Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning-driven process of alumina ceramics laser machining

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation of the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.
Representational Multiplicity Should Be Exposed, Not Eliminated

It is prevalent and well-observed, but poorly understood, that two machine learning models with similar performance during training can have very different real-world performance characteristics. This implies elusive differences in the internals of the models, manifesting as representational multiplicity (RM). We introduce a conceptual and experimental setup for analyzing RM and show that certain training methods systematically result in greater RM than others, measured by activation similarity via singular vector canonical correlation analysis (SVCCA). We further correlate it with predictive multiplicity measured by the variance in i.i. d. and out-of-distribution test set predictions, in four common image data sets. We call for systematic measurement and maximal exposure, not elimination, of RM in models. Qualitative tools such as our confabulator analysis can facilitate understanding and communication of RM effects to stakeholders.
Efficient Raycasting of View-Dependent Piecewise Constant Representations of Volumetric Data

We present an efﬁcient raycasting-based rendering algorithm for view-dependent piecewise constant representations of volumetric data. Our algorithm leverages the properties of perspective projection to simplify intersections of rays with the view-dependent frustums that form part of these representations. It also leverages spatial homogeneity in the underlying volume data to minimize memory accesses. We further introduce techniques for skipping empty-space and for dynamic subsampling for accelerated approximate renderings at controlled frame rates. Benchmarks show that responsive frame rates can be achieved close to the viewpoint of generation for HD display resolutions, while providing high-ﬁdelity approximate renderings of Gigabyte-sized volumes.
Explainability's Gain is Optimality's Loss?: How Explanations Bias Decision-making

Decisions in organizations are about evaluating alternatives and choosing the one that would best serve organizational goals. To the extent that the evaluation of alternatives could be formulated as a predictive task with appropriate metrics, machine learning algorithms are increasingly being used to improve the efficiency of the process. Explanations help to facilitate communication between the algorithm and the human decision-maker, making it easier for the latter to interpret and make decisions on the basis of predictions by the former. Feature-based explanations' semantics of causal models, however, induce leakage from the decision-maker's prior beliefs. Our findings from a field experiment demonstrate empirically how this leads to confirmation bias and disparate impact on the decision-maker's confidence in the predictions. Such differences can lead to sub-optimal and biased decision outcomes.
VectorMapNet: End-to-end Vectorized HD Map Learning

Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads. Existing solutions approach the semantic mapping problem by offline manual annotation, which suffers from serious scalability issues. Recent learning-based methods produce dense rasterized segmentation predictions to construct maps. However, these predictions do not include instance information of individual map elements and require heuristic post-processing to obtain vectorized maps. To tackle these challenges, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines in the bird's-eye view. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly to downstream autonomous driving tasks. Extensive experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP and 14.6mAP. Qualitatively, VectorMapNet is capable of generating comprehensive maps and capturing fine-grained details of road geometry. To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations. Our project website is available at \url{https://tsinghua-mars-lab.github. io/vectormapnet/}.
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models

Model selection in linear regression models is a major challenge when dealing with high-dimensional data where the number of available measurements (sample size) is much smaller than the dimension of the parameter space. Traditional methods for model selection such as Akaike information criterion, Bayesian information criterion (BIC), and minimum description length are heavily prone to overfitting in the high-dimensional setting. In this regard, extended BIC (EBIC), which is an extended version of the original BIC, and extended Fisher information criterion (EFIC), which is a combination of EBIC and Fisher information criterion, are consistent estimators of the true model as the number of measurements grows very large. However, EBIC is not consistent in high signal-to-noise-ratio (SNR) scenarios where the sample size is fixed and EFIC is not invariant to data scaling resulting in unstable behaviour. In this article, we propose a new form of the EBIC criterion called EBIC-Robust, which is invariant to data scaling and consistent in both large sample sizes and high-SNR scenarios. Analytical proofs are presented to guarantee its consistency. Simulation results indicate that the performance of EBIC-Robust is quite superior to that of both EBIC and EFIC.
A Two-Stage Approach for a Mixed-Integer Economic Dispatch Game in Integrated Electrical and Gas Distribution Systems

We formulate for the first time the economic dispatch problem among prosumers in an integrated electrical and gas distribution system (IEGDS) as a game equilibrium problem. Specifically, by approximating the nonlinear gas-flow equations either with a mixed-integer second-order cone (MISOC) or a piecewise affine (PWA) model and by assuming that electricity and gas prices depend linearly on the total consumption, we obtain a potential mixed-integer game. To compute an approximate generalized Nash equilibrium (GNE), we propose an iterative two-stage method that exploits a problem convexification and the gas-flow models. We quantify the quality of the computed solution and perform a numerical study to evaluate the performance of our method.
Near-Optimal No-Regret Learning for General Convex Games

