Switching Head-Tail Funnel UNITER for Dual Referring Expression Comprehension with Fetch-and-Carry Tasks

This paper describes a domestic service robot (DSR) that fetches everyday objects and carries them to specified destinations according to free-form natural language instructions. Given an instruction such as "Move the bottle on the left side of the plate to the empty chair," the DSR is expected to identify the bottle and the chair from multiple candidates in the environment and carry the target object to the destination. Most of the existing multimodal language understanding methods are impractical in terms of computational complexity because they require inferences for all combinations of target object candidates and destination candidates. We propose Switching Head-Tail Funnel UNITER, which solves the task by predicting the target object and the destination individually using a single model. Our method is validated on a newly-built dataset consisting of object manipulation instructions and semi photo-realistic images captured in a standard Embodied AI simulator. The results show that our method outperforms the baseline method in terms of language comprehension accuracy. Furthermore, we conduct physical experiments in which a DSR delivers standardized everyday objects in a standardized domestic environment as requested by instructions with referring expressions. The experimental results show that the object grasping and placing actions are achieved with success rates of more than 90%.Comment: Accepted for presentation at IROS202

jaw osteonecrosis risk in hip fractures

Purpose : Antiresorptive agents, such as bisphosphonates, are useful for the prevention of the recurrence of hip fractures. However, their administration has a risk of antiresorptive agent-related osteonecrosis of the jaw (ARONJ), and risk factors include poor oral hygiene. It is difficult for an orthopedic surgeon to examine a patient’s oral condition thoroughly. This study evaluated the relationship between risk factors for ARONJ and intraoral findings in hip fracture patients. Materials and Methods : We evaluated 79 patients (average age of 82.2 years) with hip fracture surgery who underwent an oral assessment by dentists. The risk assessments of the intraoral findings were classified into four levels (levels 0-3), with levels 2 and 3 requiring dental treatment intervention. Data that could be extracted as risk factors of ARONJ were also examined. Results : Level 1 was found most frequently (54.4%), followed by level 0 (35.4%), level 2 (8.9%), level 3 (1.3%). The area under the receiver operating characteristic curve for the number of risk factors for the two groups (dental treatment intervention required and unnecessary) and oral findings were 0.732. When the cut-off value was set to two risk factors, the specificity and sensitivity was 53.5% and 87.5%. Conclusions : For hip fracture patients with a more than 2 risk factors, dental visits are recommended to prevent ARONJ. This is a useful evaluation method that can be used to screen for ONJ from data obtained from other risk factors, even if it is difficult to evaluate the oral condition in hospitals where dentists are absent

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002-0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification

Mixing of Active and Sterile Neutrinos

We investigate mixing of neutrinos in the $\nu$MSM (neutrino Minimal Standard Model), which is the MSM extended by three right-handed neutrinos. Especially, we study elements of the mixing matrix $\Theta_{\alpha I}$ between three left-handed neutrinos $\nu_\alpha$ ($\alpha = e,\mu,\tau$) and two sterile neutrinos $N_I$ ($I=2,3$) which are responsible to the seesaw mechanism generating the suppressed masses of active neutrinos as well as the generation of the baryon asymmetry of the universe (BAU). It is shown that $\Theta_{eI}$ can be suppressed by many orders of magnitude compared with $\Theta_{\mu I}$ and $\Theta_{\tau I}$, when the Chooz angle $\theta_{13}$ is large in the normal hierarchy of active neutrino masses. We then discuss the neutrinoless double beta decay in this framework by taking into account the contributions not only from active neutrinos but also from all the three sterile neutrinos. It is shown that $N_2$ and $N_3$ give substantial, destructive contributions when their masses are smaller than a few 100 MeV, and as a results $\Theta_{e I}$ receive no stringent constraint from the current bounds on such decay. Finally, we discuss the impacts of the obtained results on the direct searches of $N_{2,3}$ in meson decays for the case when $N_{2,3}$ are lighter than pion mass. We show that there exists the allowed region for $N_{2,3}$ with such small masses in the normal hierarchy case even if the current bound on the lifetimes of $N_{2,3}$ from the big bang nucleosynthesis is imposed. It is also pointed out that the direct search by using $\pi^+ \to e^+ + N_{2,3}$ and $K^+ \to e^+ + N_{2,3}$ might miss such $N_{2,3}$ since the branching ratios can be extremely small due to the cancellation in $\Theta_{eI}$, but the search by $K^+ \to \mu^+ + N_{2,3}$ can cover the whole allowed region by improving the measurement of the branching ratio by a factor of 5.Comment: 30 pages, 32 figure

The inversion mechanism for the reaction H + CD₄ → CD₃H + D

The reaction H + CD4 → CD3H + D is shown to take place by an inversion mechanism. The evidence is as follows. When the H atom has an anisotropic (perpendicular) velocity distribution, the D atom velocity distribution is also perpendicular. For a relative energy near 2 eV, the reaction cross section for H + CD4 is 0.084 ± 0.014 A2 and for H + CH3D is 0.040 ± 0.015 A2. At the same H atom energy, when CH3CD3 is substituted for CD4, no D atoms can be detected. Finally, around 80% of the initial H atom kinetic energy is released as kinetic energy of the D atom showing that the reaction is nearly vibrationally adiabatic

Filtration-induced production of conductive/robust Cu films on cellulose paper by low-temperature sintering in air

Electrical resistivity,Transmitted light microscopy images, TG curves, XRD pattern,AES spectru