Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Error Modelling for the Parallelism Between the Gear Axis and the Z-axis of GMI

Gear measuring instrument (GMI) is the mainstream equipment for gear measurement. As the measurement accuracy of gears improves and GMI is gradually used in production, thermal errors are becoming an important factor affecting the measurement accuracy of GMI. In the precision measurement of the gears, the parallelism errors between the gear axis and the Z-axis of the GMI are the major error source. This error is usually reduced by mechanical adjustment when a GMI is assembled. When the temperature of the GMI and its environment changes, the thermal deformation will lead to new parallelism errors between the gear axis and the Z-axis of the GMI, which will further cause errors in the GMI. In this paper, the effect of the parallelism thermal errors of the GMI is studied, and the parallelism thermal errors model under room temperature environment was established. First, the temperature field of the GMI and its environment and the parallelism thermal errors were measured; then, the TMP are grouped using the FCM, and the key TMP were selected by optimizing the group of the measurement points using Pearson correlation coefficient method; finally, the parallelism thermal errors were established by RBF (Radial Basis Function) neural network and tested by using the measured data. The experimental results show that the predictive accuracy of the established parallelism thermal error model can reach 60.89% in the X direction and 82.26% in the Y direction, indicating good generalization ability and robustness. The model can be used for subsequent thermal error compensation.
Establishment of Straight Profile Hourglass Worm under Virtual Reality

Virtual workpiece is widely used in dynamic simulation and can be imported into the Virtual Gear Measuring Center (VGMC) to be virtually measured. The tooth surface of hourglass worm is complicated, so the dynamic meshing performance is often improved by modification of the tooth surface. The design cycle of traditional tooth surface modification without digital virtual workpiece is long, and the dynamic meshing performance only can be judged by getting meshing spots with sample parts. In this paper, based on the direct digital modeling, the working tooth surface and the non-working tooth surface are processed respectively for the straight profile enveloping worm, and the errors introduced by the turning tool in the modeling process are eliminated. Segmental cutting and inversion methods are proposed to greatly improve the modeling efficiency. By writing software and parameterizing modeling process, the virtual workpiece model of the hourglass worm with controllable accuracy and no holes and redundant faces can be obtained conveniently and quickly. The model can improve the design efficiency and shorten the design cycle.
Laser Doppler Anemometry technique to study the flow field in the nozzle and in the water jet of a Pelton turbine

The constant availability of water from the mountain basins is crucial to feed the hydropower plants that currently represent the most important renewable energy source in many countries, like Italy. The long dry periods that are nowadays registered during the summer periods have imposed the managers of the hydropower plants to operate the machines at very low flow rates, close to the lowest allowed values. In this work we focus our attention on the fluid-dynamics of the water jet of a Pelton machine and of the corresponding nozzle, with particular attention to the low flow-rate ranges. The nozzle has been manufactured in PMMA to be able to study the internal fluid-dynamics using the Laser Doppler Anemometry (LDA) technique. The shape of the nozzle has been adapted to limit the laser beam distortion and an algorithm has been implemented to correct the position of the measurement volume and the evaluation of the velocity components. Moreover, a specific measurement layout has been proposed and implemented to be able to study the velocity of the water jet downstream the nozzle and to study the homogeneity of the flow velocity in the jet, which is a crucial characteristic to grant a proper operation of the Pelton runner. The specific set-up of the test bench and the LDA parameters are presented; the signal of the LDA receiver proved to be very consistent and reliable. Moreover, the results of the measurements have been used to validate CFD simulations and to extend the results of the analyses to other operating conditions of the Pelton nozzle
Improved Broadband Emissivity Parameterization for Water Vapor Cooling Rate Calculations

Abstract Reference transmissivities based on line-by-line calculations have been computed for a wide range of homogeneous paths of water vapor. A new approach is employed in which wideband emissivities are directly fitted to the line-by-line reference calculations without using the intermediate step of narrowband models. A significant improvement in accuracy is obtained over previous schemes. Compared with line-by-line computed fluxes and cooling rates (without continuum absorption) for the standard middle-latitude summer (MLS) profile, the maximum error in fluxes is 1.5 W m−2 agreement is within 1% in fluxes and within 0.11 K/day, or 5%. in cooling rate. Unlike most published water vapor continuum schemes, which use the Roberts et al. model, the authors have reformulated the treatment of the water vapor continuum by producing a new parameterization based on the semiempirical model of Cough et al. This results in ∼7.5 W m−2 difference in calculated radiative fluxes at the tropopause, and maximum differenc.
In vivo skin optical clearing for improving imaging and light-induced therapy: a review

