Miracle-dz/UniFAR-FTUs huggingface dataset:
Negotiating the bends: An endodontic management of curved canals – A case series

Negotiating the dilacerated and S-shaped roots often present a challenging situation in their Endodontic treatment. An insight of canal anatomy, coronal preflaring, precurving of all the hand instruments and use of smaller number files facilitates easy negotiation of canal curvature and maintenance of the shape without any procedural mishaps. All hand files are used in present cases with balanced force technique which has advantages of less extrusion of debris, less iatrogenic errors, and maintenance of instrument centrally. A thorough assessment of preoperative radiographs coupled with careful approach yielded into a safe and a successful endodontic treatment of such teeth. This case series presents two such interesting cases of endodontic management of curved canals.
The mediating role of outsourcing in the relationship between speed, flexibility and performance: a Saudi Arabian study

Supply chain operatives that want to grow and develop competitive advantage must be able to adapt their operations to the dynamic market variables and effectively meet their customers' demand. Supply chain competitive advantage can be attained by excelling at quality, responsiveness, innovativeness and skilled workforce. The preliminary findings from the tested hypothesised theoretical model suggest that despite being affected by a skills shortage, Saudi Arabian supply chain operatives tend to keep their logistics operations in-house, to achieve speed and flexibility, rather than outsourcing specialised 3PL firms for more efficient execution. The lack of outsourcing practices seems to go against international trends, where an increasing amount of partnerships are being formed between companies with limited supply chain skills and 3PL firms with the experience to manage these new challenges. The heavy reliance of Saudi supply chain operatives on do-it-yourself strategies is negatively impacting on service levels, competitive advantage, profitability and performance.
Automatic screw tightening apparatus

Less damaging to the inner wall of the transport pipe, it is to stabilize the position of the screw to stabilize the screw to the driver bit is supplied, and provide an automatic screw clamping device to shorten the time of the fastening screw. Automatic present invention is the transfer screw by a screw feed mechanism using air suction to the screw mechanism from a screw feed mechanism, and tightening the screw into the threaded target portion combines the screw to the front end driver bit portion of the screw mechanism to a desired a screw tightening device, the screw feed mechanism, the screw head portion such that the head comprising a discharge for transferring into a screw feed tube of the transfer mechanism the screw, and wherein the discharge unit is provided with the times East Rock, the rotating East Rock tip of the screwing of and added installation supply head close to the front end driver bit portion of the apparatus, the supply head portion is installed to be movable to the retracted position away from the close location and the driver bit portion to close the front end driver bit in the screw tightening mechanism portion, the close position is connected to the front end portion of the driver bit in the screw-down mechanism, the head part by suction screw Characterized in that the part to couple to the driver bit.
Damage identification using modal strains identified from operational fiber-optic Bragg grating data

Vibration-based structural health monitoring of civil structures relies on the repeated identification of dynamic structural characteristics of the structure from output-only vibration data. Natural frequencies and displacement mode shapes are the most commonly employed dynamic characteristics; yet their sensitivity to local damage of moderate severity is rather low with respect to their sensitivity to other factors such as temperature, necessitating data normalization. Strain mode shapes offer a higher sensitivity to local damage, but their accurate identification in a dense grid is challenging given the very small dynamic strain levels that are encountered under ambient excitation. In this article, a method is presented for tackling this challenge. It consists of three stages. First, fiber-optic Bragg grating strain sensors are attached to the structure and interrogated with a tunable laser performing a wavelength sweep. In this way, the measured strain amplitudes have the required accuracy but synchronization errors are introduced between the different Bragg sensors. Second, a modal analysis is performed on the dynamic strain data using an accurate parametric system identification technique. This is followed by a synchronization step which compensates for the delays introduced by the wavelength sweep. Finally, the synchronized strain mode shapes are employed as damage-sensitive features, either directly or via a newly proposed quantity, the top-to-bottom strain ratio. The method is validated by progressive damage testing of a complex, prestressed concrete “roof” beam, reinforced with steel fibers. It is observed that the proposed method can identify both the presence and the location of the damage in a relatively early stage.
Автоматизация создания генераторов проблемно-ориентированных программ

