Miracle-dz/UniFAR-FTUs huggingface dataset:
C ONCEPT DISCOVERY AND D ATASET EXPLORATION WITH S INGULAR V ALUE D ECOMPOSITION

Providing reliable and trustworthy predictions as the outcome of deep learning models is a major challenge, particularly in supervised settings that include misleading training annotations. Concept-based explanations clarify the relevance of high-level concepts to the model predictions, although this may be biased by the user’s expectations of the concepts. Here we propose a post-hoc unsupervised method that automatically discovers high-level concepts learned by intermediate layers of vision models. By the singular value decomposition of the latent space of a layer, we discover concept vectors that correspond to orthogonal directions of high variance and are relevant to the model prediction. Most of the identified concepts are human-understandable, coherent and relevant to the task. Moreover, by using the discovered concepts we identify training samples with confounding factors that emerge as outliers. Our method is straightforward to implement, and it can be easily adapted to interpret multiple architectures and identify anomalies in the data collection.
"Never complain, never explain": why robots may not have to be explicable after all

 Abstract — Explicability in AI and robotics is at the centre of interdisciplinary discussions, which involve machine learning, computer science, ethics, and more. Transparency and explicability are generally seen as a need for systems that encompass AI. We offer a different point of view, arguing for re-thinking the necessity of explicability at all costs, citing examples from videogames user-centred design, and existing applications in social robotics.
Calcifications on Cerebrovascular Events Configurations of Carotid Artery Plaque Impact Analysis of Different CT

BACKGROUND AND PURPOSE: CT is considered the standard reference both for quanti ﬁ cation and characterization of carotid artery calci ﬁ cations. Our aim was to investigate the relationship among different types of calcium con ﬁ gurations detected with CT within the plaque with a novel classi ﬁ cation and to investigate the prevalence of cerebrovascular events. MATERIALS AND METHODS: Seven hundred ninety patients (men= 332; mean age, 69.7 [SD, 13] years; 508 symptomatic for cerebro-vascular symptoms and 282 asymptomatic) who underwent computed tomography of the carotid arteries were retrospectively included in this institutional review board – approved study. The plaque was classi ﬁ ed into 6 types according to the different types of calcium con ﬁ gurations as the following: type 1, complete absence of calci ﬁ cation within the plaque; type 2, intimal or super ﬁ cial calci ﬁ cations; type 3, deep or bulky calci ﬁ cations; type 4, adventitial calci ﬁ cations with internal soft plaqueof , 2mm thickness; type 5, mixed patterns with intimal and bulky calci ﬁ cations; and type 6, positive rim sign. RESULTS: The highest prevalence of cerebrovascular events was observed for type 6, for which 89 of the 99 cases were symptomatic. Type 6 plaque had the highest degree of correlation with TIA, stroke, symptoms, and ipsilateral infarct for both sides with a higher prevalence in younger patients. The frequency of symptoms observed by con ﬁ guration type signi ﬁ cantly differed between right and left plaques, with symptoms observed more frequently in type 6 calci ﬁ cation on the right side (50/53; 94%) than on the left side (39/46; 85%, P , . 001
The Social Cost of Ozone-Related Mortality Impacts From Methane Emissions

Atmospheric methane directly affects surface temperatures and indirectly affects ozone, impacting human welfare, the economy, and environment. The social cost of methane (SC‐CH4) metric estimates the costs associated with an additional marginal metric ton of emissions. Current SC‐CH4 estimates do not consider the indirect impacts associated with ozone production from changes in methane. We use global model simulations and a new BenMAP webtool to estimate respiratory‐related deaths associated with increases in ozone from a pulse of methane emissions in 2020. By using an approach consistent with the current SC‐CH4 framework, we monetize and discount annual damages back to present day values. We estimate that the methane‐ozone mechanism is attributable to 760 (95% CI: 330–1200) respiratory‐related deaths per million metric tons of methane globally, for a global net present damage of $1800/mT (95% CI: $760–$2800/mT CH4; 2% Ramsey discount rate); this would double the current SC‐CH4 if included. These physical impacts are consistent with recent studies, but comparing direct costs is challenging. Economic damages are sensitive to uncertainties in the exposure and health risks associated with tropospheric ozone, assumptions about future projections of NOx emissions, socioeconomic conditions, and mortality rates, monetization parameters, and other factors. Our estimates are highly sensitive to uncertainties in ozone health risks. We also develop a reduced form model to test sensitivities to other parameters. The reduced form tool runs with a user‐supplied emissions pulse, as well as socioeconomic and precursor projections, enabling future integration of the methane‐ozone mechanism into the SC‐CH4 modeling framework.
Embedding Reliability: On the Predictability of Downstream Performance

