Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Irrigants in Endodontics

Irrigation is a key part of successful root canal treatment as it fulfi ls several important mechanical, chemical and (micro) biological functions including the healing of periapical tissues. It is the only way to reach and impact those areas of the root canal wall which are not touched by mechanical instrumentation. The initial and primary endodontic treatment (rootcanal treatment) goal must be to optimize root canal disinfection and to prevent reinfection. All over the years many materials have been used to serve as endodontic irrigants particularly sodium hypochloride and EDTA. However there are also other solutions available which can be used for the purpose of irrigation. So, in this article we will narrate different types of irrigating solutions with their specifi cs, requirements, which can be used in future endodontic practice, and their advantages and limitations. Review Article Role of Irrigants in Endodontics Prabjot Kaur* Lecturer, Department of Conservative Dentistry and Endodontics, Desh Bhagat Dental College and Hospital, Mandi Gobindgarh, Punjab, India Received: 30 November, 2020 Accepted: 07 December, 2020 Published: 09 December, 2020 *Corresponding author: Dr. Prabjot Kaur, Department of Conservative Dentistry and Endodontics, Desh Bhagat Dental College and Hospital, Mandi Gobindgarh, Punjab, India, E-mail: ORCID: https://orcid.org/0000-0001-7870-9552
Economic Growth Nexus Trade Liberalization in Ethiopia: Evidence from the Johnson’s Multivariate Cointegration Analysis

Trade liberalization has long been said to influence favorably in the long run. In an attempt to confirm this theory in the context of Ethiopian economy, we adopted the Johnson’s approach for cointegration to test the long run behavior of variables entered the growth model of this paper. The test results suggest the existence of positive and significant impact of trade liberalization on economic growth in Ethiopia; its short run growth impact is estimated to be insignificant. Moreover, the impact of real effective exchange rate and foreign direct investment variables were found to be positive and highly significant in the long run. Therefore, there is a need to design and implement any policy action that could help improve the magnitude of the country’s exposure to international trade integration.
PHYSIOLOGICAL DIFFERENCES BETWEEN OFFSPRING OF LONG AND SHORT MIGRATORY BROWN TROUT (SALMO TRUTTA)

3 Introduction . 4 Salmonid migration . 4 Long and short migrating fish . 4 Present study and aim . 5 Materials and methods . 6 Catching area and test fish . 6 Equipment and setup . 7 Study protocol . 7 Metabolism . 8 Determination of blood parameters and tissue collection . 9 Field tests . 9 Cortisol and glucose . 11 Statistics . 11 Results . 12 Laboratory tests . 12 Field tests . 12 Discussion . 13 Present study . 13 Glucose, size and estimated population size . 14 Metabolic rates . 15 Implications for management and further studies . 15 Conclusion . 16 Acknowledgements . 16 References . 17 Appendix 1 . 20 Popular science summary . 20 Appendix 2 . 21 Statistics . 21
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims at learning a good policy from a batch of collected data, without extra interactions with the environment during training. However, current offline RL benchmarks commonly have a large reality gap, because they involve large datasets collected by highly exploratory policies, and the trained policy is directly evaluated in the environment. In real-world situations, running a highly exploratory policy is prohibited to ensure system safety, the data is commonly very limited, and a trained policy should be well validated before deployment. In this paper, we present a near real-world offline RL benchmark, named NeoRL, which contains datasets from various domains with controlled sizes, and extra test datasets for policy validation. We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward. The empirical results demonstrate that the tested offline RL algorithms become less competitive to the deterministic policy on many datasets, and the offline policy evaluation hardly helps. The NeoRL suit can be found at this http URL. We hope this work will shed some light on future research and draw more attention when deploying RL in real-world systems.
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments

Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artiﬁcial agents. We formalize this idea into an unsupervised reinforcement learning method called surprise minimizing reinforcement learning (SMiRL). SMiRL alternates between learning a density model to evaluate the surprise of a stimulus, and improving the policy to seek more predictable stimuli. The policy seeks out stable and repeatable situations that counteract the environment’s prevailing sources of entropy. This might include avoiding other hostile agents, or ﬁnding a stable, balanced pose for a bipedal robot in the face of disturbance forces. We demonstrate that our surprise minimizing agents can successfully play Tetris, Doom, control a humanoid to avoid falls, and navigate to escape enemies in a maze without any task-speciﬁc reward supervision. We further show that SMiRL can be used together with standard task rewards to accelerate reward-driven learning
An optimum design for a time-of-flight neutron diffractometer for measuring engineering stresses

A method is described for optimizing the design of a time-of-flight neutron diffractometer designed to measure lattice parameters. Such diffractometers are now used extensively by engineers and materials scientists for measuring strain within metallic and ceramic components. The method presented relies on the identification of a figure of merit (FOM) that accurately describes the performance of such an instrument. For the first time, an FOM for an instrument exhibiting non-Gaussian peak shapes is described, and the methods by which this FOM may be maximized are described. Although the instrument described is based on the time-of-flight technique, the FOM derived may equally well be used to optimize a reactor-based instrument. While measuring peak position is a straightforward example, it is shown that similar figures of merit may be found for other peak shape parameters, and thus other types of instrumentation.
Encoding and Decoding Construction D' Lattices for Power-Constrained Communications

This paper focuses on the encoding and decoding of Construction D' coding lattices that can be used with shaping lattices for power-constrained channels. Two encoding methods and a decoding algorithm for Construction D' lattices are given. A design of quasi-cyclic low-density parity-check (QC-LDPC) codes to form Construction D' lattices is presented. This allows construction of nested lattice codes which are good for coding, good for shaping, and have low-complexity encoding and decoding. Numerical results using $E_{8},\ BW_{16}$ and Leech lattices for shaping a Construction D' lattice indicate that the shaping gains 0.65 dB, 0.86 dB and 1.03 dB are preserved, respectively.
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-Domain Gait Recognition

Gait, i.e., the movement pattern of human limbs during locomotion, is a promising biometrie for identification of persons. Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario — unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapt it to an unlabeled dataset. Due to the domain shift and class gap, directly applying a model trained on one source dataset to other target datasets usually obtains very poor results. Therefore, this paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. To learn effective prior knowledge for gait representation, we first adopt a backbone network pre- trained on the labeled source data in a supervised manner. Then we design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. During training, the class consistency indicator is adopted to select confident neighborhoods of samples based on their entropy measurements. Moreover, we explore a high- entropy-first neighbor selection strategy, which can effectively transfer prior knowledge to the target domain. Our method achieves the state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
Mysterious, variable, and extremely hot: White dwarfs showing ultra-high excitation lines

About 10% of all stars exhibit absorption lines of ultra-high excited (UHE) metals (e.g. OVIII) in their optical spectra when entering the white dwarf cooling sequence. The recent discovery of a both spectroscopic and photometric variable UHE white dwarf led to the speculation that the UHE lines might be created in a shock-heated circumstellar magnetosphere. We investigate (multi-band) light curves from several ground- and space-based surveys of all 16 currently known UHE white dwarfs (including one newly discovered) and eight white dwarfs that show only the HeII line problem, as both phenomena are believed to be connected. We find that $75^{+8}_{-13}$% of the UHE white dwarfs, and $75^{+9}_{-19}$% of the HeII line problem white dwarfs are significantly photometrically variable, with periods ranging from 0.22d to 2.93d and amplitudes from a few tenth to a few hundredth mag. The high variability rate is in stark contrast to the variability rate amongst normal hot white dwarfs (we find $9^{+4}_{-2}$%), marking UHE and HeII line problem white dwarfs as a new class of variable stars. The period distribution of our sample agrees with both the orbital period distribution of post-common envelope binaries and the rotational period distribution of magnetic white dwarfs if we assume that the objects in our sample will spin-up as a consequence of further contraction. The lack of increasing photometric amplitudes towards longer wavelengths, as well as the non-detection of optical emission lines arising from the highly irradiated face of a hypothetical secondary in the optical spectra of our stars, makes it seem unlikely that an irradiated late type companion is the origin of the photometric variability. Instead, we believe that spots on the surfaces of these stars and/or geometrical effects of circumstellar material might be responsible. (abridged)
Quantum magnetism on small-world networks