—Problem-oriented program generation is an approach to improving performance for certain class of GPGPU (General Purpose computing for Graphical Processing Units) programs. Creating problem-oriented program generators manually is a time-consuming and difficult task, one that requires the programmer to possess a certain degree of insight in the problem being solved by said program. This paper covers an approach to automating the creation of problem-oriented program generators. Meta-generator is introduced as a program creating said generators from GPU program’s source code. The key steps that meta-generator performs in the course of program analysis are discussed along with technologies most suitable for implementing on each step. Lastly, a comparison between proposed approach and similar existing means to enhancing software performance was made, outlining their key differences.
Reduction of the head-up pitching moment of small quad-rotor unmanned aerial vehicles in uniform flow

Multi-rotor unmanned aerial vehicles are unstable in headwind because of nose-up pitching moment generation and thrust degradation. Reducing the pitching moment generation could enhance the tolerance of the unmanned aerial vehicles to wind and improve cruise flight speed. In this study, a canted rotor configuration was proposed to reduce the moment in uniform flow and examined. The objective of the study is to evaluate the effect of moment reduction by canted rotors. First, flow interactions between rotors of the quad-rotor were visualized using a smoke wire method. Second, the moment reduction by canted rotors was estimated based on isolated rotor performance. Third, the moment of the quad-rotor varying the body pitching angle was examined using a wind tunnel. At an 8 m/s wind, when the body pitching angle is horizontal, slanting the rotor by 20° to outside the body degraded the moment by 26% compared with the parallel rotor configuration.
upper-ocean thermo-dynamical structure modeling with combined effects of surface waves and M 2 internal tides on vertical mixing: a case study for the Indian Ocean”

The main result of this study is to investigate the contribution of surface waveand internal tides-induced vertical mixing in the upper ocean of the Indian Ocean. By adding three mixing schemes (vertical diffusive terms) into ocean circulation model, namely non-breaking surface-wave-generated turbulent mixing, the mixing induced by the wave transport flux residue, and the internal-tide-generated turbulent mixing schemes, the role of three vertical mixing schemes is quantified by switching off each diffusive term. Especially, the surface wave mainly improves the vertical mixing in the sea surface while the internal tide mainly contributes to the vertical mixing in the ocean interior. Improvement of upper ocean temperature structure is observed when all three schemes are combined.
Inertia Adequacy in Transient Stability Models for Synthetic Electric Grids

If a disturbance rocks a low-inertia power system, the frequency decline may be too rapid to arrest before it triggers undesirable responses from generators and loads. In the worst case, this instability could lead to blackout and major equipment damage. Electric utilities, to combat this, study inertia adequacy in systems that are particularly vulnerable. This process, involving detailed transient simulations, usually leads to a notion of a system-wide inertia floor. Ongoing questions in this analysis are in how to set the inertial floor and to what extent the location of frequency control resources matters. This paper proposes a new analysis technique that quantifies theoretical locational rate of change of frequency (ROCOF) as a computationally efficient screening algorithm scalable to large systems. An additional challenge in moving this area forward is the lack of high-quality, public benchmark dynamics cases. This paper presents a synthetic case for such purposes and a methodology for validation, to ensure that it is well suited to inertia adequacy studies to improve electric grid performance.
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark

Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would largely restricts their power on versatile images. To exploit the potential power of decomposition mechanism, in this paper, we generalize it from the image domain to the broader feature domain. To this end, we propose a lightweight Feature Decomposition Aggregation Network (FDAN). In particular, we design a Feature Decomposition Block (FDB) to achieve learnable separation of detail and base feature maps, and develop a Hierarchical Feature Decomposition Group by cascading FDBs for powerful multi-level feature decomposition. Moreover, to better evaluate the comparison methods, we collect a large-scale dataset for joint SR-ITM, i.e., SRITM-4K, which provides versatile scenarios for robust model training and evaluation. Experimental results on two benchmark datasets demonstrate that our FDAN is efficient and outperforms state-of-the-art methods on joint SR-ITM. The code of our FDAN and the SRITM-4K dataset are available at https://github.com/CS-GangXu/FDAN.
A Survey on Hyperlink Prediction

As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks and social communication networks to protein-protein interaction networks. In this article, we provide a systematic and comprehensive survey on hyperlink prediction. We adopt a classical taxonomy from link prediction to classify the existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling

This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property of knowledge-intensive generation: The connection of query-answer pairs to items in the knowledge base. We filter training examples via a threshold of confidence on the relevance labels, whether a pair is answerable by the knowledge base or not. We train a single Fusion-in-Decoder (FiD) generator on seven combined tasks of the KILT benchmark. The experimental results suggest that our simple yet effective approach substantially improves competitive baselines on two strongly imbalanced tasks; and shows either smaller improvements or no significant regression on the remaining tasks. Furthermore, we demonstrate our multi-task training with relevance label sampling scales well with increased model capacity and achieves state-of-the-art results in five out of seven KILT tasks.
Style Interleaved Learning for Generalizable Person Re-Identification

Domain generalization (DG) for person re-identification (ReID) is a challenging problem, as access to target domain data is not permitted during the training process. Most existing DG ReID methods update the feature extractor and classifier parameters based on the same features. This common practice causes the model to overfit to existing feature styles in the source domain, resulting in sub-optimal generalization ability on target domains. To solve this problem, we propose a novel style interleaved learning (IL) framework. Unlike conventional learning strategies, IL incorporates two forward propagations and one backward propagation for each iteration. We employ the features of interleaved styles to update the feature extractor and classifiers using different forward propagations, which helps to prevent the model from overfitting to certain domain styles. To generate interleaved feature styles, we further propose a new feature stylization approach. It produces a wide range of meaningful styles that are both different and independent from the original styles in the source domain, which caters to the IL methodology. Extensive experimental results show that our model not only consistently outperforms state-of-the-art methods on large-scale benchmarks for DG ReID, but also has clear advantages in computational efficiency.
Linearized Physics-Based Lithium-Ion Battery Model for Power System Economic Studies

—This paper proposes the linearized physics-based model of a lithium-ion battery that can be incorporated into the optimization framework for power system economic studies. The proposed model is a linear approximation of the single particle model and it allows to characterize dynamics of the physical processes inside the battery that impact the battery operation. There is a need for such model as a simplistic power-energy model that is widely employed in operation and planning studies with the lithium-ion battery energy storage system (LIBESS) results in infeasible operation and misleading economic assessment. The proposed linearized model is computationally beneﬁcial compared with a recently used nonlinear physics-based model. The energy arbitrage application is used to assess the advantages of the proposed model over a simple power-energy model.
NeuralGrasps: Learning Implicit Representations for Grasps of Multiple Robotic Hands

We introduce a neural implicit representation for grasps of objects from multiple robotic hands. Different grasps across multiple robotic hands are encoded into a shared latent space. Each latent vector is learned to decode to the 3D shape of an object and the 3D shape of a robotic hand in a grasping pose in terms of the signed distance functions of the two 3D shapes. In addition, the distance metric in the latent space is learned to preserve the similarity between grasps across different robotic hands, where the similarity of grasps is defined according to contact regions of the robotic hands. This property enables our method to transfer grasps between different grippers including a human hand, and grasp transfer has the potential to share grasping skills between robots and enable robots to learn grasping skills from humans. Furthermore, the encoded signed distance functions of objects and grasps in our implicit representation can be used for 6D object pose estimation with grasping contact optimization from partial point clouds, which enables robotic grasping in the real world.
Multi seasonal discrete time risk model revisited

In this work we set up the distribution function of $\mathcal{M}:=\sup_{n\geqslant1}\sum_{i=1}^{n}{(Z_i-1)}$, where the random walk $\sum_{i=1}^{n}Z_i, n\in\mathbb{N},$ is generated by $N$ periodically occurring distributions and the integer-valued and non-negative random variables $Z_1,\,Z_2,\,\ldots$ are independent. The considered random walk generates so-called multi seasonal discrete time risk model, and a known distribution of random variable $\mathcal{M}$ enables to calculate ultimate time ruin or survival probability. Verifying obtained theoretical statements we demonstrate several computational examples for survival probability $\mathbb{P}(\mathcal{M}
Scaling limit of an adaptive contact process

We introduce and study an interacting particle system evolving on the $d$-dimensional torus $(\mathbb Z/N\mathbb Z)^d$. Each vertex of the torus can be either empty or occupied by an individual of type $\lambda \in (0,\infty)$. An individual of type $\lambda$ dies with rate one and gives birth at each neighboring empty position with rate $\lambda$; moreover, when the birth takes place, the newborn individual is likely to have the same type as the parent, but has a small probability of being a mutant. A mutant child of an individual of type $\lambda$ has type chosen according to a probability kernel. We consider the asymptotic behavior of this process when $N\to \infty$ and the parameter $\delta_N$ tends to zero fast enough that mutations are sufficiently separated in time, so that the amount of time spent on configurations with more than one type becomes negligible. We show that, after a suitable time scaling and deletion of the periods of time spent on configurations with more than one type, the process converges to a Markov jump process on $(0,\infty)$, whose rates we characterize.
A Comprehensive Empirical Study of Bias Mitigation Methods for Software Fairness

