Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Reinforcing Agents on the Mechanical Properties of Denture Base Resin: A Systematic Review

Knowledge about the influence of fillers in denture base resin is vague. This systematic review aimed to report the reinforcing effect of fillers on the mechanical properties of denture base resin by following PRISMA guidelines. Two electronic databases (Pubmed/Medline & Web of Science) were searched for articles using the keywords: fibers in denture base, fillers in denture base, and reinforcement of denture base. Laboratory studies complying with the inclusion criteria were reviewed according to the set protocol. The established focus question was: “Do reinforcing fillers positively influence the mechanical properties of polymethyl methacrylate (PMMA) heat polymerized denture base material? ” A total of twenty-nine relevant papers qualified for final inclusion. Of these, 24 were determined to have a moderate risk of bias. Micron or nano-sized metal/metal oxides particles and glass fibers were the frequently used reinforcing agents. The trend of evaluating fractural strength (FS) was common. Most of the studies limited the use of reinforcing agents up to 5 wt. %. FS, fracture toughness (FT), and impact strength (IS) tend to increase if the fillers are chemically bonded and well-dispersed in denture base resin. Though fillers with a higher elastic modulus increase the hardness of the reinforced denture base resin, they compromise other mechanical properties. Well-dispersed lower filler loading PMMA denture base resin can enhance the FS, FT, and other related mechanical properties.
Evaluation of the Shaping Ability of Three Thermally Treated Nickel–Titanium Endodontic Instruments on Standardized 3D-printed Dental Replicas Using Cone-Beam Computed Tomography

Background and Objectives: The aim of the present study is to compare the efficacy of three root canal preparation systems in the shaping of 3D-printed root canal replicas of single rooted teeth. Materials and Methods: Sixty 3D-printed root canal replicas were produced and divided into three groups, each consisting of twenty samples. Each group was shaped with a different instrument: Reciproc Blue R25/08 (VDW GmbH, Munich, Gemany), WaveOne Gold Primary 25/07 (Dentsply Sirona, Ballaigues, Switzerland), and ProTaper Gold F2 25/08 (Denstply Sirona). To ensure the reproducibility of pre- and post-operative CBCT images of the root canals, the endodontic printed replicas were placed in a mould of silicon impression material. A cone-beam computed tomography (CBCT) software was used to compare pre- and post-instrumentation images collected at three levels of the root canal length: 3, 6, and 9 mm from the apical foramen. The amount of transportation, centring ability, and curvature angle after shaping were evaluated for each system. The results were statistically analysed and compared using one-way analysis of variance (ANOVA). Results: Regarding the transportation of the root canal after shaping, significant differences between groups at 3 mm (p = 0.010721) and 6 mm (p = 0.000046) were recorded in the mesio-distal direction, while in the bucco-lingual significant differences were only observed at 6 mm (p = 0.000554). Reciproc Blue removed more dentin from the mesial and buccal wall of the root canal. When evaluating the centring ability of the three systems, significant differences were observed between the groups at the level of 9 mm (p = 0.037258) in the mesio-distal direction, and at the level of 6 mm (p = 0.038197) in the bucco-lingual direction. Significant differences of the canal curvature angle after shaping were also observed between groups (p = 0.000001). Reciproc Blue straightened the curvature the most, while ProTaper Gold the least. Conclusions: All systems produced minor root canal transportation. No instrument was able to achieve a perfect centring preparation of the root canal. All systems produced a small degree of root canal straightening.
business and technological Perspectives About Mobile Commerce

This paper presents a research in process about the privacy and security issues regarding m-commerce (mobile commerce). In this research, privacy issues are viewed as the business perspectives and security issues are viewed as the technological perspectives. M-commerce is an alternative way for customers to be reached. The questions are: is customer privacy acceptable in the m-commerce environment? and is security (reliability and trustfulness of the mobile systems) acceptable in the m-commerce environment? A pilot study was conducted to University students to observe their opinion regarding those questions. The intention of this research is to conduct a formal survey that present possible strategic solutions to improve m-commerce environment. Findings from the pilot study suggests that students would like to be informed about the lowest prices of their favorite products and services and that actual stability of the system is acceptable.
Learning of Parameters in Behavior Trees for Movement Skills

Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero explanation when they fail to perform the task. For these reasons, the adoption of RL in industrial settings is not common. Behavior Trees (BTs), on the other hand, can provide a policy representation that a) supports modular and composable skills, b) allows for easy interpretation of the robot actions, and c) provides an advantageous low-dimensional parameter space. In this paper, we present a novel algorithm that can learn the parameters of a BT policy in simulation and then generalize to the physical robot without any additional training. We leverage a physical simulator with a digital twin of our workstation, and optimize the relevant parameters with a black-box optimizer. We showcase the efficacy of our method with a 7-DOF KUKAiiwa manipulator in a task that includes obstacle avoidance and a contact-rich insertion (peg-in-hole), in which our method outperforms the baselines.
Towards "Zero-buffer" Datacenter Networks

In this paper, we investigate the possibility of building a data center network (DCN) with simplified zero-buffer switches. It exposes a stringent requirement to the traffic control scheme to ensure perfect collision-free between packets. We find it is infeasible, if not impossible, to make a large-scale DCN completely zero buffering. We take a step back and propose Fastpod, a new data center architecture that enables zero buffering in pod scale, which can be expanded with buffered devices. With time synchronization, Fastpod avoids collisions by performing fine-grained centralized control on data packets and coordination-free scheduling on control packets. It leverages an optimistic sending mechanism to reduce the latency overhead induced by the centralized control. Our preliminary simulation results indicate that Fastpod successfully meets the requirement of collision-free in the zero-buffer pod and maintains similar performance with conventional buffered DCN. It follows the trend of simplifying the network by pushing the functionalities to the edge. Fastpod explores an extreme end of the design spectrum and can be treated as a thought experiment to raise discussion in the community of data center networks.
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data

There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significant challenge for building efficient multi-label classification algorithms. In this paper, we tackle this problem by proposing a minority class oversampling scheme, UCLSO, which integrates Unsupervised Clustering and Label-Specific data Oversampling. Clustering is performed to find out the key distinct and locally connected regions of a multi-label dataset (irrespective of the label information). Next, for each label, we explore the distributions of minority points in the cluster sets. Only the minority points within a cluster are used to generate the synthetic minority points that are used for oversampling. Even though the cluster set is the same across all labels, the distributions of the synthetic minority points will vary across the labels. The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently. Experiments using 12 multi-label datasets and several multi-label algorithms show that the proposed method performed very well compared to the other competing algorithms.
The forgotten preconditions for a well-functioning internet

Abstract For decades, proponents of the Internet have promised that it would one day provide a seamless way for everyone in the world to communicate with each other, without introducing new boundaries, gatekeepers, or power structures. What happened? This article explores the system-level characteristics of a key design feature of the Internet that helped it to achieve widespread adoption, as well as the system-level implications of certain patterns of use that have emerged over the years as a result of that feature. Such patterns include the system-level acceptance of particular authorities, mechanisms that promote and enforce the concentration of power, and network effects that implicitly penalize those who do not comply with decisions taken by privileged actors. We provide examples of these patterns and offer some key observations, toward the development of a general theory of why they emerged despite our best efforts, and we conclude with some suggestions on how we might mitigate the worst outcomes and avoid similar experiences in the future.
Classical State Masking over a Quantum Channel

Transmission of classical information over a quantum state-dependent channel is considered, when the encoder can measure channel side information (CSI) and is required to mask information on the quantum channel state from the decoder. In this quantum setting, it is essential to conceal the CSI measurement as well. A regularized formula is derived for the masking equivocation region, and a full characterization is established for a class of measurement channels.
The extraordinary frequency pattern variation in δ Scuti stars

Inspection of the periodograms of TESS delta Scuti stars indicates that there is little, if any, similarity between the frequencies of stars in the same region of the H-R diagram. This is difficult to understand because pulsation models predict that stars with similar physical parameters should have similar frequencies. To investigate the problem, a quantitative measure of similarity between frequency patterns is described. When applied to non-adiabatic pulsation models with similar temperatures and luminosities, a strong correlation is found between the frequency patterns, as expected. The correlation increases when rotational frequency splitting is included. When applied to observations, no correlation can be found, confirming the impression from visual inspection. It seems that each star has its own unique frequency pattern, unrelated to its position in the H-R diagram. This presents a problem in our understanding of stellar pulsation. The existence of a period-luminosity law and the effect of combination frequencies in delta Scuti stars is briefly discussed.
Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)