A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player’s regret after T repetitions grows polylogarithmically in T , an exponential improvement over the traditional guarantees within the no-regret framework. However, so far these results have only been limited to certain classes of games with structured strategy spaces—such as normal-form and extensive-form games. The question as to whether O (polylog T ) regret bounds can be obtained for general convex and compact strategy sets—which occur in many fundamental models in economics and multiagent systems—while retaining eﬃcient strategy updates is an important question. In this paper, we answer this in the positive by establishing the ﬁrst uncoupled learning algorithm with O (log T ) per-player regret in general convex games , that is, games with concave utility functions supported on arbitrary convex and compact strategy sets. Our learning dynamics are based on an instantiation of optimistic follow-the-regularized-leader over an appropriately lifted space using a self-concordant regularizer that is, peculiarly, not a barrier for the feasible region. Further, our learning dynamics are eﬃciently implementable given access to a proximal oracle for the convex strategy set, leading to O (log log T ) per-iteration complexity; we also give extensions when access to only a linear optimization oracle is assumed. Finally, we adapt our dynamics to guarantee O ( √ T ) regret in the adversarial regime. Even in those special cases where prior results apply, our algorithm improves over the state-of-the-art regret bounds either in terms of the dependence on the number of iterations or on the dimension of the strategy sets.
Neural ensemble search via Bayesian sampling

Recently, neural architecture search (NAS) has been applied to automate the design of neural networks in real-world applications. A large number of algorithms have been developed to improve the search cost or the performance of the final selected architectures in NAS. Unfortunately, these NAS algorithms aim to select only one single well-performing architecture from their search spaces and thus have overlooked the capability of neural network ensemble (i.e., an ensemble of neural networks with diverse architectures) in achieving improved performance over a single final selected architecture. To this end, we introduce a novel neural ensemble search algorithm, called neural ensemble search via Bayesian sampling (NESBS), to effectively and efficiently select well-performing neural network ensembles from a NAS search space. In our extensive experiments, NESBS algorithm is shown to be able to achieve improved performance over state-of-the-art NAS algorithms while incurring a comparable search cost, thus indicating the superior performance of our NESBS algorithm over these NAS algorithms in practice.
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

Covariate-shift generalization, a typical case in out-of-distribution (OOD) generalization, requires a good performance on the unknown test distribution, which varies from the accessible training distribution in the form of covariate shift. Recently, independence-driven importance weighting algorithms in stable learning literature have shown empirical effectiveness to deal with covariate-shift generalization on several learning models, including regression algorithms and deep neural networks, while their theoretical analyses are missing. In this paper, we theoretically prove the effectiveness of such algorithms by explaining them as feature selection processes. We first specify a set of variables, named minimal stable variable set, that is the minimal and optimal set of variables to deal with covariate-shift generalization for common loss functions, such as the mean squared loss and binary cross-entropy loss. Afterward, we prove that under ideal conditions, independence-driven importance weighting algorithms could identify the variables in this set. Analysis of asymptotic properties is also provided. These theories are further validated in several synthetic experiments.
New interpretation of the chiral phase transition: Violation of the trilemma in QCD

We ﬁnd that the chiral phase transition (chiral crossover) in QCD at physical point is triggered by big imbalance among three fundamental quantities essential for the QCD vacuum structure: susceptibility functions for the chiral symmetry, axial symmetry, and the topological charge. The balance, dobbed the QCD trilemma, is unavoidably violated when one of the magnitudes among them is highly dominated, or suppressed. Based on a three-ﬂavor Nambu-Jona-Lasinio model, we explicitly evaluate the amount of violation of the QCD trilemma at physical point, and show that the violation takes place not only at vacuum, but even in a whole temperature regime including the chiral crossover epoch. This work conﬁrms and extends the suggestion recently reported from lattice QCD with 2 ﬂavors on dominance of the axial and topological susceptibilities left in the chiral susceptibility at high temperatures. It turns out that the imbalance is essentially due to the ﬂavor symmetry violation of the lightest three ﬂavors, and the ﬂavor breaking speciﬁcally brings enhancement of the axial anomaly contribution in the chiral order parameter, while the the strength of the axial breaking and the transition rate of the topological charge are fairly insensitive to the ﬂavor symmetry. The violation of QCD trilemma and its ﬂavor dependence can be tested by lattice simulations with 2 + 1 ﬂavors in the future, and would also give a new guiding principle to explore the ﬂavor dependence of the chiral phase transition, such as the Columbia plot, including possible extension with external ﬁelds. evaluate the temperature dependence of the (subtracted) quark condensate, scalar and pseudoscalar susceptibilities, and the topological susceptibility. We also check the consistency with the recent lattice QCD data on 2 + 1
CHEMICHAL COMPOSITION OF Origanum onites L. AND Thymbra spicata L., AND THEIR CARDIORESPIRATORY EFFECTS IN RABBITS