Abstract. Significance Skin is the largest organ and also the first barrier of body. Skin diseases are common, and cutaneous microcirculation is relative to various diseases. Researchers attempt to develop novel imaging techniques to obtain the complex structure, components, and functions of skin. Modern optical techniques provide a powerful tool with non-invasiveness, but the imaging performance suffers from the turbid character of skin. In vivo skin optical clearing technique has been proposed to reduce tissue scattering and enhance penetration depth of light and became a hot topic of research. Aim The aim of this review is to provide a comprehensive overview of recent development of in vivo skin optical clearing methods, how in vivo skin optical clearing enhances imaging performance, and its applications in study and light therapy of various diseases. Approach Based on the references published over the last decade, the important milestones on the mechanism, methods, and its fundamental and clinical applications of in vivo skin optical clearing technique are provided. Results With the deepening understanding of skin optical clearing mechanisms, efficient in vivo skin optical clearing methods were constantly screened out. These methods have been combined with various optical imaging techniques to improve imaging performances and acquire deeper and finer skin-related information. In addition, in vivo skin optical clearing technique has been widely applied in assisting study of diseases as well as achieving safe, high-efficiency light-induced therapy. Conclusions In the last decade, in vivo skin optical clearing technique has developed rapidly and played an important role in skin-related studies.
Effectiveness of acupuncture therapy for the prevention of emergence agitation in children: A systematic review and meta-analysis with trial sequential analysis

This study aimed to evaluate the effectiveness of acupuncture therapy in preventing emergence agitation (EA) in children. A systematic review and meta-analysis were conducted across multiple locations according to the articles searched. Seven databases, including trial registration sites, were searched. A total of six trials were included involving 489 patients; of them, 244 received acupuncture therapy. Randomized clinical trials (RCTs) evaluating the incidence of EA compared with placebo/sham or standard care in children were included. The primary outcome was the incidence of EA, as evaluated using a specific assessment tool. Data about the incidence rate of EA, heterogeneity, quality of trials and evidence, and adverse events were collected. Additionally, data about patient demographic characteristics, type of anesthesia, duration and onset of acupuncture therapy, EA and pain score, time taken for extubation, and post-anesthesia care unit length of stay were collected. The results indicated that the overall incidence of EA in the acupuncture therapy group and the control group was 23.4% and 39.5%, respectively, with no significant difference (risk ratio, 0.62; 95% confidence interval, 0.26–1.48; I2 = 63%). Subgroup analysis showed a significant difference in the overall incidence of EA in the acupuncture therapy and control groups according to surgery type (high-risk vs. low-risk surgery), suggesting that acupuncture therapy may be effective in reducing EA for patients undergoing high-risk surgery. The quality of evidence was downgraded to “very low” due to the study designs, inconsistency, and possible publication bias. In conclusion, this meta-analysis shows that the currently available RCTs are insufficient to determine the effectiveness of acupuncture therapy in preventing EA in children undergoing general anesthesia.
Performance Characteristics of a High-Sensitivity, Three-Wavelength, Total Scatter/Backscatter Nephelometer

Abstract As designed in the 1940s by Beuttell and Brewer, the integrating nephelometer offers a direct method of measuring light scattering by airborne particles without assumptions about particle composition, shape, or physical state. A large number of such instruments have been deployed; however, only a limited number of validation experiments have been attempted. This paper reports a set of closure experiments in which a gas-calibrated nephelometer is used to measure the scattering coefficient of laboratory-generated particles of known size and refractive index. Specifically, it evaluates the performance of a high-sensitivity, three-wavelength, total scatter/backscatter integrating nephelometer (TSI, Inc., model 3563). Sources of uncertainty associated with the gas-calibration procedure, with photon-counting statistics, and with nonidealities in wavelength and angular sensitivity are investigated. Tests with particle-free gases indicate that noise levels are well predicted by photon-counting statistics.
On the Role of Randomization in Adversarially Robust Classification