While classical spin systems in random networks have been intensively studied, much less is known about quantum magnets in random graphs. Here, we investigate interacting quantum spins on small-world networks, building on mean-field theory and extensive quantum Monte Carlo simulations. Starting from one-dimensional (1D) rings, we consider two situations: all-to-all interacting and long-range interactions randomly added. The effective infinite dimension of the lattice leads to a magnetic ordering at finite temperature $T_\mathrm{c}$ with mean-field criticality. Nevertheless, in contrast to the classical case, we find two distinct power-law behaviors for $T_\mathrm{c}$ versus the average strength of the extra couplings. This is controlled by a competition between a characteristic length scale of the random graph and the thermal correlation length of the underlying 1D system, thus challenging mean-field theories. We also investigate the fate of a gapped 1D spin chain against the small-world effect.
Using Structural and Semantic Information to Identify Software Components

Component Based Software Engineering (CBSE) seeks to promote the reuse of software by using existing software modules into the development process. However, the availability of such a reusable component is not immediate and is costly and time consuming. As an alternative, the extraction from preexisting OO software can be considered.In this work, we evaluate two community detection algorithms for the task of software components identification. Considering ‘components’ as ‘communities’, the aim is to evaluate how independent, yet cohesive, the components are when extracted by structurally informed algorithms.We analyze 412 Java systems and evaluate the cohesion of the extracted communities using four document representation techniques. The evaluation aims to find which algorithm extracts the most semantically cohesive, yet separated communities.The results show a good performance in both algorithms, however, each has its own strengths. Leiden extracts less cohesive, but better separated, and better clustered components that depend more on similar ones. Infomap, on the other side, creates more cohesive, slightly overlapping clusters that are less likely to depend on other semantically similar components.
BembaSpeech: A Speech Recognition Corpus for the Bemba Language

We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91%, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language.
Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming

One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance. The universal solution of a fixed bitrate ladder is inadequate in ensuring a high quality of user experience without re-buffering or introducing annoying compression artifacts. However, a content-tailored solution, based on extensively encoding across all resolutions and over a wide quality range is highly expensive in terms of computational, financial, and energy costs. Inspired by this, we propose an approach that exploits machine learning to predict a content-optimized bitrate ladder for on-demand video services. The method extracts spatio-temporal features from the uncompressed content, trains machine-learning models to predict the Pareto front parameters and, based on that, builds the ladder within a defined bitrate range. The method has the benefit of significantly reducing the number of encodes required per sequence. The presented results, based on 100 HEVC-encoded sequences, demonstrate a reduction in the number of encodes required when compared to an exhaustive search and an interpolation-based method, by 89.06% and 61.46%, respectively, at the cost of an average Bjøntegaard Delta Rate difference of 1.78% compared to the exhaustive approach. Finally, a hybrid method is introduced that selects either the proposed or the interpolation-based method depending on the sequence features. This results in an overall 83.83% reduction of required encodings at the cost of an average Bjøntegaard Delta Rate difference of 1.26%.
Enhancement of the Adsorption of Co(II) and Ni(II) Ions onto Peanut Hulls through Esterification Using Citric Acid

