Theme Aspect Argumentation Model for Handling Fallacies

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However, a fallacy in one context may not be a fallacy in another context, so there is also a need to explain how and why it has come to be judged a fallacy. For the explainable fallacy identification, we present a novel approach to characterising fallacies through formal constraints, as a viable alternative to more traditional fallacy classifications by informal criteria. To achieve this objective, we introduce a novel context-aware argumentation model, the theme aspect argumentation model, which can do both: the modelling of a given argumentation as it is expressed (rhetorical modelling); and a deeper semantic analysis of the rhetorical argumentation model. By identifying fallacies with formal constraints, it becomes possible to tell whether a fallacy lurks in the modelled rhetoric with a formal rigour. We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability. We show and prove the consequences of these formal constraints. We then analyse the computational complexities of deciding the satisfiability of the constraints

Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems

The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm

Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise

The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets

Development of Low-Yield Stress Co–Cr–W–Ni Alloy by Adding 6 Mass Pct Mn for Balloon-Expandable Stents

This is the first report presenting the development of a Co–Cr–W–Ni–Mn alloy by adding 6 mass pct Mn to ASTM F90 Co–20Cr–15W–10Ni (CCWN, mass pct) alloy for use as balloon-expandable stents with an excellent balance of mechanical properties and corrosion resistance. The effects of Mn addition on the microstructures as well as the mechanical and corrosion properties were investigated after hot forging, solution treatment, swaging, and static recrystallization. The Mn-added alloy with a grain size of ~ 20 µm (recrystallization condition: 1523 K, 150 seconds) exhibited an ultimate tensile strength of 1131 MPa, 0.2 pct proof stress of 535 MPa, and plastic elongation of 66 pct. Additionally, it exhibited higher ductility and lower yield stress while maintaining high strength compared to the ASTM F90 CCWN alloy. The formation of intersecting stacking faults was suppressed by increasing the stacking fault energy (SFE) with Mn addition, resulting in a lower yield stress. The low-yield stress is effective in suppressing stent recoil. In addition, strain-induced martensitic transformation during plastic deformation was suppressed by increasing the SFE, thereby improving the ductility. The Mn-added alloys also exhibited good corrosion resistance, similar to the ASTM F90 CCWN alloy. Mn-added Co–Cr–W–Ni alloys are suitable for use as balloon-expandable stents.Yanagihara S., Ueki K., Ueda K., et al. Development of Low-Yield Stress Co–Cr–W–Ni Alloy by Adding 6 Mass Pct Mn for Balloon-Expandable Stents. Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, 52, 9, 4137. https://doi.org/10.1007/s11661-021-06374-7

Improvement of mechanical properties by microstructural evolution of biomedical Co-Cr-W-Ni alloys with the addition of Mn and Si

We investigated changes in the microstructure and mechanical properties of biomedical Co-20Cr-15W-10Ni alloys (mass%) containing 8 mass% Mn and 0-3 mass% Si due to hot forging, solution treatment, cold swaging, and static recrystallization. The η-phase (M₆X-M₁₂X type cubic structure, M: metallic elements, X: C and/or N, space group: Fd-3m (227)) and CoWSi type Laves phase (C14 MgZn2 type hexagonal structure, space group: P63/mmc (194)) were confirmed as precipitates in the as-cast and as-forged alloys. To the best of our knowledge, this is the first report that reveals the formation of CoWSi type Laves phase precipitates in Co-Cr-W-Ni-based alloys. The addition of Si promoted the formation of precipitates of both η-phase and CoWSi type Laves phase. The solution-treated 8Mn+(0, 1)Si-added alloys exhibited TWIP-like plastic deformation behavior with an increasing work-hardening rate during the early to middle stages of plastic deformation. This plastic deformation behavior is effective in achieving both the low yield stress and high strength required to develop a high-performance balloon-expandable stent. The 8Mn+2Si-added alloy retained the CoWSi type Laves phase even after solution treatment, such that the ductility decreased but the strength improved. Additions of Mn and Si are effective in improving the ductility and strength of the Co-Cr-W-Ni alloy, respectively.Ueki K., Yanagihara S., Ueda K., et al. Improvement of mechanical properties by microstructural evolution of biomedical Co-Cr-W-Ni alloys with the addition of Mn and Si. Materials Transactions 62, 229 (2021); https://doi.org/10.2320/matertrans.MT-M2020300

Overcoming the strength-ductility trade-off by the combination of static recrystallization and low-temperature heat-treatment in Co-Cr-W-Ni alloy for stent application

A process combining swaging, static recrystallization, and heat treatment at 873 K (low-temperature heat-treatment, LTHT) was developed for achieving both high ultimate strength and high ductility in Co-20Cr-15W-10Ni (mass%, CCWN) alloy for stent application. The alloys swaged to a sectional area reduction rate of 58.3% were annealed at 1373–1473 K for 30–300 s. Under annealing at 1373 K for 300 s, a fine grain structure with an average grain size of ~6 μm formed, while under annealing at 1473 K, a structure with an average grain size of 12 μm formed after 120 s. In the alloys annealed at 1373–1448 K, the formation of η-phase precipitates (M6X-M12X type, M: metallic elements, X: C and/or N) was observed, while no precipitates were observed in the alloys annealed at 1473 K. The improvement in ultimate strength by grain refinement was confirmed. Alloys annealed at 1473 K showed higher ductility compared to those annealed at 1373–1448 K even if the grain size was similar. It is considered that the η-phase precipitates deteriorated the ductility of the annealed alloys. LTHT suppressed the strain-induced martensitic γ-to-ε transformation to improve the ductility of the fine-grained as well as coarse-grained alloys. Thus, regardless of the grain size, it is newly evidenced that LTHT effectively improves ductility in CCWN alloy. By combining high-temperature short-time annealing and LTHT, both the ultimate strength and ductility of Co-20Cr-15W-10Ni (mass%) alloy improved, and it was possible to provide properties suitable for next-generation balloon-expandable stents with Co-20Cr-15W-10Ni (mass%) alloy.Ueki K., Yanagihara S., Ueda K., et al. Overcoming the strength-ductility trade-off by the combination of static recrystallization and low-temperature heat-treatment in Co-Cr-W-Ni alloy for stent application. Materials Science and Engineering A, 766, 138400. https://doi.org/10.1016/j.msea.2019.138400

