Miracle-dz/UniFAR-FTUs huggingface dataset:
A Closer Look at Diagnosis in Clinical Dental Practice: Part 3. Effectiveness of Radiographic Diagnostic Procedures (Examen approfondi du diagnostic en pratique clinique dentaire : Partie 3. Efficacité des radiographies diagnostiques)

Dentists employ many diverse diagnostic procedures. From radiography to vitality testing, from determination of bleeding on probing to apex locators, the devices and systems available range from the simple to the complex. However, given the huge number of diagnostic procedures in the dental armamentarium, it is of interest to determine how well each of them performs. Is the procedure accurate? Does it add significant value to a basic clinical examination? Does one diagnostic procedure tend to produce a large number of false-positive results, while another produces many false-negatives? Can procedure results be combined to generate the best possible diagnostic evidence before treatment decisions are made? By examining some of today’s common procedures and analyzing the literature available, practitioners can become better informed about the value of each procedure they use. This article and the next one in the series use the tools described in the first 2 articles1,2 to examine the most common dental diagnostic procedures. Diverse studies serve as useful examples of the applications of these procedures to everyday dental practice, placing them in the context of their operating characteristics. With a general grounding in the concepts used in evaluating diagnostic procedures,1,2 the reader should now be able to examine these operating characteristics with a more discerning eye. This review is not a structured or systematic evaluation of the literature but rather a collection of recent or landmark papers to illustrate situations familiar to the dental practitioner. A glossary, with concise definitions of terms, is available for the entire series (see Appendix 1, Glossary of epidemiology terms, at http://www.cda-adc.ca/jcda/vol-70/issue-4/251.html). A Closer Look at Diagnosis in Clinical Dental Practice: Part 3. Effectiveness of Radiographic Diagnostic Procedures
Heterozygous mutations in the C-terminal domain of COPA underlie a complex autoinﬂammatory syndrome

Mutations in the N-terminal WD40 domain of coatomer protein complex subunit α (COPA) cause a type I interferonopathy, typically characterized by alveolar hemorrhage, arthritis, and nephritis. We described 3 heterozygous mutations in the C-terminal domain (CTD) of COPA (p.C1013S, p.R1058C, and p.R1142X) in 6 children from 3 unrelated families with a similar syndrome of autoinflammation and autoimmunity. We showed that these CTD COPA mutations disrupt the integrity and the function of coat protein complex I (COPI). In COPAR1142X and COPAR1058C fibroblasts, we demonstrated that COPI dysfunction causes both an anterograde ER-to-Golgi and a retrograde Golgi-to-ER trafficking defect. The disturbed intracellular trafficking resulted in a cGAS/STING-dependent upregulation of the type I IFN signaling in patients and patient-derived cell lines, albeit through a distinct molecular mechanism in comparison with mutations in the WD40 domain of COPA. We showed that CTD COPA mutations induce an activation of ER stress and NF-κB signaling in patient-derived primary cell lines. These results demonstrate the importance of the integrity of the CTD of COPA for COPI function and homeostatic intracellular trafficking, essential to ER homeostasis. CTD COPA mutations result in disease by increased ER stress, disturbed intracellular transport, and increased proinﬂammatory signaling.
AI-powered Coronary Artery Calcium Scans (AI-CAC) Enables Prediction of Heart Failure and Outperforms NT-proBNP: The Multi-Ethnic Study of Atherosclerosis

Introduction: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score. Methods: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% women, age 62{+/-}10 years, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We then used the 15-year outcomes data and compared the C-statistic of AI-CAC with NT-proBNP and the Agatston score for predicting incident HF versus 10 known clinical risk factors (age, gender, body surface area, diabetes, pre-baseline atrial fibrillation, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP). Results: Over 15 years of follow-up, 256 HF events accrued. The ROC area under the curve for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free NRI significantly improved on clinical risk factors (0.43), NT-proBNP (0.68), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001). Conclusion: AI-CAC significantly outperformed NT-proBNP and the Agatston CAC score and significantly improved category-free NRI of clinical risk factors for incident HF prediction. Key Words: Left ventricular volume, coronary artery calcium, artificial intelligence, heart failure.
Motor State Prediction and Friction Compensation for Brushless DC Motor Drives Using Data-Driven Techniques

In order to provide robust, reliable, and accurate position and velocity control of motor drives, friction compensation has emerged as a key difficulty. Non-characterised friction could give rise to large position errors and vibrations which could be intensified by stick-slip motion and limit cycles. This paper presents an application of two data-driven nonlinear model identification techniques to discover the governing equations of motor dynamics that also characterise friction. Namely, the extraction of low-power data from time-delayed coordinates of motor velocity and sparse regression on nonlinear terms was applied to data acquired from a Brushless DC (BLDC) motor, to identify the underlying dynamics. The latter can be considered an extension of the conventional linear motor model commonly used in many model-based controllers. The identified nonlinear model was then contrasted with a nonlinear model that included the LuGre friction model and a linear model without friction. A nonlinear grey box model estimation method was used to calculate the optimum friction parameters for the LuGre model. The resulting nonlinear motor model with friction characteristics was then validated using a feedback friction compensation algorithm. The novel model showed more than 90% accuracy in predicting the motor states in all considered input excitation signals. In addition, the model-based friction compensation scheme showed a relative increase in performance when compared with a system without friction compensation.
Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks

Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability. While physics-based computational models offer precision, they are often time-consuming and unsuitable for real-time predictions and online control in iterative design scenarios. Conversely, machine learning models rely heavily on high-quality datasets, which can be costly and challenging to obtain within the metal AM domain. Our work addresses this by introducing a physics-informed neural network framework specifically designed for temperature field prediction in metal AM. This framework incorporates a physics-informed input, physics-informed loss function, and a Convolutional Long Short-Term Memory (ConvLSTM) architecture. Utilizing real-time temperature data from the process, our model predicts 2D temperature fields for future timestamps across diverse geometries, deposition patterns, and process parameters. We validate the proposed framework in two scenarios: full-field temperature prediction for a thin wall and 2D temperature field prediction for cylinder and cubic parts, demonstrating errors below 3% and 1%, respectively. Our proposed framework exhibits the flexibility to be applied across diverse scenarios with varying process parameters, geometries, and deposition patterns.
Representation Learning of Multivariate Time Series using Attention and Adversarial Training

A critical factor in trustworthy machine learning is to develop robust representations of the training data. Only under this guarantee methods are legitimate to artificially generate data, for example, to counteract imbalanced datasets or provide counterfactual explanations for blackbox decision-making systems. In recent years, Generative Adversarial Networks (GANs) have shown considerable results in forming stable representations and generating realistic data. While many applications focus on generating image data, less effort has been made in generating time series data, especially multivariate signals. In this work, a Transformer-based autoencoder is proposed that is regularized using an adversarial training scheme to generate artificial multivariate time series signals. The representation is evaluated using t-SNE visualizations, Dynamic Time Warping (DTW) and Entropy scores. Our results indicate that the generated signals exhibit higher similarity to an exemplary dataset than using a convolutional network approach.
Decentralized Multitask Online Convex Optimization Under Random Link Failures

Decentralized optimization methods often entail information exchange between neighbors. Transmission failures can happen due to network congestion, hardware/software issues, communication outage, and other factors. In this paper, we investigate the random link failure problem in decentralized multi-task online convex optimization, where agents have individual decisions that are coupled with each other via pairwise constraints. Although widely used in constrained optimization, conventional saddle-point algorithms are not directly applicable here because of random packet dropping. To address this issue, we develop a robust decentralized saddle-point algorithm against random link failures with heterogeneous probabilities by replacing the missing decisions of neighbors with their latest received values. Then, by judiciously bounding the accumulated deviation stemming from this replacement, we first establish that our algorithm achieves $\mathcal{O}(\sqrt{T})$ regret and $\mathcal{O}(T^{\frac{3}{4}})$ constraint violations for the full information scenario, where the complete information on the local cost function is revealed to each agent at the end of each time slot. These two bounds match, in order sense, the performance bounds of algorithms with perfect communications. Further, we extend our algorithm and analysis to the two-point bandit feedback scenario, where only the values of the local cost function at two random points are disclosed to each agent sequentially. Performance bounds of the same orders as the full information case are derived. Finally, we corroborate the efficacy of the proposed algorithms and the analytical results through numerical simulations.
Simulation-Based Inference with Quantile Regression

We present Neural Quantile Estimation (NQE), a novel Simulation-Based Inference (SBI) method based on conditional quantile regression. NQE autoregressively learns individual one dimensional quantiles for each posterior dimension, conditioned on the data and previous posterior dimensions. Posterior samples are obtained by interpolating the predicted quantiles using monotonic cubic Hermite spline, with specific treatment for the tail behavior and multi-modal distributions. We introduce an alternative definition for the Bayesian credible region using the local Cumulative Density Function (CDF), offering substantially faster evaluation than the traditional Highest Posterior Density Region (HPDR). In case of limited simulation budget and/or known model misspecification, a post-processing broadening step can be integrated into NQE to ensure the unbiasedness of the posterior estimation with negligible additional computational cost. We demonstrate that the proposed NQE method achieves state-of-the-art performance on a variety of benchmark problems.
Cloud Computing para el internet de las cosas. Caso de estudio orientado a la agricultura de precisión

Precision agriculture is today one of the most advanced technologies to increase the efficiency and productivity of crops through the use of ICT solutions. One of the applied technologies is the wireless sensor network (WSN) to measure variables associated with soil, plant and fruit crops that allow among others, define strategies for irrigation control towards productivity and optimization of vital natural resources such as the water. The case study of this project is applied to control risk of an experimental cocoa plantation. The proposed objective is to define the architecture and components of a Cloud computing to the Internet of Things, acting as Monitoring and Control Center of Telemetry system. This system manages and processes data related variables measures moisture soil at different levels, thickness of the trunk of the plant and size of fruit, environmental measures such as relative humidity, temperature, speed and wind direction, and rainfall.
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search

It is often very tedious for human experts to design efficient algorithms. Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design. AEL combines the power of a large language model and the paradigm of evolutionary computation to design, combine, and modify algorithms automatically. In this paper, we use AEL to design the guide algorithm for guided local search (GLS) to solve the well-known traveling salesman problem (TSP). AEL automatically evolves elite GLS algorithms in two days, with minimal human effort and no model training. Experimental results on 1,000 TSP20-TSP100 instances and TSPLib instances show that AEL-designed GLS outperforms state-of-the-art human-designed GLS with the same iteration budget. It achieves a 0% gap on TSP20 and TSP50 and a 0.032% gap on TSP100 in 1,000 iterations. Our findings mark the emergence of a new era in automatic algorithm design.
Mutual-visibility problems on graphs of diameter two

The mutual-visibility problem in a graph $G$ asks for the cardinality of a largest set of vertices $S\subseteq V(G)$ so that for any two vertices $x,y\in S$ there is a shortest $x,y$-path $P$ so that all internal vertices of $P$ are not in $S$. This is also said as $x,y$ are visible with respect to $S$, or $S$-visible for short. Variations of this problem are known, based on the extension of the visibility property of vertices that are in and/or outside $S$. Such variations are called total, outer and dual mutual-visibility problems. This work is focused on studying the corresponding four visibility parameters in graphs of diameter two, throughout showing bounds and/or closed formulae for these parameters. The mutual-visibility problem in the Cartesian product of two complete graphs is equivalent to (an instance of) the celebrated Zarankievicz's problem. Here we study the dual and outer mutual-visibility problem for the Cartesian product of two complete graphs and all the mutual-visibility problems for the direct product of such graphs as well. We also study all the mutual-visibility problems for the line graphs of complete and complete bipartite graphs. As a consequence of this study, we present several relationships between the mentioned problems and some instances of the classical Tur\'an problem. Moreover, we study the visibility problems for cographs and several non-trivial diameter-two graphs of minimum size.
Asymptotically Optimal Proper Conflict-Free Colouring

A proper conflict-free colouring of a graph is a colouring of the vertices such that any two adjacent vertices receive different colours, and for every non-isolated vertex $v$, some colour appears exactly once on the neighbourhood of $v$. Caro, Petru\v{s}evski and \v{S}krekovski conjectured that every connected graph with maximum degree $\Delta \geq 3$ has a proper conflict-free colouring with at most $\Delta+1$ colours. This conjecture holds for $\Delta=3$ and remains open for $\Delta \geq 4$. In this paper we prove that this conjecture holds asymptotically; namely, every graph with maximum degree $\Delta$ has a proper conflict-free colouring with $(1+o(1))\Delta$ colours.
Detection and Discovery of Misinformation Sources using Attributed Webgraphs

Website reliability labels underpin almost all research in misinformation detection. However, misinformation sources often exhibit transient behavior, which makes many such labeled lists obsolete over time. We demonstrate that Search Engine Optimization (SEO) attributes provide strong signals for predicting news site reliability. We introduce a novel attributed webgraph dataset with labeled news domains and their connections to outlinking and backlinking domains. We demonstrate the success of graph neural networks in detecting news site reliability using these attributed webgraphs, and show that our baseline news site reliability classifier outperforms current SoTA methods on the PoliticalNews dataset, achieving an F1 score of 0.96. Finally, we introduce and evaluate a novel graph-based algorithm for discovering previously unknown misinformation news sources.
On Time-Indexing as Inductive Bias in Deep RL for Sequential Manipulation Tasks

