinsic and extrinsic experiments show significant improvement on cold start users.
Semi-quantitative analysis of arbuscular mycorrhizal colonization of onion roots inoculated with single or mixed species based upon PCR-RFLP

Abstract Arbuscular mycorrhizal (AM) fungi colonizing roots cannot be identified based on their hyphal morphology. We applied the PCR-RFLP technique to distinguish the AM fungal species inoculated to plants and to quantify the biomass in roots. Seedlings of onion were inoculated with single or mixed AM fungal species, Gigaspora margarita and / or Scutellospora cerradensis, and were raised in a growth chamber. About 1.2 kbp of the fungal 18S rRNA gene in the roots were amplified with the primer VANSI which is specific to AM fungi and the non-specific primer NS4. The restriction enzyme digestion with HincII of the PCR products enabled to distinguish these species. The amounts of PCR products were quantified based upon the relative brightness of the bands on agarose gel stained with ethidium bromide. In single species inoculation, the band brightness was correlated with the colonization percentage. In mixed species inoculation, the relative proportion of each species could be estimated. These results suggest that the current semi-quantitative PCR-RFLP technique is effective to analyze the interaction between these two fungal species in roots.
Accumulation of Glutamine by Suspension Cultures of Symphytum officinale

A callus was induced from the veins of a leaf of Symphytum officinale, comfrey, on a medium containing the inorganic elements reported by Murashige and Skoog with addition of 3% sucrose, 0.5 mg/liter 2,4-D and 0.3~3.0 mg/liter kinetin.Suspension cultures of this cell line obtained from the callus were shown to accumulate a large amount of L-glutamine intracellularly, The effect of growth hormones and nutrients on accumulation of the amino acid has been examined in suspension cultures. The most suitable concentrations of 2,4-D and kinetin for glutamine accumulation were 0.3 mg/liter each. The presence of potassium nitrate as a nitrogen source was beneficial for growth and ammonium nitrate stimulated the accumulation of glutamine. High levels of these nitrogen sources in the medium were required for obtaining a high level of glutamine. The concentration of glutamine accumulated reached to approximately 20% of dry cell weight when S. officinale was incubated in the medium containing 0.495 % of ammonium nitra.
Toshiba MT System Description for the WAT2015 Workshop

This paper provides the system description of Toshiba Machine Translation System for the 2nd Workshop on Asian Translation (WAT2015). We participated in all tasks that consist of “scientific papers subtask” and “patents subtask”. We submitted statistically post edited translation (SPE) results based on our rule based translation system and SMT for each language pair. In addition, we submitted system combination results between SPE and SMT with a recurrent neural language model (RNNLM). In experimental results, the system combination achieved higher BLEU scores than single system with reranking. We also obtained improvements in Chinese translation in crowdsourcing evaluations.
Intragenomic variation in the internal transcribed spacer regions between 16S–23S rRNA genes among the three copies of Sinorhizobium fredii strains

Abstract The soybean-nodulating Sinorhizobium fredii strain has been reported to possess three copies of rRNA gene operons. In the present study, we investigated the diversity of the 16S–23S rDNA internal transcribed spacer (ITS) regions of S. fredii strains. Based on the sequences of the ITS regions, we divided the sequences of the S. fredii strains into two groups, type A and type B. A dot-matrix analysis indicated that the region flanked by tRNA-Ile and tRNA-Ala is longer in type A than in type B, whereas type B sequences possess longer regions upstream of tRNA-Ile and downstream of tRNA-Ala than those of the type A sequence. Restriction fragment length polymorphism of polymerase chain reaction product (PCR-RFLP) of the ITS region in the cloned plasmids as templates could reconstruct the PCR-RFLP pattern from the total DNA as a template. The results of Southern hybridization using the insert sequence between tRNA-Ile and tRNA-Ala in type A as a probe indicated differences in the copy numbers of the type A ITS regions among the strains tested. These results indicated that S. fredii strains possess the type A and type B sequences of the ITS regions at ratios of 3:0, 2:1, 1:2 or 0:3. These S. fredii strains may be useful biological materials for the study of intraspecific variations.
Methods and Theories for Large-scale Structured Prediction

Many important NLP tasks are casted as structured prediction problems, and try to predict certain forms of structured output from the input. Examples of structured prediction include POS tagging, named entity recognition, PCFG parsing, dependency parsing, machine translation, and many others. When apply structured prediction to a specific NLP task, there are the following challenges:1. Model selection: Among various models/algorithms with different characteristics, which one should we choose for a specific NLP task? 2. Training: How to train the model parameters effectively and efficiently? 3. Overfitting: To achieve good accuracy on test data, it is important to control the overfitting from the training data. How to control the overfitting risk for structured prediction? This tutorial will provide a clear overview of recent advances in structured prediction methods and theories, and address the above issues when we apply structured prediction to NLP tasks. We will introduce large margin methods (e.g., perceptrons, MIRA), graphical models (e.g., CRFs), and deep learning methods (e.g., RNN, LSTM), and show the respective advantages and disadvantages for NLP applications. For the training algorithms, we will introduce online/ stochastic training methods, and we will introduce parallel online/stochastic learning algorithms and theories to speed up the training (e.g., the Hogwild algorithm). For controlling the overfitting from training data, we will introduce the weight regularization methods, structure regularization, and implicit regularization methods.
Machine Learning for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language

