Miracle-dz/UniFAR-FTUs huggingface dataset:
Wage Inequality, Increased Competition, and Trade Liberalization: Short Run vs Long Run

This paper examines the impact of increased competition and trade liberalization on skilled–unskilled wage inequality in the short run as well as the long run. It is shown that an increase in the number of firms in the producer services sector increases wage inequality in the short run even if the income shares of capital in the industrial and agricultural sectors were identical. A decrease in the services sector's fixed cost decreases wage inequality in the short run if the income share of capital in the agricultural sector is relatively large. Owing to the presence of external economies, a decrease in the services sector's fixed cost increases wage inequality in the long run. A decrease in import duty on the agricultural good increases wage inequality in the short as well as the long run but its effect in the long run is stronger due to the presence of external economies in the industrial sector.
Debt and Macro Stability

A dynamic Keynes/Kalecki macro model with debt and capital accumulation is used to explore the issue of financial instability. It is argued that market systems may reasonably be described as having stable and unstable regions that coexist. The proximity of these regions can be taken as a measure of financial fragility. The degree of fragility is shown to depend on expectational, distributional, and interest-rate factors. Thus, ideas of H. Minsky are integrated with work that emphasizes the importance of profitability.
Controllable Text Generation

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efﬁcient collaborative learning of generator and discriminators, our model learns highly inter-pretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.
Uniform Upper Bound for a Stable Measure of a Small Ball

P. Hitczenko, S.Kwapien, W.N. Li, G.Schechtman, T.Schlumprecht and J.Zinn stated the following conjecture. Let $\mu$ be a symmetric $\alpha$-stable measure on a separable Banach space and $B$ a centered ball such that $\mu(B)\le b$. Then there exists a constant $R(b)$, depending only on $b$, such that $\mu(tB)\le R(b)t\mu(B)$ for all $0 < t < 1$. We prove that the above inequality holds but the constant $R$ must depend also on $\alpha$.
Give Me Strong Moments and Time: Combining GMM and SMM to Estimate the Long-Run Risk Asset Pricing Model

The long-run consumption risk (LRR) model is a convincing approach towards resolving prominent asset pricing puzzles. Whilst the simulated method of moments (SMM) provides a natural framework to estimate its deep parameters, caveats concern model solubility and weak identification. We propose a two-step estimation strategy that combines GMM and SMM, and for which we elicit informative moment matches from the LRR model structure. In particular, we exploit the persistent serial correlation of consumption and the equilibrium conditions for market return and risk-free rate, as well as the model-implied predictability of the risk-free rate. We match analytical moments when possible and simulated moments when necessary and determine the crucial factors that are required for identification and reasonable estimation precision. By means of a simulation study---the first in the context of long-run risk modeling---we delineate the pitfalls associated with SMM estimation of LRR models, and we present a blueprint for successful estimation.
Cellular organization of cortical barrel columns is whisker-specific

Significance Cortical columns are thought to be the elementary functional building blocks of sensory cortices. Here we show that the cellular architecture of cortical “barrel” columns in rodent somatosensory cortex is not stereotypic, but specific for each whisker on the animals’ snout. Our findings challenge the concepts underlying contemporary simulation efforts that build up large-scale network models of repeatedly occurring identical cortical circuits. The cellular organization of the cortex is of fundamental importance for elucidating the structural principles that underlie its functions. It has been suggested that reconstructing the structure and synaptic wiring of the elementary functional building block of mammalian cortices, the cortical column, might suffice to reverse engineer and simulate the functions of entire cortices. In the vibrissal area of rodent somatosensory cortex, whisker-related “barrel” columns have been referred to as potential cytoarchitectonic equivalents of functional cortical columns. Here, we investigated the structural stereotypy of cortical barrel columns by measuring the 3D neuronal composition of the entire vibrissal area in rat somatosensory cortex and thalamus. We found that the number of neurons per cortical barrel column and thalamic “barreloid” varied substantially within individual animals, increasing by ∼2.5-fold from dorsal to ventral whiskers. As a result, the ratio between whisker-specific thalamic and cortical neurons was remarkably constant. Thus, we hypothesize that the cellular architecture of sensory cortices reflects the degree of similarity in sensory input and not columnar and/or cortical uniformity principles.
Hydrological Response of East China to the Variation of East Asian Summer Monsoon