While solving complex manipulation tasks, manipulation policies often need to learn a set of diverse skills to accomplish these tasks. The set of skills is often quite multimodal - each one may have a quite distinct distribution of actions and states. Standard deep policy-learning algorithms often model policies as deep neural networks with a single output head (deterministic or stochastic). This structure requires the network to learn to switch between modes internally, which can lead to lower sample efficiency and poor performance. In this paper we explore a simple structure which is conducive to skill learning required for so many of the manipulation tasks. Specifically, we propose a policy architecture that sequentially executes different action heads for fixed durations, enabling the learning of primitive skills such as reaching and grasping. Our empirical evaluation on the Metaworld tasks reveals that this simple structure outperforms standard policy learning methods, highlighting its potential for improved skill acquisition.
Singularity-agnostic incomplete U-statistics for testing polynomial constraints in Gaussian covariance matrices

Testing the goodness-of-fit of a model with its defining functional constraints in the parameters could date back to Spearman (1927), who analyzed the famous"tetrad"polynomial in the covariance matrix of the observed variables in a single-factor model. Despite its long history, the Wald test typically employed to operationalize this approach could produce very inaccurate test sizes in many situations, even when the regular conditions for the classical normal asymptotics are met and a very large sample is available. Focusing on testing a polynomial constraint in a Gaussian covariance matrix, we obtained a new understanding of this baffling phenomenon: When the null hypothesis is true but"near-singular", the standardized Wald test exhibits slow weak convergence, owing to the sophisticated dependency structure inherent to the underlying U-statistic that ultimately drives its limiting distribution; this can also be rigorously explained by a key ratio of moments encoded in the Berry-Esseen bound quantifying the normal approximation error involved. As an alternative, we advocate the use of an incomplete U-statistic to mildly tone down the dependence thereof and render the speed of convergence agnostic to the singularity status of the hypothesis. In parallel, we develop a Berry-Esseen bound that is mathematically descriptive of the singularity-agnostic nature of our standardized incomplete U-statistic, using some of the finest exponential-type inequalities in the literature.
MULTI-CASE: A Transformer-based Ethics-aware Multimodal Investigative Intelligence Framework

AI-driven models are increasingly deployed in operational analytics solutions, for instance, in investigative journalism or the intelligence community. Current approaches face two primary challenges: ethical and privacy concerns, as well as difficulties in efficiently combining heterogeneous data sources for multimodal analytics. To tackle the challenge of multimodal analytics, we present MULTI-CASE, a holistic visual analytics framework tailored towards ethics-aware and multimodal intelligence exploration, designed in collaboration with domain experts. It leverages an equal joint agency between human and AI to explore and assess heterogeneous information spaces, checking and balancing automation through Visual Analytics. MULTI-CASE operates on a fully-integrated data model and features type-specific analysis with multiple linked components, including a combined search, annotated text view, and graph-based analysis. Parts of the underlying entity detection are based on a RoBERTa-based language model, which we tailored towards user requirements through fine-tuning. An overarching knowledge exploration graph combines all information streams, provides in-situ explanations, transparent source attribution, and facilitates effective exploration. To assess our approach, we conducted a comprehensive set of evaluations: We benchmarked the underlying language model on relevant NER tasks, achieving state-of-the-art performance. The demonstrator was assessed according to intelligence capability assessments, while the methodology was evaluated according to ethics design guidelines. As a case study, we present our framework in an investigative journalism setting, supporting war crime investigations. Finally, we conduct a formative user evaluation with domain experts in law enforcement. Our evaluations confirm that our framework facilitates human agency and steering in security-sensitive applications.
Nonreciprocal photon blockade with Kerr magnons

Nonreciprocal devices, allowing to manipulate one-way signals, are crucial to quantum information processing and quantum network. Here we propose a nonlinear cavity-magnon system, consisting of a microwave cavity coupled to one or two yttrium-iron-garnet (YIG) spheres supporting magnons with Kerr nonlinearity, to investigate nonreciprocal photon blockade. The nonreciprocity originates from the direction-dependent Kerr effect, distinctly different from previous proposals with spinning cavities and dissipative couplings. For a single sphere case, nonreciprocal photon blockade can be realized by manipulating the nonreciprocal destructive interference between two active paths, via vary the Kerr coefficient from positive to negative, or vice versa. By optimizing the system parameters, the perfect and well tuned nonreciprocal photon blockade can be predicted. For the case of two spheres with opposite Kerr effects, only reciprocal photon blockade can be observed when two cavity-magnon coupling strengths Kerr strengths are symmetric. However, when coupling strengths or Kerr strengths become asymmetric, nonreciprocal photon blockade appears. This implies that two-sphere nonlinear cavity-magnon systems can be used to switch the transition between reciprocal and nonreciprocal photon blockades. Our study offers a potential platform for investigating nonreciprocal photon blockade effect in nonlinear cavity magnonics.