In (self-)supervised (pre-)training, such as in contrastive learning, often a network is presented with correspondent (positive) and non-correspondent (negative) pairs of datapoints, and is trained to find an embedding vector for each datapoint, i.e., a representation, which can be further fine-tuned for various downstream tasks. To safely deploy these models in critical decision-making systems, it is crucial to equip them with a measure of their reliability . Here we study whether such measures can be quantified for a datapoint in a meaningful way. In other words, we explore if the downstream performance on a given datapoint is predictable, directly from a few characteristics of its pre-trained embedding. We study whether this goal can be achieved by directly estimating the distribution of the training data in the embedding space, and accounting for the local consistency of the representations. Our experiments show that these notions of reliability often strongly correlate with its downstream accuracy. For a more detailed version of this study, please refer to [Ardeshir and Azizan, 2022].
Comparing AI planning algorithms with humans on the Tower of London task

Understanding problem solving or planning has been a shared challenge for both AI and cognitive science since the birth of both fields. We explore the extent to which modern planners from the field of AI can account for human performance on the Tower of London (TOL) task, a close relative of the Tower of Hanoi problem that has been extensively studied by psychologists. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate an adaptive online planner and a family of well-known planners, including online planners, optimal planners and satisficing planners. Each planner is evaluated based on its ability to predict the actions and planning times of participants in a new behavioral experiment. Our results suggest that participants use a range of strategies but that an adaptive lookahead planner provides the best overall account of both human actions and human planning times. This finding is consistent with the view that humans differ from standard AI planners by integrating a mechanism for evidence accumulation.
Pre-Trained Embeddings for Enhancing Multi-Hop Reasoning

Knowledge graphs are an efficient way to represent heterogeneous data from multiple sources or disciplines by utilizing nodes and their relations. Nevertheless, they are frequently incomplete in terms of the subject they represent. Link prediction methods are used to discover additional links (or even to create new ones) between entities present in the Knowledge Graph (KG). In order to achieve this, multi-hop reasoning models have demonstrated good predictive performance and the ability to generate interpretable decisions, thereby enabling their application in high-stakes domains such as finance and public health. A multi-hop reasoning model usually has two tasks: 1) construct an accurate representation of the entities and relationships of the KG; 2) use these representations to explore the reasoning paths in the KG that support the newly predicted links. In this paper, we investigate how the performance of a multi-hop reasoning model changes when using pre-trained embeddings for the KG’s nodes and relations. The experiments conducted on three benchmark datasets, respectively WN18RR, NELL-995 and FB15K-237, suggest that using pre-trained embeddings improves: (i) the predictive performance of multi-hop reasoning models for all three datasets, (ii) the number of newly predicted links, and (iii) the quality of paths used as explanations.
High-Pressure Turbine Aerodynamics Design of Small Turbofan Engines

Due to the general requirements of small turbofan engines and the characteristics of high-pressure turbines,the high-pressure turbine for small turbofan engines has a low aspect ratio and the secondary loss and tip leakage loss are high.According to the general requirements of performance and structure,we have designed a single stage high-pressure turbine for the small turbofan engine with3.4 pressure ratio.The three-dimensional viscous numerical calculations show that the performance of the high-pressure turbine meets the target with high efficiency.We mainly describe the flow phenomena in the guide vane passage.For better evaluation of the flow in the high-pressure turbine,we modeled the purge flow of the cooling and sealing air between the blade and vane in the numerical calculation.We analyze its influence on the secondary flow and the incidence angle of the blade.
Autocorrelation analysis-based OCT velocimetry for axial blood flow velocity imaging of the cerebral capillary network.

The accurate measurement of blood flow velocity in the capillary network is challenging due to the small size of the vessels and the slow flow of red blood cells (RBCs) within the vessel. Here, we introduce an autocorrelation analysis-based optical coherence tomography (OCT) method that takes less acquisition time to measure the axial blood flow velocity in the capillary network. The axial blood flow velocity was obtained from the phase change in the decorrelation period of the first-order field autocorrelation function (g1) of the OCT field data, which was acquired with M-mode acquisition (repeated A-scans). The rotation center of g1 in the complex plane was first re-centralized to the origin, then the phase change due to the movement of RBCs was extracted in the g1 decorrelation period which is usually 0.2-0.5 ms. In phantom experiments, the results suggest that the proposed method could accurately measure the axial speed with a wide range of 0.5-15 mm/s. We further tested the method on living animals. Compared with the phase-resolved Doppler optical coherence tomography (pr-DOCT), the proposed method can obtain robust axial velocity measurements with more than five times shorter acquisition time.
Supercontinuum generation in polarization maintaining photonic crystal fiber by using various harmonics of sub-nanosecond Q-switched laser

