Miracle-dz/UniFAR-FTUs huggingface dataset:
How Institutions Matter in the Organization and Regulation of Agricultural Markets after Liberalization ? an Institutional Analysis of Hog Market Integration in Vietnam

The recent literature on market integration in developing countries stresses the need for using information on institutional marketing arrangements in the interpretation of cointegration results. In the context of the Vietnamese hog market, this study examines market integration using conventional tests of cointegration between prices of spatial markets. However, to address the importance of institutions, principles from the New Institutional Economics framework are applied to augment results from the conventional approach. The results suggest that price formation and transmission between spatial markets are influenced not only by supply and demand, but also by market institutional structure. First, we describe the Vietnamese post-liberalization context and resulting hog market organization. Second, we analyze market integration by modeling weekly pork price series in 19 provinces of the country. A VAR model of price fluctuations captures, in a reduced form, the net effects of microeconomic(,) as well as institutional structural mechanisms underlying market interdependencies. In this case, the multivariate Johansen method is applied to identify long run stable vectors in the system of pork retail prices. The associated VECM helps describe the causality between markets, and determinants of price fluctuations and information transmission through impulse analysis and forecast error variance decomposition. The statistical findings are discussed in the third part. The results suggest that in the long run, hog markets are better integrated in the South of Vietnam, than in the North. In the short run, prices are primarily driven by the demand in the North, and by supply in the South. The economic interpretation of results are given in terms of the growing demand for pork, and the structure of pork production costs. However, some inconsistencies in the results can at least be partially explained by considering market institutions. Using field data, we show that in the North, long term contracts and private institutions contribute to market segmentation. Whereas, in the South, public institutions foster market integration.
‘It’s what Emmeline Pankhurst would have wanted’: Celebrity Big Brother: Year of the Woman (2018, UK) and negotiations of popular feminism(s)

ABSTRACT Feminist scholarship has invested attention in popular culture as a terrain upon which understandings of feminism are circulated, contested and explored. This is particularly so in the contemporary moment in which feminism appears to have achieved a new ascendency. But whilst popular culture and feminism are recognised as inextricably enmeshed, there remains the implicit or explicit assumption in feminist scholarship that popular media culture could do ‘better’, and that there is a more ‘authentic’ form of feminism waiting to find representation. In response to this context, this article undertakes an analysis of Twitter responses to Celebrity Big Brother: Year of the Woman (2018) in order to explore the ways in which a popular media text provides an arena for the negotiation of popular feminism. Rather than positioning reality TV and celebrity culture as a site of ‘ideological ruin’ for feminism, this article explores how CBB is discussed in relation to feminism as popular television, and the ways in which this may offer affordances and limitations. The article concludes that feminist media scholars need to give due attention to the complexities of popular feminism as articulated by popular media culture.
Worldview commitment and narrative foreclosure among older Dutch adults: Assessing the importance of grand narratives

ABSTRACT Grand narratives offered by religion and other worldviews provide a background against which people can narrate their personal life stories. Therefore, the extent of commitment older adults experience toward their worldview is expected to influence the narrative openness of their life story. Regression analyses based on a survey study among 356 older Dutch adults demonstrate that reconsideration of the commitment toward their worldview is associated with “narrative foreclosure”: the premature sentiment that their life story is actually over. Moreover, the association we found between age and narrative foreclosure toward the future emphasizes the lack of vital cultural narratives of aging.
Symbol error probability analysis of a multiuser detector for M-PSK signals based on successive cancellation

A narrow-band multiuser receiver based on successive signal detection and subtraction, is considered. The symbol error probability (SEP) for M-PSK modulated signals is evaluated and analytical approximations for the SEP of the individual signals are presented and compared with results obtained from simulations. For geometrically related signal amplitudes, a constant minimum distance can be guaranteed independent of the number of signals. The required amplitude ratio is shown to be related to M and the number of co-channel signals. Optimizing the transmitted power for the different signals while ensuring the same SEP is then addressed and closed-form expressions of the signal amplitude ratios are derived. The effect of inaccurately estimated signal parameters due to noise is also analyzed. SEP results are presented for synchronous signals in an additive white Gaussian noise environment.
Challenges in Providing Automatic Affective Feedback in Instant Messaging Applications