The sensitivity of hydrologic variables in East China, that is, runoff, precipitation, evapotranspiration, and soil moisture to the fluctuation of East Asian summer monsoon (EASM), is evaluated by the Mann-Kendall correlation analysis on a spatial resolution of 1/4° in the period of 1952–2012. The results indicate remarkable spatial disparities in the correlation between the hydrologic variables and EASM. The regions in East China susceptible to hydrological change due to EASM fluctuation are identified. When the standardized anomaly of intensity index of EASM (EASMI) is above 1.00, the runoff of Haihe basin has increased by 49% on average, especially in the suburb of Beijing and Hebei province where the runoff has increased up to 105%. In contrast, the runoff in the basins of Haihe and Yellow River has decreased by about 27% and 17%, respectively, when the standardized anomaly of EASMI is below −1.00, which has brought severe drought to the areas since mid-1970s. The study can be beneficial for national or watershed agencies developing adaptive water management strategies in the face of global climate change.
The effects of a community-based partnership, Project Access Dallas (PAD), on emergency department utilization and costs among the uninsured.

BACKGROUND Approximately 19% of non-elderly adults are without health insurance. The uninsured frequently lack a source of primary care and are more likely to use the emergency department (ED) for routine care. Improving access to primary care for the uninsured is one strategy to reduce ED overutilization and related costs. METHODS A comparison group quasi-experimental design was used to evaluate a broad-based community partnership that provided access to care for the uninsured-Project Access Dallas (PAD)-on ED utilization and related costs. Eligible uninsured patients seen in the ED were enrolled in PAD (n = 265) with similar patients not enrolled in PAD (n = 309) serving as controls. Study patients were aged 18-65 years, <200% of the federal poverty level and uninsured. Outcome measures include the number of ED visits, hospital days and direct and indirect costs. RESULTS PAD program enrollees had significantly fewer ED visits (0.93 vs. 1.44; P < 0.01) and fewer inpatient hospital days (0.37 vs. 1.07; P < 0.05) than controls. Direct hospital costs were ∼60% less ($1188 vs. $446; P < 0.01) and indirect costs were 50% less ($313 vs. $692; P < 0.01). CONCLUSIONS A broad-based community partnership program can significantly reduce ED utilization and related costs among the uninsured.
The C-LINK System for Collaborative Web Usage: A Real-World Deployment in Rural Nicaragua

Information exchange is one of the most crucial elements of education and business in the modern world. Therefore, equipping developing regions with access to the internet is becoming increasingly important. Though many regions will not receive broadband, wired connections in the near future, limited connectivity is rapidly become available. The connectivity may come from the increasing density of mobile devices or from novel delay-tolerant approaches. In this paper we demonstrate a collaborative caching system we have developed that allows for such connectivity. Further, we report on our experiences deploying our system in rural Nicaragua. We show that collaborative caching allows miss rates to drop in half through the course of the deployment, and that user satisfaction remains high despite long round trip times. Although general web access is sometimes viewed as not amenable to collaborative caching and prefetching, our data shows value in collaboration, with 74% of pages accessed by more than one user.
On Simulation of e-Learning Convergence Time Using Artificial Neural Networks

This paper addresses a challenging and critical issue of how to evaluate dynamically e-Learners' performance. That is by considering their learning response (convergence) time to reach desired output answer. More precisely, this piece of research aims to investigate realistically performance of eeducational phenomenon associated with e-learners' brain response time. Moreover, presented work integrates neuronal sciences and computer technology into e-educational environment as an interdisciplinary research direction to study realistically e-learning performance issue. Accordingly, Artificial Neural Networks (ANN) models are suggested for realistic performance evaluation of timely dependant response till reaching learning process convergence. So, response time is adopted as an appropriate candidate parameter for e-learning systems' evaluation. Herein, analysis of response time parameter considers individual learners' differences while performing learning process. Additionally, evaluated effect of contributing neurons on e-learning processes performance is presented. Finally, after running of suggested realistic simulation programs, some interesting results are introduced.
Predicting Personality Traits using Multimodal Information

Measuring personality traits has a long story in psychology where analysis has been done by asking sets of questions. These question sets (inventories) have been designed by investigating lexical terms that we use in our daily communications or by analyzing biological phenomena. Whether consciously or unconsciously we express our thoughts and behaviors when communicating with others, either verbally, non-verbally or using visual expressions. Recently, research in behavioral signal processing has focused on automatically measuring personality traits using different behavioral cues that appear in our daily communication. In this study, we present an approach to automatically recognize personality traits using a video-blog (vlog) corpus, consisting of transcription and extracted audio-visual features. We analyzed linguistic, psycholinguistic and emotional features in addition to the audio-visual features provided with the dataset. We also studied whether we can better predict a trait by identifying other traits. Using our best models we obtained very promising results compared to the official baseline.
Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a Bayesian probabilistic framework

