Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an "action" for obtaining one's desired decision result. In practice, however, showing only a perturbation vector is often insufficient for users to execute the action. The reason is that if there is an asymmetric interaction among features, such as causality, the total cost of the action is expected to depend on the order of changing features. Therefore, practical CE methods are required to provide an appropriate order of changing features in addition to a perturbation vector. For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). We introduce a new objective function that evaluates a pair of an action and an order based on feature interaction. To extract an optimal pair, we propose a mixed-integer linear optimization approach with our objective function. Numerical experiments on real datasets demonstrated the effectiveness of our OrdCE in comparison with unordered CE methods.Comment: 20 pages, 5 figures, to appear in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Ferromagnetism in multi--band Hubbard models: From weak to strong Coulomb repulsion

We propose a new mechanism which can lead to ferromagnetism in Hubbard models containing triangles with different on-site energies. It is based on an effective Hamiltonian that we derive in the strong coupling limit. Considering a one-dimensional realization of the model, we show that in the quarter-filled, insulating case the ground-state is actually ferromagnetic in a very large parameter range going from Tasaki's flat-band limit to the strong coupling limit of the effective Hamiltonian. This result has been obtained using a variety of analytical and numerical techniques. Finally, the same results are shown to apply away from quarter-filling, in the metallic case.Comment: 12 pages, revtex, 12 figures,needs epsf and multicol style file

An Invertebrate Hyperglycemic Model for the Identification of Anti-Diabetic Drugs

The number of individuals diagnosed with type 2 diabetes mellitus, which is caused by insulin resistance and/or abnormal insulin secretion, is increasing worldwide, creating a strong demand for the development of more effective anti-diabetic drugs. However, animal-based screening for anti-diabetic compounds requires sacrifice of a large number of diabetic animals, which presents issues in terms of animal welfare. Here, we established a method for evaluating the anti-diabetic effects of compounds using an invertebrate animal, the silkworm, Bombyx mori. Sugar levels in silkworm hemolymph increased immediately after feeding silkworms a high glucose-containing diet, resulting in impaired growth. Human insulin and 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR), an AMP-activated protein kinase (AMPK) activator, decreased the hemolymph sugar levels of the hyperglycemic silkworms and restored growth. Treatment of the isolated fat body with human insulin in an in vitro culture system increased total sugar in the fat body and stimulated Akt phosphorylation. These responses were inhibited by wortmannin, an inhibitor of phosphoinositide 3 kinase. Moreover, AICAR stimulated AMPK phosphorylation in the silkworm fat body. Administration of aminoguanidine, a Maillard reaction inhibitor, repressed the accumulation of Maillard reaction products (advanced glycation end-products; AGEs) in the hyperglycemic silkworms and restored growth, suggesting that the growth defect of hyperglycemic silkworms is caused by AGE accumulation in the hemolymph. Furthermore, we identified galactose as a hypoglycemic compound in jiou, an herbal medicine for diabetes, by monitoring its hypoglycemic activity in hyperglycemic silkworms. These results suggest that the hyperglycemic silkworm model is useful for identifying anti-diabetic drugs that show therapeutic effects in mammals

The use of wearable devices in chronic disease management to enhance adherence and improve telehealth outcomes: a systematic review and meta-analysis

Introduction: Wearable device (WD) interventions are rapidly growing in chronic disease management; nevertheless, the effectiveness of these technologies to monitor telehealth outcomes has not been adequately discussed. This study aims to evaluate the effects of WDs in adherence and other health outcomes for people with chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and cardiac disease (CD). Methods: CINAHL, PsycINFO, CENTRAL, and EMBASE were searched for randomized controlled trials (RCTs) and non-RCTs from 1937 to February 2020. Studies comparing interventions with the use of WD were assessed for quality in RCTs and a meta-analysis was performed. Results: Eleven studies were included in this review. All of the interventions involved WD use with educational support such as goal setting, virtual social support, e-health program, real-time feedback, written information, maintain diary, and text messaging. The meta-analysis showed no difference in adherence (p = .38). The DM group showed effects of more than a 2% reduction in weight when WDs were implemented for three months (risk ratio = 2.20; 95% confidence interval (CI) 1.38 to 3.50; p = .0009), as well as blood glucose (mean difference (MD) = –32.39; 95% CI = –48.07 to –16.72; p

Care Staff’s Daily Living Decision-Making Support Scale for Older Adults with Dementia in Japan: Development of Validity and Reliability

This study aimed to develop and validate a scale to assess the daily-living decision-making support of care staff for older adults with dementia (OwDs) in Japan. A questionnaire survey was conducted among 138 care staff at two geriatric healthcare facilities from February to March 2021. The Daily Living Decision-Making Support Scale for Older Adults with Dementia (DL-DM) was developed using item analysis, factor analysis, and covariance structure analysis. The factor analysis yielded 12 items and three factors: (1) support for the formation and expression of intentions in daily life based on the life background and values of OwDs; (2) attitudes and devising ways to communicate regarding the formation and expression of intentions in OwDs daily lives; and (3) devising ways to support OwDs in realizing their intentions in daily life. The internal consistency reliability analysis yielded a Cronbach’s α of 0.87 for the total scale. The DL-DM correlated with the concurrent validity measures as expected. The DL-DM demonstrated validity and reliability as a potential scale to assess support for OwDs in daily life decision-making. The results also suggest an association between the DL-DM and person-centered care for OwDs