SUMMARY Origanum onites L. and Thymbra spicata L. were collected in June-July 2004 in Denizli, Western Turkey. Essential oils from the leaves of these plants were extracted using either Soxhlet or steam distillation. The components of essential oils were removed from the steam distillate by C18 solid phase extraction. Chemical compositions of the essential oils were determined using two-dimensional gas chromatography (GC-TOF/MS). Portions (1 mL) of aqueous extracts (differing between groups in types of plants and extraction methods) were given orally to twenty-eight anesthetized rabbits using feeding tubes, and their electrocardiography (ECG) and spirometric data recorded. The possible effects of these extracts obtained by using two different methods were evaluated by statistical analyses. According to the data obtained from the animal experiments, administration of these plant extracts caused a decrease in ventilation and heart rate. The common usage of thyme water in public, and the purposes of consumption were discussed in terms of public health.
PENGARUH E-SERVICE QUALITY DAN E-TRUS TERHADAP E-CUSTOMER SATISFACTION SERTA IMPLIKASINYA TERHADAP ECUSTOMER LOYALTY

Penelitian ini bertujuan untuk menjelaskan pengaruh eService Quality terhadap eCustomer Satisfaction, eTrust terhadap eCustomer Satisfaction, eService Quality terhadap eCustomer Loyalty, eTrust terhadap eCustomer Loyalty, dan eCustomer Satisfaction terhadap eCustomer Loyalty. Jumlah sampel dalam penelitian ini sebanyak 98 nasabah di BRI KCP Lawang yang pernah menikmati atau menggunakan layanan eBanking BRI baik Internet Banking maupun Mobile Banking BRI. Metode analisis data yang digunakan yaitu Generalized Structured Component Analysis (GSCA). Hasil penelitian menunjukkan bahwa eService Quality eBanking BRI berpengaruh secara signifikan pada eCustomer Satisfaction. Terdapat pengaruh signifikan antara eTrust dan eCustomer Satisfaction. eService Quality secara langsung berpengaruh tidak signifikan terhadap eCustomer Loyalty nasabah pengguna eBanking BRI di BRI KCP Lawang. eTrust berpengaruh signifikan terhadap eCustomer Loyalty. Terdapat pengaruh langsung antara eCustomer Satisfaction terhadap eCustomer Loyalty.
Design, implementation, and evaluation of community health worker training programs in Latinx communities: A scoping review

Abstract This review examines the current reporting trends of program design, implementation, and evaluation of training programs for Latinx community health workers. Five scholarly databases were searched using a scoping review methodology to identify articles describing training programs for Latinx community health workers. The timeframe was 2009 to 2021. We identified 273 articles, with 59 meeting inclusion criteria. Researchers thematically coded the articles to identify reporting strategies related to program design, implementation, and evaluation. Findings suggest a lack of consensus in reporting elements critical to program resources, instructor qualifications, frequency and length of training implementation, theoretical background, and pedagogical tools associated with the training program. We offer detailed reporting recommendations of community health worker training programs to support the consistent dissemination of promising practices and facilitate the initiation of new programs for Latinx community health workers.
A network analysis in adolescent anorexia nervosa exploring the connection between both patient and carer reactions and outcome

Abstract Objective This paper used network analysis to test the associations between eating disorder‐related psychopathology and carers’ responses to anorexia nervosa symptoms in adolescents. Additionally, the prognostic value of central and bridge network nodes was explored. Method This is a secondary analysis of a three‐armed randomised‐controlled‐trial of adolescents with anorexia nervosa (n = 149) and their primary carer (n = 149) who were allocated to either treatment as usual (TAU), or one of two versions of a carer skills intervention (ECHO) added to TAU. A network analysis was run in the full sample. The prognostic role of central and bridge nodes was tested through multiple regression analyses. Results Carers’ depression and emotional over‐involvement, as well as patients' depression showed the highest strength centrality. Patients' depression and carers’ accommodation exhibited the highest bridge expected influence. Across the full sample, and in the ECHO group, carers’ accommodation predicted patients' higher body mass index (BMI), while patients' depression predicted worse psychosocial functioning at 1‐year follow‐up. In the ECHO group, higher carers’ depression also predicted lower BMI. Conclusions Carers’ accommodation and depression in both carers and patients were involved in the maintenance of psychopathology in adolescents with anorexia nervosa. Depression in both patients and carers is a potential treatment target for family interventions.
Oral manifestations in SARS-CoV-2 infection