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). Although US has many advantages over others, tracked US for Orthopedic Surgery has been researched by only a few authors. An important factor limiting the accuracy of tracked US to CT registration (1-3mm) has been the difficulty in determining the exact location of the bone surfaces in the US images (the response could range from 2-4mm). Thus it is crucial to localize the bone surface accurately from these images. Moreover conventional US imaging systems are known to have certain inherent inaccuracies, mainly due to the fact that the imaging model is assumed planar. This creates the need to develop a bone segmentation framework that can couple information from various post-processed spatially separated US images (of the bone) to enhance the localization of the bone surface. In this paper we discuss the various reasons that cause inherent uncertainties in the bone surface localization (in B-mode US images) and suggest methods to account for these. We also develop a method for automatic bone surface detection. To do so, we account objectively for the high-level understanding of the various bone surface features visible in typical US images. A combination of these features would finally decide the surface position. We use a Bayesian probabilistic framework, which strikes a fair balance between high level understanding from features in an image and the low level number crunching of standard image processing techniques. It also provides us with a mathematical approach that facilitates combining multiple images to augment the bone surface estimate.
How the Australian media cover humanitarian issues

This article reports on the quantitative analysis of a large-scale study of the coverage of aid and humanitarian issues in the Australian media in 1999. It should be read in conjunction with the qualitative analysis (forthcoming in AJR). The analysis indicates there are large variations in the amount and type of information on these issues available to Australian audiences, depending on their geographical location, print/television/radio and public/private media preferences. For some audiences, including private sector outlets in all media outside the SydneyCanberra-Melbourne media reach, extremely limited information is available on these issues. There are marked differences between mastheads/sections/programs within particular media outlets, and between print/television/radio media. Across all media there is a tendency for saturation coverage ofa small number ofissues, most notably East Timor in the period of our study, and the exclusion of coverage of a broader range of issues, events and regions. Coverage tends to be organised around themes of conflict and violence, to tocus on certain international regions and to ignore or underplay others, to be sourced from international media outlets and to favour spokespeople notfrom the people most directly affected by the issues being reported.
Estrogen Rather Than Progesterone Cause Constipation in Both Female and Male Mice

Females are more often affected by constipation than males, especially during pregnancy, which is related to the menstrual cycle. Although still controversial, alterations of progesterone and estrogen may be responsible. Therefore, this study was conducted in order to determine whether the female sex steroid hormone itself is responsible for development of constipation in both female and male mice. Administration of estrogen resulted in a decrease in weight of accumulated feces on days 2, 3, 4, and 5 in male mice and on day 5 in female mice, compared with the control group, but progesterone administration did not. Administration of estrogen resulted in a decrease in gastrointestinal movement, compared to normal; however, no significant change was observed by administration of progesterone. In conclusion, estrogen, rather than progesterone, may be a detrimental factor of constipation via decreased bowel movement in mice.
ONLY Margolis — The Conceptual Mind El 19 All Concepts Are Ad Hoc Concepts

To explain how people think and communicate, cognitive scientists posit a repository of concepts, categories, and word meanings that are stable across time and shared across individuals. But if concepts are stable, how can people use them so flexibly? Here we explore a possible answer: maybe this stability is an illusion. Perhaps all concepts , categories, and word meanings (CC&Ms) are constructed ad hoc, each time we use them. On this proposal, which we call the ad hoc cognition (AHC) framework, all words are infinitely polysemous, all communication is " good enough, " and no idea is ever the same twice. The details of people's ad hoc CC&Ms are determined by the way retrieval cues (such as words) interact with the physical, social, and linguistic context. Commonalities across instantiations of CC&Ms yield some emergent stability and create the illusion of context-independent core properties. Here we argue that even the most stable-seeming CC&Ms are instantiated via the same processes as those that are more obviously ad hoc, and they vary (a) from one microsecond to the next within a given instantiation, (b) from one instantiation to the next within an individual , and (c) from person to person and group to group as a function of people's experiential history. If this is true, then a central goal of research on language and cognition should be to elucidate the fleeting, idiosyncratic neurocognitive representations that people actually use for thinking and communicating, rather than to discern the nature and origin of context-independent CC&Ms, which, we argue, only exist as theoretical abstractions. Thinking depends on brains, and brains are always changing ; therefore thoughts are always changing. Rather than trying to explain concepts, categories, and word meanings as things that we have in our minds, like entries in a mental dictionary or mental encyclopedia, it may be more fruitful to build theories of conceptualizing, categorizing, and constructing word meanings: things that we do with our minds.
Paraventricular nucleus of the human hypothalamus in primary hypertension: Activation of corticotropin‐releasing hormone neurons

