Public Capital Spillovers and Growth: A Foray Downunder

We extend the deterministic growth model of Glomm and Ravikumar (1994) to a stochastic endogenous growth model which nests both exogenous and endogenous growth factors. By introducing simple shocks to production technology, private capital and public capital investment, we can derive testable time series properties of the analytical model. The hypothesis of strict endogenous growth due to public capital spillovers cannot be statistically rejected for our Australian data set. We find further short-run evidence of public capital contributing to permanent increases in the levels of per capita income and private capital.

Aluminum alters NMDA receptor 1A and 2A/B expression on neonatal hippocampal neurons in rats

<p>Abstract</p> <p>Background</p> <p>High aluminum (Al) content in certain infant formula raises the concern of possible Al toxicity on brain development of neonates during their vulnerable period of growing. Results of in vivo study showed that Al content of brain tissues reached to 74 μM when oral intake up to 1110 μM, 10 times of that in the hi-Al infant formula.</p> <p>Methods</p> <p>Utilizing a cultured neuron cells in vitro model, we have assessed Al influence on neuronal specific gene expression alteration by immunoblot and immunohistochemistry and neural proliferation rate changes by MTT assay.</p> <p>Results</p> <p>Microscopic images showed that the neurite outgrowth of hippocampal neurons increased along with the Al dosages (37, 74 μM Al (AlCl₃)). MTT results also indicated that Al increased neural cell viability. On the other hand, the immunocytochemistry staining suggested that the protein expressions of NMDAR 1A and NMDAR 2A/B decreased with the Al dosages (p < 0.05).</p> <p>Conclusion</p> <p>Treated hippocampal neurons with 37 and 74 μM of Al for 14 days increased neural cell viability, but hampered NMDAR 1A and NMDAR 2A/B expressions. It was suggested that Al exposure might alter the development of hippocampal neurons in neonatal rats.</p

Modulation of Serum Antinuclear Antibody Levels by Levamisole Treatment in Patients With Oral Lichen Planus

Background/PurposeSerum autoantibodies, including antinuclear antibodies (ANAs), have been found in patients with oral lichen planus (OLP). This study evaluated whether Taiwanese OLP patients had significantly higher frequencies of serum ANAs than healthy control subjects, and whether levamisole treatment could modulate the antibody levels.MethodsThis study used an indirect immunofluorescence technique to measure the baseline serum levels of ANA in a group of 583 Taiwanese OLP patients and 53 healthy control subjects. Seventy-nine ANA-positive OLP patients were treated with levamisole under a regular follow-up schedule in our dental clinic, and their serum ANA levels were measured after treatment.ResultsWe found that the frequencies of serum ANA in patients with OLP (23.2%), erosive OLP (EOLP, 23.8%), major EOLP (31.5%), and minor EOLP (18.1%) were all significantly higher than that (5.7%) in healthy control subjects. In addition, major EOLP patients had a significantly higher serum ANA positive rate than minor EOLP or non-erosive OLP patients. Of 135 ANA-positive OLP patients, 79 were treated with levamisole under a regular follow-up schedule. We found that treatment with levamisole for a period of 2–38 months (mean, 12 ± 9 months) effectively reduced the high mean serum ANA titer (557 ± 98) at baseline to an undetectable level (0) in all ANA-positive OLP patients, regardless of different high initial serum titers of ANA.ConclusionThere was a significantly higher frequency of serum ANA (23.2%) in Taiwanese OLP patients than in healthy control subjects. Treatment with levamisole for 2–38 months reduced the high serum ANA to an undetectable level, and significantly improved the signs and symptoms in all treated OLP patients

COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation

Personalized text generation has broad industrial applications, such as explanation generation for recommendations, conversational systems, etc. Personalized text generators are usually trained on user written text, e.g., reviews collected on e-commerce platforms. However, due to historical, social, or behavioral reasons, there may exist bias that associates certain linguistic quality of user written text with the users' protected attributes such as gender, race, etc. The generators can identify and inherit these correlations and generate texts discriminately w.r.t. the users' protected attributes. Without proper intervention, such bias can adversarially influence the users' trust and reliance on the system. From a broader perspective, bias in auto-generated contents can reinforce the social stereotypes about how online users write through interactions with the users. In this work, we investigate the fairness of personalized text generation in the setting of explainable recommendation. We develop a general framework for achieving measure-specific counterfactual fairness on the linguistic quality of personalized explanations. We propose learning disentangled representations for counterfactual inference and develop a novel policy learning algorithm with carefully designed rewards for fairness optimization. The framework can be applied for achieving fairness on any given specifications of linguistic quality measures, and can be adapted to most of existing models and real-world settings. Extensive experiments demonstrate the superior ability of our method in achieving fairness while maintaining high generation performance

NormBank: A Knowledge Bank of Situational Social Norms

We present NormBank, a knowledge bank of 155k situational norms. This resource is designed to ground flexible normative reasoning for interactive, assistive, and collaborative AI systems. Unlike prior commonsense resources, NormBank grounds each inference within a multivalent sociocultural frame, which includes the setting (e.g., restaurant), the agents' contingent roles (waiter, customer), their attributes (age, gender), and other physical, social, and cultural constraints (e.g., the temperature or the country of operation). In total, NormBank contains 63k unique constraints from a taxonomy that we introduce and iteratively refine here. Constraints then apply in different combinations to frame social norms. Under these manipulations, norms are non-monotonic - one can cancel an inference by updating its frame even slightly. Still, we find evidence that neural models can help reliably extend the scope and coverage of NormBank. We further demonstrate the utility of this resource with a series of transfer experiments

Newcomers’ relationship-building behavior, mentor information sharing and newcomer adjustment: The moderating effects of perceived mentor and newcomer deep similarity

Drawing on similarity-attraction theory, we propose that relationship-building behaviors from newcomers are more positively related to information-sharing behaviors from mentors when they perceive a deep similarity with the newcomers, and that mentors' information sharing is likely to be well received by newcomers when they perceive a deep similarity with their mentors. We also hypothesize that newcomers' perceived mentor information sharing is positively associated with newcomer adjustment (i.e., role clarity and job performance). A time-lagged study with a total of 99 newcomers and their mentors was conducted within three months of newcomers entering the company. The results support our hypotheses, suggesting that perceived deep similarity is a key factor that associates with the effectiveness of newcomers' proactivity and mentors' information sharing behavior in newcomer adjustment