Peanut hulls were modified chemically by esterification with citric acid under conditions which yielded hulls rich in carboxyl content. The esterified products thus obtained were used in the removal of Co(II) and Ni(II) ions from aqueous solutions. The factors affecting the esterification reaction and the adsorption of the metal cations onto the peanut hulls before and after esterification were thoroughly investigated. The results obtained highlighted the following conclusions. The optimum conditions for esterification involve the treatment of peanut hulls (1 g) with 12.4 mmol citric acid in the presence of a very small amount of water for 2 h at 140°C. Similarly, the optimum pH for the adsorption of cobalt(II) ions onto the peanut hulls citrate was 7.0. The adsorption capacity, qe (mg/g), of Co(II) ions onto the peanut hulls citrate increased on increasing the extent of esterification, expressed as mequiv. —COOH/100 g sample. The maximum values for the adsorption capacity, qmax, were 28.7 mg/g and 270.3 mg/g on native and peanut hulls citrate, respectively. The qmax values for of Ni(II) ion adsorption were 5 mg/g and 175.4 mg/g on native and peanut hulls citrate, respectively. The equilibrium data were well fitted by the Langmuir and Freundlich adsorption models and showed the large capacity of peanut hulls citrate in the removal of Co(II) and Ni(II) ions from aqueous solutions.
A detailed study of X-ray cavities in the intracluster environment of the cool core cluster Abell 3017

We present a detailed analysis of a cool-core galaxy cluster Abell 3017, at a redshift of z = 0.219, which has been identified to be merging with its companion cluster Abell 3016. This study has made use of X-ray (Chandra), UV (GALEX), optical (ESO/VLT), mid-infrared (WISE) and radio uGMRT observations of this cluster. Using various image processing techniques, such as unsharp masking, 2-d fits using Beta models, contour binning and the use of surface brightness profiles, we show the existence of a pair of X-ray cavities, at a projected distance of ∼20(70 kpc) and ∼16(57 kpc), respectively from the core of Abell 3017. We also detect an excess of X-ray emission located at 25∼(88 kpc) south of the centre of Abell 3017, is likely due to the bulk motions in the ICM either by gas sloshing or ram-pressure striping due to a merger. We find that the radio lobes are responsible for the observed X-ray cavities detected in this system. The lower values of mid-IR WISE colour [W1-W2] and [W2-W3] imply that the central BCG of Abell 3017 is a star-forming galaxy. The current star formation rate of the central BCG, estimated from the Hα and GALEX FUV luminosities, are equal to be ∼ 5.06± 0.78 M⊙yr −1 and ∼ 9.20± 0.81 M⊙yr , respectively. We detect, for the first time, a radio phoenix ∼150kpc away from the radio core, with a spectral index of (α6−1.8). We also report the detection of Paα emission in this cluster using ESO VLT SINFONI imaging data.
Behavioral Economics Approach to Interpretable Deep Image Classification. Rationally Inattentive Utility Maximization Explains Deep Image Classification

Are deep convolutional neural networks (CNNs) for image classiﬁcation consistent with utility maximization behavior with information acquisition costs? This paper demonstrates the remarkable result that a deep CNN behaves equivalently (in terms of necessary and sufﬁcient conditions) to a rationally inattentive utility maximizer, a model extensively used in behavioral economics to explain human decision making. This implies that a deep CNN has a parsimonious representation in terms of simple intuitive human-like decision parameters, namely, a utility function and an information acquisition cost. Also the reconstructed utility function that rationalizes the decisions of the deep CNNs, yields a useful preference order amongst the image classes (hypotheses). All results in this paper are available on GitHub and completely reproducible.
Improving Visual Reasoning by Exploiting The Knowledge in Texts

This paper presents a new framework for training image-based classiﬁers from a combination of texts and images with very few labels. We consider a classiﬁcation framework with three modules: a backbone, a relational reasoning component, and a classiﬁcation component. While the backbone can be trained from unlabeled images by self-supervised learning, we can ﬁne-tune the relational reasoning and the classiﬁcation components from external sources of knowledge instead of annotated images. By proposing a transformer-based model that creates structured knowledge from textual input, we enable the utilization of the knowledge in texts. We show that, compared to the supervised baselines with 1% of the annotated images, we can achieve ∼ 8x more accuracte re-sults in scene graph classiﬁcation, ∼ 3x in object classiﬁcation, and ∼ 1.5x in predicate classiﬁcation.
Core Imaging Library - Part I: a versatile Python framework for tomographic imaging

We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.
Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals.