Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model

A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their accuracy is limited. Recently, deep learning methods were proposed to predict stock return in terms of the multi-factor model. Although these methods perform quite well, they have significant disadvantages such as a lack of transparency and limitations in the interpretability of the prediction. It is thus difficult for institutional investors to use black-box-type machine learning techniques in actual investment practice because they should show accountability to their customers. Consequently, the solution we propose is based on LSTM with LRP. Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM. Then, we approximate and linearize the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent factor model. Finally, we perform an empirical analysis of the Japanese stock market and show that our recurrent model has better predictive capability than the traditional linear model and fully-connected deep learning methods.Comment: In AAAI-19 Workshop on Network Interpretability for Deep Learnin

Effect of a serum factor on IgE-mediated histamine release from whole blood.

IgE-mediated histamine release from whole blood was analyzed in 44 patients with bronchial asthma by observing maximum present release and dose-response curves of histamine release induced by anti-IgE and house dust extract. The maximum histamine release from whole blood induced by anti-IgE correlated with total serum IgE levels. There was a close correlation between allergen-induced release from whole blood and the serum levels of specific IgE antibodies. In the maximum histamine release from whole blood induced by both anti-IgE and allergen, the interaction with a serum factor was not clearly recognized. Effect of a serum factor was shown in the dose-response curves of anti-IgE-induced histamine release, but not in those of allergen-induced histamine release. The dose-response curves caused by anti-IgE showed that basophils from cases with a high serum IgE level require much more anti-IgE to produce maximum histamine release than basophils from cases with a low serum IgE level. The results showed that IgE molecules contained in the serum participate in anti-IgE-induced histamine release from whole blood.</p

Describing coseismic groundwater level rise using tank model in volcanic aquifers, Kumamoto, southern Japan

The change of groundwater levels after the 2016 Mw 7.0 Kumamoto crustal earthquake was evaluated using a simple conceptual hydrological model in an attempt to show the presence, intensity, and probable mechanism of water level rise observed in Kumamoto where a comprehensive observation-well network exists. A tank model was applied to verify 16 wells in the study field. In the model groundwater levels were first calibrated for the periods in ca. 2 years before the main shock using several hydrological parameters including precipitation, evapotranspiration, water recharge and discharge, and artificial recharge by irrigation. Water levels were then simulated by extrapolating this law of water fluctuating patterns for ca. 2.5 years after the main shock of the earthquake, without considering hydrogeological changes due to the earthquake. A difference in groundwater levels between observation and simulation results yields a degree of coseismic water level rises for each well. The coseismic abnormal water level increase was calculated to be ~11 m in 4?5 month after the main shock and was most significantly on the western slope of the Aso caldera rim mountains. The spatial distribution of the coseismic water increases clarified that the most dominate increasing anomalies prevail at mountain feet surrounding the plains, suggesting the occurrence of coseismic mountain water release resulting in the rise of water levels in downslope aquifers. Identified coseismic water level increases still continue up to 2.5 years after the earthquake, probably because changes in hydrogeological properties in mountain aquifers, i.e., permeability, are still sustained. Our forecasting water recovering trends require ca. 3.5?5 year after the earthquake for complete recovery to the original conditions. We demonstrated that our approaches are capable of describing coseismic water level changes and could potentially be applied to other fields

Novel COL5A2 mutation in Ehlers–Danlos syndrome

Ehlers–Danlos syndrome (EDS) is a group of inherited connective tissue disorders characterized by hyperextensible skin, joint hypermobility and soft tissue fragility. For molecular diagnosis, targeted exome sequencing was performed on a 9-year-old male patient who was clinically suspected to have EDS. The patient presented with progressive kyphoscoliosis, joint hypermobility and hyperextensible skin without scars. Ultimately, classical EDS was diagnosed by identifying a novel, mono-allelic mutation in COL5A2 [NM_000393.3(COL5A2_v001):c.682G>A, p.Gly228Arg]

Novel CHD7 mutation in CHARGE syndrome

CHARGE syndrome is a rare autosomal dominant developmental disorder involving multiple organs. CHD7 is a major causative gene of CHARGE syndrome. We performed targeted-exome sequencing using a next-generation sequencer for molecular diagnosis of a 4-month-old male patient who was clinically suspected to have CHARGE syndrome, and report a novel monoallelic mutation in CHD7, NM_017780.3(CHD7_v001):c.2966del causing a reading frameshift [p.(Cys989Serfs*3)]

Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply

The 2016 Kumamoto earthquake had a significant impact on groundwater levels and quality. In some areas, the groundwater level increased significantly due to the release of groundwater from upstream mountainous regions. Conversely, the groundwater level in other areas greatly decreased due to the creation of new fracture networks by the earthquake. There were also significant changes in certain groundwater quality variables. In this study, we used clustering based SOM (self-organizing maps) analysis to improve the understanding of earthquake effects on groundwater quality. We were especially interested in effects on groundwater used for drinking purposes and in nitrate concentration. For this purpose, we studied groundwater nitrate (NO3 + NO2–N) concentrations for the period 2012–2017. Nitrate concentration changes were classified into seven typical SOM clusters. The clusters were distributed in three representative geographical regions: A high concentration region (>4 mg/L), a low concentration region (<1.6 mg/L) with minimal anthropogenic loading area, and an intermediate concentration region (2–4 mg/L). Depending on these regions, the nitrate concentration changes just before and after the earthquake had both increasing and decreasing trends between 2015–2017. This points to complex physiographical relationships for release of stored upstream groundwater, promotion of infiltration of shallow soil water/groundwater, and nitrate concentration as affected by earthquakes. We present an analysis of these complex relationships and a discussion of causes of nitrate concentration changes due to earthquakes