We present results of supercontinuum generation in highly nonlinear polarization maintaining photonic crystal fiber using various harmonics of sub-nanosecond passively Q-switched Nd:YAG microlaser. The pump source was a Nd:YAG microlaser generating 50 μJ energy 300 ps duration pulses at 1064 nm with kilohertz repetition rate. We demonstrated that we can expand the supercontinuum spectrum to cover the whole visible range and beyond using either first or second harmonic of our pump laser in simple experimental setups: supercontinuum extended from 400 nm to 1300 nm in case of λp = 1064 nm and from 400 nm to 900 nm in case of λp = 532 nm. We compared supercontinuum evolution and dependence on pump pulse energy in both cases. We also performed numerical simulations of supercontinuum generation in PCF by applying the traditional approach of solving generalized nonlinear Schrödinger equation (GNLSE) and also presented a new numerical simulation approach -deriving and solving equation for evolution of spectral components of pulse propagating in the PCF. In case of GNLSE approach, the simulated supercontinuum spectra display the same qualitative features as the ones measured in the experiment.
Analytical solution to laser short-pulse heating of microsized metal wire: volumetric and surface heat source considerations

Analytical solution for laser short-pulse heating of a micro-sized metal wire is presented. In the analysis, volumet- ric and surface heat sources are incorporated for the same pulse intensity. The volumetric heat source resembles absorption by irradiated field according to Lambert's Beer law while a surface heat source represents short pulse heating through high intensity thermal contact at the surface. The method of Lie point symmetries is combined with a Fourier cosine transforma- tion to solve the temperature equation with appropriate boundary conditions. It is found that temperature profiles differ sig- nificantly for volumetric heat source and surface heat source considerations; in which case, volumetric heat source consideration results in considerably lower surface temperatures than that of the surface heat source consideration. PACS Nos: 44.10. +i Resume : Nous presentons une solution analytique pour le chauffage d'un fil metallique tres fin par impulsion laser courte. Cette analyse incorpore les sources de chaleur en volume et en surface pour la meme intensite d'impulsion. La source de chaleur en volume ressemble a l'absorption par champ irradie selon la loi Beer-Lambert, alors que la source en surface re- presente le chauffage par l'impulsion courte via un contact thermique de haute intensite a la surface. Nous introduisons ici la transformation de Fourier cosine pour resoudre l'equation de temperature avec les conditions limites appropriees. Nous trouvons que les profiles de temperature different significativement selon que nous considerons les sources en volume ou les sources en surface; auquel cas, la consideration des sources de chaleur en volume resultent en des temperatures de surface considerablement plus basses que ne donnent les sources de chaleur de surface. (Traduit par la Redaction)
Expression Recognition Based on Multi-Regional Coordinate Attention Residuals

Facial expression recognition is an important research direction of emotion computing and has broad application prospects in human-computer interaction. However, noise such as illumination and occlusion in natural environment brings many challenges to facial expression recognition. In order to solve the problems such as low recognition rate of facial expression in natural environment, unable to highlight the characteristics of facial expression in global facial research, and misclassification caused by the similarity between negative expressions. In this paper, a multi-region coordinate attentional residual expression recognition model (MrCAR) is proposed. The model is mainly composed of the following three parts: 1) multi-region input: MTCNN is used for face detection and alignment processing, and the eyes and mouth parts are further cropped to obtain multi-region pictures. Through multi-region input, local details and global features are more easily obtained, which reduces the influence of complex environmental noise and highlights the facial features. 2) Feature extraction module: On the basis of residual element, CA-Net and multi-scale convolution were added to obtain coordinate residual attention module, through which the model’s ability to distinguish subtle changes of expression and the utilization rate of key features were improved; 3) Classifier: Arcface Loss is used to enhance intra-class tightness and inter-class difference at the same time, thus reducing the wrong classification of negative expressions by the model. Finally, the accuracy rates of CK+, JAFFE, FER2013 and RAF-DB were 98.78%, 99.09%, 74.50% and 88.26%, respectively. The experimental results show that compared with many advanced models, the MrCAR model in this paper is more competent for the task of expression classification.
Mobile Videography on YouTube and the Significance of the Marginalised Creator