Background We aimed to evaluate the prevalence of predisposing factors and oral manifestations in SARS-CoV-2 infection. Material and Methods 204 SARS-CoV-2 positive patients were included in the study. Questions regarding the systemic, periodontal health, oral hygiene habits, common symptoms and, oral manifestations of COVID-19 such as oral lesions, and dry mouth were included in the survey. The Visual Analogue Scale (VAS) was used. Results 47.5% of individuals had various systemic diseases. Dry mouth (44.2%) and oral lesions (22.4%) were the most common oral manifestations in COVID-19 patients. Also, dry mouth had the highest VAS score. The most common oral lesion locations were buccal mucosa (15.2%) and tongue (10.8%). The majority of participants (142 patients) were affected by taste disorders. Patients who received periodontal treatment before SARS-CoV-2 infection reported fewer oral complaint and manifestations than those who did not receive periodontal therapy (p=0.032). There was no statistically significant difference between males and females on the presence of any oral manifestations, and taste disorders. Conclusions Our results showed that SARS-CoV-2 could cause oral manifestations. However various predisposing factors may be part of the etiology and promote oral findings. Key words:SARS-CoV-2, COVID-19, xrestomia, dysgeusia, oral manifestation.
Sensitivity of the S1 neuronal calcium network to insulin and Bay‐K 8644 in vivo: Relationship to gait, motivation, and aging processes

Neuronal hippocampal Ca2+ dysregulation is a critical component of cognitive decline in brain aging and Alzheimer's disease and is suggested to impact communication and excitability through the activation of a larger after hyperpolarization. However, few studies have tested for the presence of Ca2+ dysregulation in vivo, how it manifests, and whether it impacts network function across hundreds of neurons. Here, we tested for neuronal Ca2+ network dysregulation in vivo in the primary somatosensory cortex (S1) of anesthetized young and aged male Fisher 344 rats using single‐cell resolution techniques. Because S1 is involved in sensory discrimination and proprioception, we tested for alterations in ambulatory performance in the aged animal and investigated two potential pathways underlying these central aging‐ and Ca2+‐dependent changes. Compared to young, aged animals displayed increased overall activity and connectivity of the network as well as decreased ambulatory speed. In aged animals, intranasal insulin (INI) increased network synchronicity and ambulatory speed. Importantly, in young animals, delivery of the L‐type voltage‐gated Ca2+ channel modifier Bay‐K 8644 altered network properties, replicating some of the changes seen in the older animal. These results suggest that hippocampal Ca2+ dysregulation may be generalizable to other areas, such as S1, and might engage modalities that are associated with locomotor stability and motivation to ambulate. Further, given the safety profile of INI in the clinic and the evidence presented here showing that this central dysregulation is sensitive to insulin, we suggest that these processes can be targeted to potentially increase motivation and coordination while also reducing fall frequency with age.
Nursing students' motivational and self‐regulated learning during the COVID‐19 pandemic: A cross‐sectional study

Abstract On‐site clinical training, which has been greatly hindered during the COVID‐19 pandemic, is essential in nursing practicum. This study aimed to identify the predictors of nursing students' motivational and self‐regulated learning in clinical nursing practicum during the pandemic. This cross‐sectional study aimed to identify the predictors of motivational and self‐regulated learning among nursing students in clinical nursing practicum during the COVID‐19 pandemic. An online survey was completed by 195 nursing students in Korea in 2021. Students' moral sensitivity and motivational and self‐regulated learning were assessed using the Moral Sensitivity Questionnaire and the Motivated Strategies for Learning Questionnaire. Multiple regression analysis revealed that moral sensitivity was the most potent predictor of motivational and self‐regulated learning, followed by online clinical practicum experience, determination to become a nurse, and online practicum type; these factors explained 21.0% of the variance. Practicum courses using a tailored hybrid model, combining online and in‐person approaches for imparting nursing practices and inspiring moral sensitivity, can enhance nursing students' motivational and self‐regulated learning. Modules to enhance nursing students' moral sensitivity should be included in online practicum curricula.