Deep neural networks are known to be vulnerable to small adversarial perturbations in test data. To defend against adversarial attacks, probabilistic classifiers have been proposed as an alternative to deterministic ones. However, literature has conflicting findings on the effectiveness of probabilistic classifiers in comparison to deterministic ones. In this paper, we clarify the role of randomization in building adversarially robust classifiers. Given a base hypothesis set of deterministic classifiers, we show the conditions under which a randomized ensemble outperforms the hypothesis set in adversarial risk, extending previous results. Additionally, we show that for any probabilistic binary classifier (including randomized ensembles), there exists a deterministic classifier that outperforms it. Finally, we give an explicit description of the deterministic hypothesis set that contains such a deterministic classifier for many types of commonly used probabilistic classifiers, i.e. randomized ensembles and parametric/input noise injection.
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations

Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms.However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains. We use XSemPLR to conduct a comprehensive benchmark study on a wide range of multilingual language models including encoder-based models (mBERT, XLM-R), encoder-decoder models (mBART, mT5), and decoder-based models (Codex, BLOOM). We design 6 experiment settings covering various lingual combinations (monolingual, multilingual, cross-lingual) and numbers of learning samples (full dataset, few-shot, and zero-shot). Our experiments show that encoder-decoder models (mT5) achieve the highest performance compared with other popular models, and multilingual training can further improve the average performance. Notably, multilingual large language models (e.g., BLOOM) are still inadequate to perform CLSP tasks. We also find that the performance gap between monolingual training and cross-lingual transfer learning is still significant for multilingual models, though it can be mitigated by cross-lingual few-shot training. Our dataset and code are available at https://github.com/psunlpgroup/XSemPLR.
IUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for multi layer summarization of clinical conversations?

Clinical conversation summarization has become an important application of Natural language Processing. In this work, we intend to analyze summarization model ensembling approaches, that can be utilized to improve the overall accuracy of the generated medical report called chart note. The work starts with a single summarization model creating the baseline. Then leads to an ensemble of summarization models trained on a separate section of the chart note. This leads to the final approach of passing the generated results to another summarization model in a multi-layer/stage fashion for better coherency of the generated text. Our results indicate that although an ensemble of models specialized in each section produces better results, the multi-layer/stage approach does not improve accuracy. The code for the above paper is available at https://github.com/dhananjay-srivastava/MEDIQA-Chat-2023-iuteam1.git
Long Sequence Hopfield Memory

Sequence memory is an essential attribute of natural and artificial intelligence that enables agents to encode, store, and retrieve complex sequences of stimuli and actions. Computational models of sequence memory have been proposed where recurrent Hopfield-like neural networks are trained with temporally asymmetric Hebbian rules. However, these networks suffer from limited sequence capacity (maximal length of the stored sequence) due to interference between the memories. Inspired by recent work on Dense Associative Memories, we expand the sequence capacity of these models by introducing a nonlinear interaction term, enhancing separation between the patterns. We derive novel scaling laws for sequence capacity with respect to network size, significantly outperforming existing scaling laws for models based on traditional Hopfield networks, and verify these theoretical results with numerical simulation. Moreover, we introduce a generalized pseudoinverse rule to recall sequences of highly correlated patterns. Finally, we extend this model to store sequences with variable timing between states' transitions and describe a biologically-plausible implementation, with connections to motor neuroscience.
Orphan Articles: The Dark Matter of Wikipedia

With 60M articles in more than 300 language versions, Wikipedia is the largest platform for open and freely accessible knowledge. While the available content has been growing continuously at a rate of around 200K new articles each month, very little attention has been paid to the accessibility of the content. One crucial aspect of accessibility is the integration of hyperlinks into the network so the articles are visible to readers navigating Wikipedia. In order to understand this phenomenon, we conduct the first systematic study of orphan articles, which are articles without any incoming links from other Wikipedia articles, across 319 different language versions of Wikipedia. We find that a surprisingly large extent of content, roughly 15\% (8.8M) of all articles, is de facto invisible to readers navigating Wikipedia, and thus, rightfully term orphan articles as the dark matter of Wikipedia. We also provide causal evidence through a quasi-experiment that adding new incoming links to orphans (de-orphanization) leads to a statistically significant increase of their visibility in terms of the number of pageviews. We further highlight the challenges faced by editors for de-orphanizing articles, demonstrate the need to support them in addressing this issue, and provide potential solutions for developing automated tools based on cross-lingual approaches. Overall, our work not only unravels a key limitation in the link structure of Wikipedia and quantitatively assesses its impact, but also provides a new perspective on the challenges of maintenance associated with content creation at scale in Wikipedia.
Blowup analysis for a quasi-exact 1D model of 3D Euler and Navier–Stokes

