Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio

The Optimization of Finishing Train Based on Improved Genetic Algorithm

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.389

Solution to a class of multistate Landau-Zener model beyond integrability conditions

We study a class of multistate Landau-Zener model which cannot be solved by integrability conditions or other standard techniques. By analyzing analytical constraints on its scattering matrix and performing fitting to results from numerical simulations of the Schr\"{o}dinger equation, we find nearly exact analytical expressions of all its transition probabilities for specific parameter choices. We also determine the transition probabilities up to leading orders of series expansions in terms of the inverse sweep rate (namely, in the diabatic limit) for general parameter choices. We further show that this model can describe a Su-Schrieffer-Heeger chain with couplings changing linearly in time. Our work presents a new route, i.e., analytical constraint plus fitting, to analyze those multistate Landau-Zener models which are beyond the applicability of conventional solving methods.Comment: Version accepted by Physica Script

On the Calibration of Human Pose Estimation

Most 2D human pose estimation frameworks estimate keypoint confidence in an ad-hoc manner, using heuristics such as the maximum value of heatmaps. The confidence is part of the evaluation scheme, e.g., AP for the MSCOCO dataset, yet has been largely overlooked in the development of state-of-the-art methods. This paper takes the first steps in addressing miscalibration in pose estimation. From a calibration point of view, the confidence should be aligned with the pose accuracy. In practice, existing methods are poorly calibrated. We show, through theoretical analysis, why a miscalibration gap exists and how to narrow the gap. Simply predicting the instance size and adjusting the confidence function gives considerable AP improvements. Given the black-box nature of deep neural networks, however, it is not possible to fully close this gap with only closed-form adjustments. As such, we go one step further and learn network-specific adjustments by enforcing consistency between confidence and pose accuracy. Our proposed Calibrated ConfidenceNet (CCNet) is a light-weight post-hoc addition that improves AP by up to 1.4% on off-the-shelf pose estimation frameworks. Applied to the downstream task of mesh recovery, CCNet facilitates an additional 1.0mm decrease in 3D keypoint error

Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data

Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange. Nonetheless, FL implementations often suffer from non-i.i.d. and long-tailed class distributions across mobile applications, e.g., autonomous vehicles, which leads models to overfitting as local training may converge to sub-optimal. In our study, we explore the impact of data heterogeneity on model bias and introduce an innovative personalized FL framework, Multi-level Personalized Federated Learning (MuPFL), which leverages the hierarchical architecture of FL to fully harness computational resources at various levels. This framework integrates three pivotal modules: Biased Activation Value Dropout (BAVD) to mitigate overfitting and accelerate training; Adaptive Cluster-based Model Update (ACMU) to refine local models ensuring coherent global aggregation; and Prior Knowledge-assisted Classifier Fine-tuning (PKCF) to bolster classification and personalize models in accord with skewed local data with shared knowledge. Extensive experiments on diverse real-world datasets for image classification and semantic segmentation validate that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions, which enhances accuracy by as much as 7.39% and accelerates training by up to 80% at most, marking significant advancements in both efficiency and effectiveness.Comment: 14 pages, 10 figure

Lamb waves manipulation by piezoelectric metasurface with tunable diffraction orders

In this paper, a piezoelectric metasurface is proposed to manipulate the anti-symmetric mode Lamb wave by altering the diffraction order. The metasurface attached to a host plate is symmetrically arranged by out-of-plane polarized piezoelectric patches connected with synthetic inductance circuits. Without changing the physical configuration, the transmitted phase of the anti-symmetric mode Lamb wave can be shifted arbitrarily in 0 ∼ 2π range by the metasurface. Furthermore, the relationship between the phase gradient and diffraction order is investigated, and different orders of diffraction waves can be obtained by adjusting the shunting inductance circuits. The symmetric transmission and asymmetric transmission from a couple of axis symmetric incident waves can be realized by utilizing +1st-order and −1st-order diffraction. Moreover, omnidirectional wave reflection and wave trapping in channelized waveguides can also be realized by utilizing the 0th-order diffraction. The results indicate that the proposed piezoelectric metasurface has great potentials in manipulating guided waves with a large incident angle and isolating wave propagation

Clinical and molecular evaluations of siblings with “pure” 11q23.3-qter trisomy or reciprocal monosomy due to a familial translocation t (10;11) (q26;q23.3)