Software bias is an increasingly important operational concern for software engineers. We present a large-scale, comprehensive empirical evaluation of 17 representative bias mitigation methods, evaluated with 12 Machine Learning (ML) performance metrics, 4 fairness metrics, and 24 types of fairness-performance trade-off assessment, applied to 8 widely-adopted benchmark software decision/prediction tasks. The empirical coverage is comprehensive, covering the largest numbers of bias mitigation methods, evaluation metrics, and fairness-performance trade-off measures compared to previous work on this important operational software characteristic. We find that (1) the bias mitigation methods significantly decrease the values reported by all ML performance metrics (including those not considered in previous work) in a large proportion of the scenarios studied (42% ∼ 75% according to different ML performance metrics); (2) the bias mitigation methods achieve fairness improvement in only approximately 50% over all scenarios and metrics (ranging between 29% ∼ 59% according to the metric used to asses bias/fairness); (3) the bias mitigation methods have a poor fairness-performance trade-off or even lead to decreases in both fairness and ML performance in 37% of the scenarios; (4) the effectiveness of the bias mitigation methods depends on tasks, models, and fairness and ML performance metrics, and there is no ‘silver bullet’ bias mitigation method demonstrated to be effective for all scenarios studied. The best bias mitigation method that we find outperforms other methods in only 29% of the scenarios. We have made publicly available the scripts and data used in this study in order to allow for future replication and extension of our work. may not use the latter as their proxy. This finding suggests that we take comprehensive ML performance metrics into account during the evaluation of bias mitigation methods, especially considering that different ML performance metrics measure the functional properties of ML software from different aspects and thus may provide different guidelines for real-world application scenarios. 56%). This finding further demonstrates the necessity of this study that measures the trade-off in terms of various fairness-performance metric pairs to obtain more general and comprehensive results.
On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property

and Abstract causal diagrams in our framework are built using universal constructions from category theory, including the limit or co-limit of an abstract causal diagram, more the We present two foundational results in universal causal inference. The ﬁrst result, called the Universal Causality Theorem (UCT), pertains to the universality of diagrams , which are viewed as functors mapping both objects and relationships from an indexing category of abstract causal diagrams to an actual causal model whose nodes are labeled by random variables, and edges represent functional or probabilistic relationships. UCT states that any causal inference can be represented in a canonical way as the co-limit of an abstract causal diagram of representable objects . UCT from a basic result in the theory of sheaves. The second result, the Causal Reproducing Property (CRP), states that any causal inﬂuence of a object X on another object Y is representable as a natural transformation between two abstract causal diagrams. CRP follows from the Yoneda Lemma, one of the deepest results in category theory. The CRP property is analogous to the reproducing property in Reproducing Kernel Hilbert Spaces that served as the foundation for kernel methods in machine learning. UC enables the study of causal inference over a much richer class of models by exploiting a powerful set of construction tools from category theory, including adjunctions, braiding, decorated cospans and operads that capture more sophisticated forms of interaction than the popular graph-based paradigm.
Wrapping Haptic Displays Around Robot Arms to Communicate Learning

Humans can leverage physical interaction to teach robot arms. As the human kinesthetically guides the robot through demonstrations, the robot learns the desired task. While prior works focus on how the robot learns, it is equally important for the human teacher to understand what their robot is learning. Visual displays can communicate this information; however, we hypothesize that visual feedback alone misses out on the physical connection between the human and robot. In this paper we introduce a novel class of soft haptic displays that wrap around the robot arm, adding signals without affecting that interaction. We first design a pneumatic actuation array that remains flexible in mounting. We then develop single and multi-dimensional versions of this wrapped haptic display, and explore human perception of the rendered signals during psychophysic tests and robot learning. We ultimately find that people accurately distinguish single-dimensional feedback with a Weber fraction of 11.4%, and identify multi-dimensional feedback with 94.5% accuracy. When physically teaching robot arms, humans leverage the single- and multi-dimensional feedback to provide better demonstrations than with visual feedback: our wrapped haptic display decreases teaching time while increasing demonstration quality. This improvement depends on the location and distribution of the wrapped haptic display.