We study the singularity formation of a quasi-exact 1D model proposed by Hou and Li (2008 Commun. Pure Appl. Math. 61 661–97). This model is based on an approximation of the axisymmetric Navier–Stokes equations in the r direction. The solution of the 1D model can be used to construct an exact solution of the original 3D Euler and Navier–Stokes equations if the initial angular velocity, angular vorticity, and angular stream function are linear in r. This model shares many intrinsic properties similar to those of the 3D Euler and Navier–Stokes equations. It captures the competition between advection and vortex stretching as in the 1D De Gregorio (De Gregorio 1990 J. Stat. Phys. 59 1251–63; De Gregorio 1996 Math. Methods Appl. Sci. 19 1233–55) model. We show that the inviscid model with weakened advection and smooth initial data or the original 1D model with Hölder continuous data develops a self-similar blowup. We also show that the viscous model with weakened advection and smooth initial data develops a finite time blowup. To obtain sharp estimates for the nonlocal terms, we perform an exact computation for the low-frequency Fourier modes and extract damping in leading order estimates for the high-frequency modes using singularly weighted norms in the energy estimates. The analysis for the viscous case is more subtle since the viscous terms produce some instability if we just use singular weights. We establish the blowup analysis for the viscous model by carefully designing an energy norm that combines a singularly weighted energy norm and a sum of high-order Sobolev norms.
ON MAXIMALLY NON-SELF-DUAL MODULAR CATEGORIES OF SMALL DIMENSION

We prove that a non-pointed maximally non-self-dual (MNSD) modular category of Frobenius-Perron (FP) dimension less than 2025 has at most two possible types, and all these modular categories are group-theoretical. Our result shows that a non-group-theoretical MNSD modular category of smallest FP dimension may be the category of FP dimension 2025, and non-pointed MNSD modular category of smallest FP dimension is the category of FP dimension 441.
Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance

Robots assist humans in various activities, from daily living public service (e.g., airports and restaurants), and to collaborative manufacturing. However, it is risky to assume that the knowledge and strategies robots learned from one group of people can apply to other groups. The discriminatory performance of robots will undermine their service quality for some people, ignore their service requests, and even offend them. Therefore, it is critically important to mitigate bias in robot decision-making for more fair services. In this paper, we designed a self-reflective mechanism -- Fairness-Sensitive Policy Gradient Reinforcement Learning (FSPGRL), to help robots to self-identify biased behaviors during interactions with humans. FSPGRL identifies bias by examining the abnormal update along particular gradients and updates the policy network to support fair decision-making for robots. To validate FSPGRL's effectiveness, a human-centered service scenario,"A robot is serving people in a restaurant,"was designed. A user study was conducted; 24 human subjects participated in generating 1,000 service demonstrations. Four commonly-seen issues"Willingness Issue,""Priority Issue,""Quality Issue,""Risk Issue"were observed from robot behaviors. By using FSPGRL to improve robot decisions, robots were proven to have a self-bias detection capability for a more fair service. We have achieved the suppression of bias and improved the quality during the process of robot learning to realize a relatively fair model.
StructuredMesh: 3D Structured Optimization of Façade Components on Photogrammetric Mesh Models using Binary Integer Programming

The lack of fa\c{c}ade structures in photogrammetric mesh models renders them inadequate for meeting the demands of intricate applications. Moreover, these mesh models exhibit irregular surfaces with considerable geometric noise and texture quality imperfections, making the restoration of structures challenging. To address these shortcomings, we present StructuredMesh, a novel approach for reconstructing fa\c{c}ade structures conforming to the regularity of buildings within photogrammetric mesh models. Our method involves capturing multi-view color and depth images of the building model using a virtual camera and employing a deep learning object detection pipeline to semi-automatically extract the bounding boxes of fa\c{c}ade components such as windows, doors, and balconies from the color image. We then utilize the depth image to remap these boxes into 3D space, generating an initial fa\c{c}ade layout. Leveraging architectural knowledge, we apply binary integer programming (BIP) to optimize the 3D layout's structure, encompassing the positions, orientations, and sizes of all components. The refined layout subsequently informs fa\c{c}ade modeling through instance replacement. We conducted experiments utilizing building mesh models from three distinct datasets, demonstrating the adaptability, robustness, and noise resistance of our proposed methodology. Furthermore, our 3D layout evaluation metrics reveal that the optimized layout enhances precision, recall, and F-score by 6.5%, 4.5%, and 5.5%, respectively, in comparison to the initial layout.