Instant messaging is one of the major channels of computer mediated communication. However, humans are known to be very limited in understanding others' emotions via text-based communication. Aiming on introducing emotion sensing technologies to instant messaging, we developed EmotionPush, a system that automatically detects the emotions of the messages end-users received on Facebook Messenger and provides colored cues on their smartphones accordingly. We conducted a deployment study with 20 participants during a time span of two weeks. In this paper, we revealed five challenges, along with examples, that we observed in our study based on both user's feedback and chat logs, including (i)the continuum of emotions, (ii)multi-user conversations, (iii)different dynamics between different users, (iv)misclassification of emotions and (v)unconventional content. We believe this discussion will benefit the future exploration of affective computing for instant messaging, and also shed light on research of conversational emotion sensing.
A Rapid Method for Combined Laser Scanning Confocal Microscopic and Electron Microscopic Visualization of Biocytin or Neurobiotin-labeled Neurons

Intracellular recording and dye filling are widely used to correlate the morphology of a neuron with its physiology. With laser scanning confocal microscopy, the complex shapes of labeled neurons in three dimensions can be reconstructed rapidly, but this requires fluorescent dyes. These dyes are neither permanent nor electron dense and therefore do not allow investigation by electron microscopy. Here we report a technique that quickly and easily converts a fluorescent label into a more stable and electron-dense stain. With this technique, a neuron is filled with Neurobiotin or biocytin, reacted with fluorophore-conjugated avidin, and imaged by confocal microscopy. To permit long-term storage or EM study, the fluorescent label is then converted to a stable electron-dense material by a single-step conversion using a commercially available ABC kit. We find that the method, which apparently relies on recognition of avidin's excess biotin binding sites by the biotin–peroxidase conjugate, is both faster and less labor intensive than photo-oxidation procedures in common use. The technique is readily adaptable to immunocytochemistry with biotinylated probes, as we demonstrate using anti-serotonin as an example.
Modeling the role of arterial windkessel in the enhancement and synchronization of low frequency vasomotor activity

Arterial windkessel mechanisms and arterial pressure (AP) low frequency (LF) waves were investigated by means of simple lumped models of a compliant resistant/arterial tree and of flow regulation in peripheral vascular districts (PVDs) with three types of feedback: J) delay, 2) Van der Pol oscillator, 3) relay; all were able to actively compensate flow changes and to simulate peripheral LF vasomotion. Each PVD connected to a Windkessel compartment displayed a reduction and a disappearance of oscillations with low compliance, when the Windkessel equivalent time constant T/sub eq/ fall below 2s. Two PVDs connected to the same Windkessel tended to phase opposition with a negative interference canceling their LF oscillations from AP. With a modest neural drive, cancellation was imperfect and AP waves appeared. Vasomotion, arterial compliances and neural triggers are all essential informing LF AP variability.
A Comparison of Electricity Generation System Sustainability among G20 Countries

Planning for electricity generation systems is a very important task and should take environmental and economic factors into account. This paper reviews the existing metrics and methods in evaluating energy sustainability, and we propose a sustainability assessment index system. The input indexes include generation capacity, generation cost, and land use. The output indexes include desirable and undesirable parts. The desirable outputs are total electricity generation and job creation. The undesirable outputs are external supply risk and external costs associated with the environment and health. The super-efficiency data envelopment analysis method is used to calculate the sustainability of electricity generation systems of 23 countries from 2005 to 2014. The three input indexes and three undesirable output indexes are used as the input variables. The two desirable outputs are used as the output variables. The results show that most countries’ electricity generation sustainability values have decreasing trends. In addition, nuclear and hydro generation have positive effects. Solar, wind, and fossil fuel generation have negative effects on sustainability.
EXPERIENCES WITH TERRESTRIAL LASER SCANNER MODELLING AND ACCURACY ASSESSMENT

This paper presents advances in terrestrial laser scanner systematic error modelling and self-calibration. The error model consists of 19 additional parameters grouped into two categories: physical and empirical. The physical group comprises known error sources such as rangefinder offset, cyclic errors, collimation axis error, trunnion axis error, vertical circle index error and others. The empirical terms lack ready physical explanation but nonetheless model significant errors. Results from self-calibration and an accuracy assessment experiment respectively show that up to 80% and 36% improvement was achieved as a result of using the correction models. Four other experiments designed to examine the influence of manufacturer’s correction parameters and centroid software as well as instrument biases are also described. The results from these tests reveal significant biases in some cases.
Hybrid Heterogeneous Transfer Learning through Deep Learning

Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precisely due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods.
A CLASSICAL ALGORITHM FOR AVOIDING LOCAL MINIMA

Conventional methods of supervised learning are inevitably faced with the problem of local minima; evidence is presented that conjugate gradient and quasi-Newton techniques are particularly susceptible to being trapped in sub-optimal solutions. A new classical technique is presented which by the use of a homotopy on the range of the target outputs allows supervised learning methods to find a global minimum of the error function in almost every case.
Noise Adaptive Image Restoration: A Minimum Mean Square Error Approach

This paper introduces a novel minimum mean square restoration approach with noise adaptive regularization for linear, space invariant known blur. The restoration filter is designed to de-blur the image while minimizing the noise in the image. The adaptive noise reducing characteristic of the filter is proved analytically. It has been substantiated with empirical study that this restoration approach outperforms state of the art restoration approaches. The proposed method has two major advantage: i) It de-noises the image based on the characteristic of the individual image, ii) It has simple, non-iterative implementation.
Analysis of serial ovarian volume measurements and incidence of ovarian cancer: implications for pathogenesis.

BACKGROUND Accumulating evidence suggests that many ovarian cancers represent metastases from occult fallopian tube carcinomas or tumors arising within ovarian endometriosis. We hypothesized that small ovarian volumes, as reflected in nonvisualization by transvaginal ultrasound (TVU), would be a marker of lower ovarian cancer risk, whereas enlargement on serial examinations would indicate higher risk. METHODS To address these hypotheses, we analyzed serial ovarian volume measurements determined by TVU for 29321 women (102787 scans) performed in the ovarian cancer screening arm of the Prostate, Lung, Colorectal, and Ovarian (PLCO) trial. Cox models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs) as measures of association between TVU results and ovarian cancer. We assessed whether increasing ovarian volume preceded diagnosis of ovarian cancer in a nested case-control analysis comparing case patients and control patients matched on age, center, and screening year (1:4 ratio). All statistical tests were two-sided. RESULTS Visualization of normal-appearing ovaries at the last TVU scan was associated with marginally higher ovarian cancer risk compared with nonvisualization of the ovaries (HR = 1.42, 95% CI = 1.00 to 2.01). Ovarian volume increased statistically significantly in ovarian cancer case patients one to two years before diagnosis (P < .001), but not in matched control patients. CONCLUSION Our analysis of TVU data suggests that increasing ovarian volume is associated with greater ovarian cancer risk, but it is only detectable one to two years before diagnosis.
Climate of the Past On the gas-ice depth difference ( 1 depth ) along the EPICA Dome C ice core

We compare a variety of methods for estimating the gas/ice depth offset ( 1depth) at EPICA Dome C (EDC, East Antarctica). (1) Purely based on modelling efforts, 1depth can be estimated combining a firn densification with an ice flow model. (2) The diffusive column height can be estimated fromδ15N and converted to1depth using an ice flow model and assumptions about past average firn density and thickness of the convective zone. (3) Ice and gas synchronisation of the EDC ice core to the GRIP, EDML and TALDICE ice cores shifts the ice/gas offset problem into higher accumulation ice cores where it can be more accurately evaluated. (4) Finally, the bipolar seesaw hypothesis allows us to synchronise the ice isotopic record with the gas CH4 record, the later being taken as a proxy of Greenland temperature. The general agreement of method 4 with methods 2 and 3 confirms that the bipolar seesaw antiphase happened during the last 140 kyr. Applying method 4 to the deeper section of the EDC core confirms that the ice flow is complex and can help to improve our reconstruction of the thinning function and thus, of the EDC age scale. We confirm that method 1 overestimates the glacial 1depth at EDC and we suggest that it is due to an overestimation of the glacial lock-in depth (LID) by the firn densification model. In contrast, we find that method 1 very likely underestimates1depth during Termination II, due either to an underestimated thinning function or to an underestimated LID. Finally, method 2 gives estimates within a few metres of methods 3 and 4 during the last deglacial warming, suggesting that the convective zone at Dome C cannot have been very large at this time, if it existed at all.
The Process Specification Language: Theory and Applications

As the use of information technology in manufacturing operations has matured, the need to integrate software applications has become increasingly important. However, interoperability among these manufacturing applications is hindered because the applications use different terminology and representations of the domain. These problems arise most acutely for systems that must manage the heterogeneity inherent in various domains and integrate models of different domains into coherent frameworks (Figure 1). For example, such integration occurs in business process reengineering, where enterprise models integrate processes, organizations, goals and customers. Even when applications use the same terminology, they often associate different semantics with the terms. This clash over the meaning of the terms prevents the seamless exchange of information among the applications. Typically, point-to-point translation programs are written to enable communication from one specific application to another. However, as the number of applications has increased and the information has become more complex, it has been more difficult for software developers to provide translators between every pair of applications that must cooperate. What is needed is some way of explicitly specifying the terminology of the applications in an unambiguous fashion.
Bmi-1 Promotes Glioma Angiogenesis by Activating NF-κB Signaling

Angiogenesis in glioma is associated with the poor prognosis of the disease and closely correlates with the highly invasive phenotype of glioma cells, which represents the most challenging impediment against the currently glioma treatments. Bmi-1, an onco-protein, has been implicated in the progression of various human cancers, including gliomas, whereas its role in glioma angiogenesis remains unclear. Our current study examined the effects of Bmi-1 on glioma angiogenesis in vitro as well as in vivo. We found that overexpression of Bmi-1 enhanced, whereas knockdown of Bmi-1 diminished, the capability of glioma cells to induce tubule formation and migration of endothelial cells and neovascularization in chicken chorioallantoic membrane. In vivo, Bmi-1 overexpression and knockdown, respectively, promoted and inhibited angiogenesis in orthotopically transplanted human gliomas. Furthermore, NF-κB activity and VEGF-C expression was induced by Bmi-1 overexpression, whereas Bmi-1 knockdown attenuated NF-κB signaling and decreased VEGF-C expression. Additionally suppression of NF-κB activity using a specific chemical inhibitor abrogated the NF-κB activation and the pro-angiogenic activities of glioma cells. Together, our data suggest that Bmi-1 plays an important role in glioma angiogenesis and therefore could represent a potential target for anti-angiogenic therapy against the disease.
Chained in-order/out-of-order doublecore architecture

Complexity is one of the most important problems facing microarchitects. It is exacerbated by the application of optimizations, by scaling to higher issue widths and, in general, by increasing the size of microprocessor structures. This paper presents a new microarchitecture, the chained in-order/out-of-order doublecore architecture (CIO2), designed to attack the problems of complexity and energy. The CIO2 architecture reorganizes the microarchitecture using the concepts of a centralized register file and the future file. The resulting architecture decouples that program state from the execution units. The simplicity of the architecture enables the implementation of three optimizations with little effort: register file banking, writeback filtering and instruction pre-execution. These optimizations allow a reduction of up to 75% in register file energy consumption. Instruction pre-execution further allows around 40% of all integer instructions to execute in the in-order front-end, considerably reducing the activity of the power-hungry issue queues in the out-of-order back-end. Moreover, these improvements are achieved with a negligible performance loss.
A sparse regularized model for Raman spectral analysis

Raman spectroscopy has for a long time performed as a common analytical technique in spectroscopic applications. A Raman spectrum depends upon how efficiently a molecule scatters the incident light (electron rich molecules often produce strong signals) which results in difficulties for relating the spectrum to the absolute amounts of present substances. The spectrum is however a stable and accurate representation of the sample measured especially considering that each molecule is associated with a unique spectrum. State-of-the-art spectroscopic calibration methods include the principal component regression (PCR) and partial least squares regression (PLSR) methods which have been proved to be efficient regression methods to realise the quantitative analysis of Raman spectrum. In this paper we consider the problem of Raman spectra deconvolution to analyse the sample composition, as well as possible unknown substances. In particular, we propose a sparse regularized model as a complement to traditional regression methods by leveraging the components sparsity compared to the whole chemical library and the spectra sparsity, given that the chemical fingerprint of each spectrum is mainly determined by the peaks. Experimental results illustrate the effectiveness of this sparse regularized model.
On the relationship between distributed group-behaviour and the behavioural complexity of individuals

Learning distributed behaviour by reinforcement can suffer from problems of credit assignment exploitative individuals disrupt the stability of large groups and impose a ceiling on the complexity of free-form distributed behaviour. Where small groups are compartmentalised in some way, such that members are (semi-) permanently associated with the group, exploitation will be ultimately self-defeating and thus self-regulating. In its most concrete form, a permanently compartmentatlised group is effectively a new individual for the purposes of credit assignment. The issues of parasites and compartmentation are well documented in the biological literature on group selection (eg [Maynard Smith 1976], [Wilson & Sober 1994]) and the encapsulation of communities into new species, the process of symbiogenesis, is now familiar in evolutionary theory (eg. the evolution of eukaryotic cells, Margulis [1993]). This paper describes a model which shows how the occurrence of mutually beneficial groups alters the shape of the reward landscape for member individuals. This new search space enables a single individual to learn the behaviour formerly exhibited by the group. Such new individuals are then stable enough to form groups at a higher-level, and the successive re-application of this principle may provide a mechanism for the scaling-up of complexity in distributed behaviour.