Digital learning platforms enable students to learn on a flexible and individual schedule as well as providing instant feedback mechanisms. The field of STEM education requires students to solve numerous training exercises to grasp underlying concepts. It is apparent that there are restrictions in current online education in terms of exercise diversity and individuality. Many exercises show little variance in structure and content, hindering the adoption of abstraction capabilities by students. This thesis proposes an approach to generate diverse, context rich word problems. In addition to requiring the generated language to be grammatically correct, the nature of word problems implies additional constraints on the validity of contents. The proposed approach is proven to be effective in generating valid word problems for mathematical statistics. The experimental results present a tradeoff between generation time and exercise validity. The system can easily be parametrized to handle this tradeoff according to the requirements of specific use cases.
The Baltimore Oriole’s Nest: Cool Winds from the Inner and Outer Parts of a Star-forming Galaxy at z = 1.3

Strong galactic winds are ubiquitous at z ≳ 1. However, it is not well-known where inside galaxies these winds are launched from. We study the cool winds (∼104 K) in two spatial regions of a massive galaxy at z = 1.3, which we nickname the “Baltimore Oriole’s Nest. ” The galaxy has a stellar mass of 1010.3±0.3 M ⊙, is located on the star-forming main sequence, and has a morphology indicative of a recent merger. Gas kinematics indicate a dynamically complex system with velocity gradients ranging from 0 to 60 km s−1. The two regions studied are: a dust-reddened center (Central region), and a blue arc at 7 kpc from the center (Arc region). We measure the Fe ii and Mg ii absorption line profiles from deep Keck/DEIMOS spectra. Blueshifted wings up to 450 km s−1 are found for both regions. The Fe ii column densities of winds are 1014.7±0.2 cm−2 and 1014.6±0.2 cm−2 toward the Central and Arc regions, respectively. Our measurements suggest that the winds are most likely launched from both regions. The winds may be driven by the spatially extended star formation, the surface density of which is around 0.2 M ⊙ yr−1 · kpc−2 in both regions. The mass outflow rates are estimated to be 4 M ⊙ yr−1 and 3 M ⊙ yr−1 for the Central and Arc regions, with uncertainties of one order of magnitude or more. The findings of this work and a few previous studies suggest that the cool galactic winds at z ≳ 1 might be commonly launched from the entire spatial extents of their host galaxies, due to extended galaxy star formation.
Attention Gate in Traffic Forecasting

Because of increased urban complexity and growing populations, more and more challenges about predicting city-wide mobility behavior are being organized. Traffic Map Movie Forecasting Challenge 2020 is secondly held in the competition track of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS). Similar to Traffic4Cast 2019, the task is to predict traffic flow volume, average speed in major directions on the geographical area of three big cities: Berlin, Istanbul, and Moscow. In this paper, we apply the attention mechanism on U-Net based model, especially we add an attention gate on the skip-connection between contraction path and expansion path. An attention gates filter features from the contraction path before combining with features on the expansion path, it enables our model to reduce the effect of non-traffic region features and focus more on crucial region features. In addition to the competition data, we also propose two extra features which often affect traffic flow, that are time and weekdays. We experiment with our model on the competition dataset and reproduce the winner solution in the same environment. Overall, our model archives better performance than recent methods.
Multi-task and Multi-corpora Training Strategies to Enhance Argumentative Sentence Linking Performance

Argumentative structure prediction aims to establish links between textual units and label the relationship between them, forming a structured representation for a given input text. The former task, linking, has been identified by earlier works as particularly challenging, as it requires finding the most appropriate structure out of a very large search space of possible link combinations. In this paper, we improve a state-of-the-art linking model by using multi-task and multi-corpora training strategies. Our auxiliary tasks help the model to learn the role of each sentence in the argumentative structure. Combining multi-corpora training with a selective sampling strategy increases the training data size while ensuring that the model still learns the desired target distribution well. Experiments on essays written by English-as-a-foreign-language learners show that both strategies significantly improve the model’s performance; for instance, we observe a 15.8% increase in the F1-macro for individual link predictions.
Improving Model Predictive Path Integral using Covariance Steering

—This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (CS) theory to obtain a robust controller for general nonlinear systems. The proposed Covariance-Controlled Model Predictive Path Integral (CC-MPPI) controller addresses the performance degradation observed in some MPPI implementations owing to unexpected disturbances and uncertainties. Namely, in cases where the environment changes too fast or the simulated dynamics during the MPPI rollouts do not capture the noise and uncertainty in the actual dynamics, the baseline MPPI implementation may lead to divergence. The proposed CC-MPPI controller avoids divergence by controlling the dispersion of the rollout trajectories at the end of the prediction horizon. Furthermore, the CC-MPPI has adjustable trajectory sampling distributions that can be changed according to the environment to achieve efﬁcient sampling. Numerical examples using a ground vehicle navigating in challenging environments demonstrate the proposed approach.
Estimating Angle of Arrival (AoA) of multiple Echoes in a Steering Vector Space

Consider a microphone array, such as those present in Amazon Echos, conference phones, or self-driving cars. One of the goals of these arrays is to decode the angles in which acoustic signals arrive at them. This paper considers the problem of estimating K angle of arrivals (AoA), i.e., the direct path's AoA and the AoA of subsequent echoes. Significant progress has been made on this problem, however, solutions remain elusive when the source signal is unknown (such as human voice) and the channel is strongly correlated (such as in multipath settings). Today's algorithms reliably estimate the direct-path-AoA, but the subsequent AoAs diverge in noisy real-world conditions. We design SubAoA, an algorithm that improves on the current body of work. Our core idea models signal in a new AoA sub-space, and employs a cancellation approach that successively cancels each AoA to decode the next. We explain the behavior and complexity of the algorithm from the first principles, simulate the performance across a range of parameters, and present results from real-world experiments. Comparison against multiple existing algorithms like GCC-PHAT, MUSIC, and VoLoc shows increasing gains for the latter AoAs, while our computation complexity allows real-time operation. We believe progress in multi-AoA estimation is a fundamental building block to various acoustic and RF applications, including human or vehicle localization, multi-user separation, and even (blind) channel estimation.
Validating human driver models for interaction-aware automated vehicle controllers: A human factors approach

A major challenge for autonomous vehicles is interacting with other traﬃc participants safely and smoothly. A promising approach to handle such traﬃc interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle’s actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this paper, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workﬂow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workﬂow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model’s operational behavior was inconsistent with observed human behavior. The case study illustrates that a principled evaluation workﬂow is useful and needed. We believe that our workﬂow will support the development of appropriate driver models for future automated vehicles.
News Consumption in Time of Conflict: 2021 Palestinian-Israel War as an Example

This paper examines news consumption in response to a major polarizing event, and we use the May 2021 Israeli-Palestinian conflict as an example. We conduct a detailed analysis of the news consumption of more than eight thousand Twitter users who are either pro-Palestinian or pro-Israeli and authored more than 29 million tweets between January 1 and August 17, 2021. We identified the stance of users using unsupervised stance detection. We observe that users may consume more topically-related content from foreign and less popular sources, because, unlike popular sources, they may reaffirm their views, offer more extreme, hyper-partisan, or sensational content, or provide more in depth coverage of the event. The sudden popularity of such sources may not translate to longer-term or general popularity on other topics.
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction

Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning event-event relations, which can then be used for commodity price prediction. The events found in commodity news exhibit characteristics different from generic events, hence posing a unique challenge in event extraction using existing methods. This paper proposes an effective use of Graph Convolutional Networks(GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event ex-traction in commodity news. The event ex-traction model is trained using feature embed-dings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commodity news corpus. Experimental results show the efficiency of the proposed solution, which out-performs existing methods with F1 scores as high as 0.90. Furthermore, our pre-trained language model outperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument roles classification. For the goal of re-producibility, the code and trained models are made publicly available.