Ageing trend in populations is correlated with increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. With this in mind, researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In this paper, we introduce an automatic dementia screening system for ageing Deaf signers of British Sign Language (BSL), using Convolutional Neural Networks (CNN), by analysing the sign space envelope and facial expression of BSL signers using normal 2D videos from BSL corpus. Our approach firstly establishes an accurate real-time hand trajectory tracking model together with a real-time landmark facial motion analysis model to identify differences in sign space envelope and facial movement as the keys to identifying language changes associated with dementia. Based on the differences in patterns obtained from facial and trajectory motion data, CNN models (ResNet50/VGG16) are fine-tuned using Keras deep learning models to incrementally identify and improve dementia recognition rates. We report the results for two methods using different modalities (sign trajectory and facial motion), together with the performance comparisons between different deep learning CNN models in ResNet50 and VGG16. The experiments show the effectiveness of our deep learning based approach in terms of sign space tracking, facial motion tracking and early stage dementia performance assessment tasks. The results are validated against cognitive assessment scores as of our ground truth data with a test set performance of 87.88%. The proposed system has potential for economical, simple, flexible, and adaptable assessment of other acquired neurological impairments associated with motor changes, such as stroke and Parkinson’s disease in both hearing and Deaf people.
Machine Translation from Spoken Language to Sign Language using Pre-trained Language Model as Encoder

Sign language is the first language for those who were born deaf or lost their hearing in early childhood, so such individuals require services provided with sign language. To achieve flexible open-domain services with sign language, machine translations into sign language are needed. Machine translations generally require large-scale training corpora, but there are only small corpora for sign language. To overcome this data-shortage scenario, we developed a method that involves using a pre-trained language model of spoken language as the initial model of the encoder of the machine translation model. We evaluated our method by comparing it to baseline methods, including phrase-based machine translation, using only 130,000 phrase pairs of training data. Our method outperformed the baseline method, and we found that one of the reasons of translation error is from pointing, which is a special feature used in sign language. We also conducted trials to improve the translation quality for pointing. The results are somewhat disappointing, so we believe that there is still room for improving translation quality, especially for pointing.
Population differentiation of the European pond turtle (Emys orbicularis) in Poland inferred by the analysis of mitochondrial and microsatellite DNA: implications for conservation

We investigated the genetic diversity of Polish populations of the European pond turtle (Emys orbicularis) using complete sequences of the mitochondrial cytochrome b gene and allelic variation at thirteen microsatellite loci. We collected data from 146 turtles from 28 locations covering most of the species’ range in Poland. Our results showed a low haplotype diversity and high levels of microsatellite diversity in all populations. We applied two Bayesian approaches using the multilocus data and determined relationships of mtDNA haplotypes by constructing a parsimony network. We observed relatively consistent results of the two Bayesian clustering methods and largely concordant differentiation patterns between mitochondrial and microsatellite markers. All eastern Polish populations were assigned in a highly supported eastern group, while the majority of turtles from western Poland were joined in a distinct western group. The most probable number of population clusters corresponded to the number of native mtDNA lineages. Additionally, we detected a hierarchical structure within the eastern lineage splitting the populations into two subgroups. Polish populations generally constitute genetically homogenous groups, characterized by a low population admixture, with only negligible impact from allochthonous turtles.
Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data

Abstract Methods for small area estimations were compared for estimating the proportion of forest and growing stock volume of temperate mixed forests within a district of a member state (canton) in Switzerland. The estimators combine terrestrial data with remotely sensed auxiliary data. By using different model types, different sources of auxiliary data and different methods of processing the auxiliary data, the increase in estimation precision was tested. Using the canopy height derived from remote sensing data, the growing stock volume and the proportion of forest were estimated. The regression models used for the small area estimation provided a coefficient of determination of up to 68% for the timber volume. The proportion of plots correctly classified into forest and non-forest plots ranged between 0.9 and 0.98. Models calibrated over forest area only resulted in a maximal coefficient of determination of 37%. Even though these coefficients indicate a moderate model quality, the use of remote sensing data clearly improved the estimation precision of both the proportion of forest and the growing stock volume. Generally, Lidar data led to slightly higher estimates compared to data from aerial photography. It was possible to reduce the variance of the estimated proportion of forest to nearly one tenth compared with the variance based on the terrestrial measurements alone. Similarly, the variance of the growing stock volume could be reduced to one fourth as compared with the variance based solely on the terrestrial measurements.
利用機器學習於中文法律文件之標記、案件分類及量刑預測 (Exploiting Machine Learning Models for Chinese Legal Documents Labeling, Case Classification, and Sentencing Prediction) [In Chinese]

This paper exploits machine learning methods to separate robbery and intimidation cases, and predicting their sentencing by considering defined legal factors. We introduce a framework to fetch 21 legal factor labels of robbery and intimidation cases, then use the labels for case classification and sentencing prediction. Our experiments show that the legal factor labels can indeed improve the results of case classification and sentencing prediction. We then discuss the influence of these legal factors in both case classification and sentencing prediction tasks.