Synchronous improvement in strength and ductility of biomedical Co–Cr–Mo alloys by unique low-temperature heat treatment

The microstructure and tensile properties of Co–27Cr–6Mo (mass%) alloys heat-treated at 673–1373 K were studied. Lower elongation was observed after heat treatment at 1073 K due to formation of carbonitride precipitates. In contrast, when low-temperature heat treatment (LTHT) was applied at 673–873 K, both the ultimate tensile strength and elongation synchronously improved compared with the solution-treated alloy. Electron backscatter diffraction analysis for plastic-strained alloys and in situ X-ray diffraction analysis under stress-induced conditions revealed that the strain-induced martensitic transformation (SIMT) of the γ(fcc)-phase to ε(hcp)-phase during plastic deformation was suppressed by the LTHT. Stacking faults (thin ε-phase) were observed to collide in the LTHT alloys. The following mechanisms for the synchronous improvement in the tensile strength and elongation after LHTH are proposed. First, stacking faults with multiple variants were formed during LTHT. Then, the ε-phase of a single variant formed by SIMT during plastic deformation collides with preexisting multi-variant stacking faults formed during LTHT, increasing the tensile strength. In addition, the SIMT during plastic deformation is suppressed in the high-plastic-strain region by the collision. This decreases the total amount of ε-phase formed during plastic deformation, which improves the ductility. We demonstrated that LTHT of Co–Cr–Mo alloys effectively improves the performance and mechanical safety of spinal fixation implants, which often fracture because of fatigue cracking.Ueki K., Abe M., Ueda K., et al. Synchronous improvement in strength and ductility of biomedical Co–Cr–Mo alloys by unique low-temperature heat treatment. Materials Science and Engineering A, 739, 53. https://doi.org/10.1016/j.msea.2018.10.016

What impact does postgraduate clinical training have on empathy among Japanese trainee dentists?

Background Enhancing empathy in healthcare education is a critical component in the development of a relationship between healthcare professionals and patients that would ensure better patient care; improved patient satisfaction, adherence to treatment, patients’ medication self-efficacy, improved treatment outcomes, and reduced patient anxiety. Unfortunately, however, the decline of empathy among students has been frequently reported. It is especially common when the curriculum transitions to a clinical setting. However, some studies have questioned the significance and frequency of this decline. Thus, the purpose of this study was to determine the impact of postgraduate clinical training on dental trainees’ empathy from cognitive, behavioral, and patients’ perspective. Methods This study included 64 trainee dentists at Okayama University Hospital and 13 simulated patients (SPs). The trainee dentists carried out initial medical interviews with SPs twice, at the beginning and the end of their clinical training. The trainees completed the Japanese version of the Jefferson Scale of Empathy for health professionals just before each medical interview. The SPs evaluated the trainees’ communication using an assessment questionnaire immediately after the medical interviews. The videotaped dialogue from the medical interviews was analyzed using the Roter Interaction Analysis System. Results No significant difference was found in the self-reported empathy score of trainees at the beginning and the end of the clinical training (107.73 [range, 85–134] vs. 108.34 [range, 69–138]; p = 0.643). Considering the results according to gender, male scored 104.06 (range, 88–118) vs. 101.06 (range, 71–122; p = 0.283) and female 109.17 (range, 85–134) vs. 111.20 (range, 69–138; p = 0.170). Similarly, there was no difference in the SPs’ evaluation of trainees’ communication (10.73 vs. 10.38, p = 0.434). Communication behavior in the emotional responsiveness category for trainees in the beginning was significantly higher than that at the end (2.47 vs. 1.14, p = 0.000). Conclusions Overall, a one-year postgraduate dental training program neither reduced nor increased trainee dentists’ empathy levels. Providing regular education support in this area may help trainees foster their empathy

Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model

A seismic wavefield reconstruction framework based on compressed sensing using the data-driven reduced-order model (ROM) is proposed and its characteristics are investigated through numerical experiments. The data-driven ROM is generated from the dataset of the wavefield using the singular value decomposition. The spatially continuous seismic wavefield is reconstructed from the sparse and discrete observation and the data-driven ROM. The observation sites used for reconstruction are effectively selected by the sensor optimization method for linear inverse problems based on a greedy algorithm. The proposed framework was applied to simulation data of theoretical waveform with the subsurface structure of the horizontally-stratified three layers. The validity of the proposed method was confirmed by the reconstruction based on the noise-free observation. Since the ROM of the wavefield is used as prior information, the reconstruction error is reduced to an approximately lower error bound of the present framework, even though the number of sensors used for reconstruction is limited and randomly selected. In addition, the reconstruction error obtained by the proposed framework is much smaller than that obtained by the Gaussian process regression. For the numerical experiment with noise-contaminated observation, the reconstructed wavefield is degraded due to the observation noise, but the reconstruction error obtained by the present framework with all available observation sites is close to a lower error bound, even though the reconstructed wavefield using the Gaussian process regression is fully collapsed. Although the reconstruction error is larger than that obtained using all observation sites, the number of observation sites used for reconstruction can be reduced while minimizing the deterioration and scatter of the reconstructed data by combining it with the sensor optimization method