This research aims to identify the variables that contribute to the differential viewership of mobile-phone videos on YouTube. To achieve this, a Generalized Linear Mixed Model (GLMM) is employed to examine the correlations among various variables in a randomly selected video database. The findings of the analysis reveal several significant and culturally insightful associations. Notably, subgenres such as melodrama (soap-operas or telenovelas), action, and comedy emerge as distinctive categories, irrespective of the total number of views. Comedies and action videos created by male filmmakers receive higher viewership. Similarly, melodramas created by female filmmakers, particularly those hailing from marginalized and rural backgrounds, hold greater statistical significance. These findings highlight the relevance of gender and social context in shaping viewer preferences and challenge the conventions of mainstream media in the respective countries of origin.
IMPROVEMENT OF STATISTICAL SHAPE MODELS FOR NON-RIGID TISSUES USING COHERENT POINT DRIFT ALGORITHM

Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding points of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set. In this paper, the non-rigid CPD (Coherent Point Drift) method is used to find correct correspondences among points. This method uses both Deterministic Annealing and a non-rigid scheme to register two shapes simultaneously. Then, the statistical shape model is built using a rigid transformation. The proposed method is evaluated using Compactness, Generalization ability and Specificity measures. The built model is compared to models created using the ICP (Iterative Closest Point), TPS-RPM (Thin Plate Spline – Robust Point Matching) and MDL (Minimum Descreption Length) methods by these metrics. The results show that the proposed method performs like the MDL regarding Specificity measure (0.21±0.06). The Compactness and Generalization ability measures of the proposed method are very similar to those for the MDL method. The run-time of our proposed method is about 68 seconds which is faster than non-rigid TPS-RPM and MDL approaches (390 and 3600 seconds respectively). Our results are superior to the ICP and TPS-RPM algorithms.
In-Field Tobacco Leaf Maturity Detection with an Enhanced MobileNetV1: Incorporating a Feature Pyramid Network and Attention Mechanism

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model’s robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves.
The mean waiting time in a G/G/m/∞ queue with the LCFS-P service discipline

A single- or multiserver queue with work-conserving service discipline and a stationary and ergodic input stream with bounded service times and arbitrarily light traffic intensity may have infinite mean waiting time. We give an example of this paradox and we also give a mixing condition which, in the case of the preemptive-resume LCFS discipline, excludes this phenomenon. Furthermore, the same methods allow to estimate the durations of the first busy period and cycle and the number of customers served in the first busy cycle of a work-conserving queue.
Microscale modeling of effective mechanical and electrical properties of textiles

A computational framework for assisting in the development of novel textiles is presented. Electronic textiles are key in the rapidly growing field of wearable electronics for both consumer and military uses. There are two main challenges to the modeling of electronic textiles: the discretization of the textile microstructure and the interaction between electromagnetic and mechanical fields. A director‐based beam formulation with an assumed electrical current is used to discretize the fabric at the level of individual fibrils. The open‐source package FEniCS was used to implement the finite element model. Contact integrals were added into the FEniCS framework so that multiphysics contact laws can be incorporated in the same framework, leveraging the code generation and automated differentiation capabilities of FEniCS to produce the tangents needed by the implicit solution method. The computational model is used to construct and determine the mechanical, thermal, and electrical properties of a representative volume elements of a plain woven textile. Dynamic relaxation to solve the mechanical fields and the electrical and thermal fields is solved statically for a given mechanical state. The simulated electrical responses are fit to a simplified Kirchhoff network model to determine effective resistances of the textile. Copyright © 2016 John Wiley & Sons, Ltd.
Relocking and Locking Range Extension of Partially Locked AMLL Cavity Modes with Two Detuned RF Sinusoids

Actively mode-locked fiber ring lasers (AMLLs) with loss modulators are used to generate approximately 100ps pulses with 100MHz repetition. RF detuning around the fundamental frequency, f0, causes a loss in phase lock (unlocking) of cavity modes and partial mode locking. Multiple RF inputs are shown, theoretically, to relock and extend the locking range of cavity modes in a detuned partially mode-locked AMLL. A custom-built Yb3+-doped AMLL with f0=26MHz, and operating wavelength of 1064nm, was used to experimentally verify the theoretical predictions. Two RF sinusoidal signals with constant phase and equal amplitude resulted in an extension of the range by Xn=6.4kHz in addition to the range Rn=14.34kHz with single input for the mode n=10. An increase in locking range was also observed for higher modes. Pulsewidth reduction to approximately 205ps from about 2ns was also observed in the AMLL.