By using quantitative immunohistochemical and in situ hybridization techniques, we studied corticotropin‐releasing hormone (CRH) ‐producing neurons of the hypothalamic paraventricular nucleus (PVN) in patients who suffered from primary hypertension and died due to acute cardiac failure. The control group consisted of individuals who had normal blood pressure and died of acute heart failure due to mechanical trauma. Both magno‐ and parvocellular populations of CRH neurons appeared to be more numerous in the PVN of hypertensive patients. Quantitative analysis showed approximately a twofold increase in the total number of CRH neurons and a more than fivefold increase in the amount of CRH mRNA in the hypertensive PVN compared with the control. It is suggested that synthesis of CRH in hypertensive PVN is enhanced. Increased activity of CRH‐producing neurons in the PVN of hypertensive patients is proposed not only to entail hyperactivity of the hypothalamo‐pituitary‐adrenal axis, but also of the sympathetic nervous system and, thus, to be involved in the pathogenesis of hypertension. J. Comp. Neurol. 443:321–331, 2002. © 2002 Wiley‐Liss, Inc.
Comprehensive Automated 3D Urban Environment Modelling Using Terrestrial Laser Scanning Point Cloud

In this paper we present a novel street scene modelling framework, which takes advantage of 3D point cloud captured by a high definition LiDAR laser scanner. We propose an automatic and robust approach to detect, segment and classify urban objects from point clouds hence reconstructing a comprehensive 3D urban environment model. Our system first automatically segments grounds point cloud. Then building facades will be detected by using binary range image processing. Remained point cloud will be grouped into voxels and subsequently transformed into super voxels. Local 3D features are extracted from super voxels and classified by trained boosted decision trees with semantic classes e.g. tree, pedestrian, and car. Given labeled point cloud the proposed algorithm reconstructs the realistic model in two phases. Firstly building facades will be rendered by ShadVis algorithm. In the second step we apply a novel and fast method for fitting the solid predefined template mesh models to non-building labeled point cloud. The proposed method is evaluated both quantitatively and qualitatively on a challenging TLS NAVTEQ True databases.
Input Trade Liberalisation and Wage-Inequality with Non-Traded Goods

This paper develops a multi-sector full-employment general equilibrium model with internationally non-traded goods and international fragmentation in skill-intensive production, to understand the mechanism how trade-induced growth in the skill-intensive sector is mediated to informal sector wages and employment through the existence of finished non-tradable and the corresponding domestic demand-supply forces. The underlying developing economy is characterised by dual unskilled labour market with unionised formal and non-unionised informal sectors, consistent with the empirical literature on developing economies India. Numerical analysis has also been performed to simulate how the changes in elasticities of factor substitution in production in different sectors account for the movement in informal wage and therefore the movement in skilled–unskilled wage gap. This paper challenges the view that the relative wage-inequality in a DC like India with rigid organised sector labour market has unequivocally been governed only by the increase in the skilled wages. An extension with involuntary unemployment of skilled labour using the fair wage hypothesis has also been presented that effectively demonstrates the robustness of the results obtained under full-employment model.
Experimental Tests of the Separation Theorem and the Capital Asset Pricing Model

A computer-controlled portfolio selection task with three risky assets and either with or without a riskless asset was devised to test experimentally assumptions underlying the separation theorem and the capital asset pricing model. Two differently paid groups of subjects completed individually up to 300 portfolio selection problems. Although most of the subjects diversified among the risky assets, the introduction of a riskless asset did not have the effect predicted by the separation theorem, nor were the subjects affected by systematic changes in the variance-covariance matrix governing the risky returns. However, performance improved as the reward was increased tenfold. Copyright 1988 by American Economic Association.
Affective Video Classification Based on Spatio-temporal Feature Fusion

In this paper, we propose a novel affective video classification method based on facial expression recognition by learning the spatio-temporal feature fusion of actors' and viewers' facial expressions. For spatial features, we integrate Haar-like features into compositional ones according to the features' correlation, and train a mid classifier during the period. Then this process is embedded into improved AdaBoost learning algorithm to obtain spatial features. And for temporal feature fusion, we adopt hidden dynamic conditional random fields (HDCRFs) based on HCRFs by introducing time dimension variable. Finally spatial features are embedded into HDCRFs to recognize facial expressions. Experiments on the well-known Cohn-Kanada database show that the proposed method has a promising recognition performance. And affective classification experimental results on our own videos show that most subjects are satisfied with the classification results.