11qter trisomy is rare, mostly occurs in combination with partial monosomy of a terminal segment of another chromosome due to unbalanced segregation of parental translocations. Pure 11qter trisomy is rarer, only five cases have so far been reported. Here we report a family with all four siblings affected with neurodevelopmental disorders and facial dysmorphism. Chromosomal microarray analysis (CMA) identified 11q23.3-qter (15.1 Mb) deletion in one and reciprocal duplication in the other three siblings. Both father and grandfather are balanced translocation (46, XY, t (10;11) (q26;q23)) carriers. The genetic material involved on chromosome 10 is very limited (270 kb). Thus, the pedigree presented rare cases with “pure” 11qter trisomy or reciprocal 11qter monosomy (Jacobsen syndrome), offering a unique opportunity to examine clinical presentations of multiple individuals with identical genomic imbalance. The proband with 11qter monosomy presented with many features of Jacobsen syndrome. The three 11qter trisomy carriers presented with shared craniofacial features including brachycephaly and short philtrum. They are also significant for the following neurodevelopmental and neuropsychiatric defects: intellectual disability, expressive language deficiency, autistic features, auditory hallucination, self-talking and pain insensitivity. To our knowledge, this is the smallest “pure” trisomy 11qter so far reported and this is the first report to describe the neuropsychiatric features of patients with 11qter trisomy. Our observation also revealed dissimilar features in our patients compared with those of previously published trisomy 11qter cases. The pedigree also revealed phenotypic heterogeneity among siblings with identical genomic imbalance

Clinical and molecular evaluations of siblings with “pure” 11q23.3-qter trisomy or reciprocal monosomy due to a familial translocation t (10;11) (q26;q23.3)

11qter trisomy is rare, mostly occurs in combination with partial monosomy of a terminal segment of another chromosome due to unbalanced segregation of parental translocations. Pure 11qter trisomy is rarer, only five cases have so far been reported. Here we report a family with all four siblings affected with neurodevelopmental disorders and facial dysmorphism. Chromosomal microarray analysis (CMA) identified 11q23.3-qter (15.1 Mb) deletion in one and reciprocal duplication in the other three siblings. Both father and grandfather are balanced translocation (46, XY, t (10;11) (q26;q23)) carriers. The genetic material involved on chromosome 10 is very limited (270 kb). Thus, the pedigree presented rare cases with “pure” 11qter trisomy or reciprocal 11qter monosomy (Jacobsen syndrome), offering a unique opportunity to examine clinical presentations of multiple individuals with identical genomic imbalance. The proband with 11qter monosomy presented with many features of Jacobsen syndrome. The three 11qter trisomy carriers presented with shared craniofacial features including brachycephaly and short philtrum. They are also significant for the following neurodevelopmental and neuropsychiatric defects: intellectual disability, expressive language deficiency, autistic features, auditory hallucination, self-talking and pain insensitivity. To our knowledge, this is the smallest “pure” trisomy 11qter so far reported and this is the first report to describe the neuropsychiatric features of patients with 11qter trisomy. Our observation also revealed dissimilar features in our patients compared with those of previously published trisomy 11qter cases. The pedigree also revealed phenotypic heterogeneity among siblings with identical genomic imbalance

Relationship between eating attitudes, depression, and insight in schizophrenic patients with and without type 2 diabetes mellitus: a comparative study in Guangdong, China

BackgroundSchizophrenia, a severe mental disorder, is often complicated by Type 2 Diabetes Mellitus (T2DM), which can further impact patients’ psychological health. This study investigated the differences in eating attitudes, depression, and insight between schizophrenic patients with and without comorbid T2DM and explored the correlations among these factors to provide empirical support for clinical interventions.MethodsThis case-control study was conducted in Guangdong Province, China. From December 2022 to May 2023, a total of 300 hospitalized patients with schizophrenia (92 with comorbid T2DM and 208 without T2DM) were recruited. Data were collected using the Personal Information Form, Eating Attitudes Test (EAT-26), Hamilton Depression Scale (HAMD), and Insight and Treatment Attitudes Questionnaire (ITAQ). Statistical analyses, including t-tests, ANOVA, and multiple linear regression, were performed to examine differences and predictive factors of eating attitudes among patients. This study was approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (approval number: 2020028), and written informed consent was obtained from all participants.ResultsPatients with schizophrenia and comorbid T2DM exhibited significantly higher risks of eating disorders (EAT-26: 12.54 ± 9.77 vs. 9.07 ± 7.90, P=0.003), more severe depression (HAMD: 14.71 ± 7.36 vs. 11.80 ± 6.04, P=0.001), and poorer insight (ITAQ: 10.46 ± 6.01 vs. 12.16 ± 6.09, P=0.025) compared to those without T2DM. Regression analysis revealed that gender, weekly exercise frequency, depression, and insight were significant predictors of eating attitudes among patients with T2DM. For patients without T2DM, weekly exercise frequency, smoking status, and insight were significant predictors.ConclusionSchizophrenic patients with comorbid T2DM are facing increasing risks related to eating attitudes, depression, and insight which highlight the need for targeted interventions. Regular psychological assessment and tailored support strategies might improve their mental health and quality of life. Future research should focus on longitudinal studies to clarify causal relationships and develop more effective interventions