Miracle-dz/UniFAR-FTUs huggingface dataset:
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow frame- work adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative de- coders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10× than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments1.
Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT

Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-based workflows use limited views of the data that may obscure digital artifacts. In practice, multiple factors such as network issues, accelerated acquisitions, motion artifacts, and imaging protocol design can impede the interpretation of image collections. The medical image processing community has developed a wide variety of tools for the inspection and validation of imaging data. Yet, IQA of computed tomography (CT) remains an under-recognized challenge, and no user-friendly tool is commonly available to address these potential issues. Here, we create and illustrate a pipeline specifically designed to identify and resolve issues encountered with large-scale data mining of clinically acquired CT data. Using the widely studied National Lung Screening Trial (NLST), we have identified approximately 4% of image volumes with quality concerns out of 17,392 scans. To assess robustness, we applied the proposed pipeline to our internal datasets where we find our tool is generalizable to clinically acquired medical images. In conclusion, the tool has been useful and time-saving for research study of clinical data, and the code and tutorials are publicly available at https://github.com/MASILab/QA_tool.
Digital Collections of Examples in Mathematical Sciences

Some aspects of Computer Algebra (notably Computation Group Theory and Computational Number Theory) have some good databases of examples, typically of the form “all the 𝑋 up to size 𝑛 ”. But most of the others, especially on the polynomial side, are lacking such, despite the utility they have demonstrated in the related ﬁelds of SAT and SMT solving. We claim that the ﬁeld would be enhanced by such community-maintained databases, rather than each author hand-selecting a few, which are often too large or error-prone to print, and therefore diﬃcult for subsequent authors to reproduce.
All-electrical control of hole singlet-triplet spin qubits at low-leakage points

We study the effect of the spin-orbit interaction on heavy holes confined in a double quantum dot in the presence of a magnetic field of arbitrary direction. Rich physics arise as the two hole states of different spin are not only coupled by the spin-orbit interaction but additionally by the effect of site-dependent anisotropic $g$ tensors. It is demonstrated that these effects may counteract in such a way as to cancel the coupling at certain detunings and tilting angles of the magnetic field. This feature may be used in singlet-triplet qubits to avoid leakage errors and implement an electrical spin-orbit switch, suggesting the possibility of task-tailored two-axes control. Additionally, we investigate systems with a strong spin-orbit interaction at weak magnetic fields. By exact diagonalization of the dominant Hamiltonian we find that the magnetic field may be chosen such that the qubit ground state is mixed only within the logical subspace for realistic system parameters, hence reducing leakage errors and providing reliable control over the qubit.
Adversarial Attacks with Time-Scale Representations

We propose a novel framework for real-time black-box universal attacks which disrupts activations of early convolutional layers in deep learning models. Our hypothesis is that perturbations produced in the wavelet space disrupt early convolutional layers more effectively than perturbations performed in the time domain. The main challenge in adversarial attacks is to preserve low frequency image content while minimally changing the most meaningful high frequency content. To address this, we formulate an optimization problem using time-scale (wavelet) representations as a dual space in three steps. First, we project original images into orthonormal sub-spaces for low and high scales via wavelet coefficients. Second, we perturb wavelet coefficients for high scale projection using a generator network. Third, we generate new adversarial images by projecting back the original coefficients from the low scale and the perturbed coefficients from the high scale sub-space. We provide a theoretical framework that guarantees a dual mapping from time and time-scale domain representations. We compare our results with state-of-the-art black-box attacks from generative-based and gradient-based models. We also verify efficacy against multiple defense methods such as JPEG compression, Guided Denoiser and Comdefend. Our results show that wavelet-based perturbations consistently outperform time-based attacks thus providing new insights into vulnerabilities of deep learning models and could potentially lead to robust architectures or new defense and attack mechanisms by leveraging time-scale representations.
Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation

Understanding occupant-vehicle interactions by modeling control transitions is important to ensure safe approaches to passenger vehicle automation. Models which contain contextual, semantically meaningful representations of driver states can be used to determine the appropriate timing and conditions for transfer of control between driver and vehicle. However, such models rely on real-world control take-over data from drivers engaged in distracting activities, which is costly to collect. Here, we introduce a scheme for data augmentation for such a dataset. Using the augmented dataset, we develop and train take-over time (TOT) models that operate sequentially on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views, showing models trained on the augmented dataset to outperform the initial dataset. The demonstrated model features encode different aspects of the driver state, pertaining to the face, hands, foot and upper body of the driver. We perform ablative experiments on feature combinations as well as model architectures, showing that a TOT model supported by augmented data can be used to produce continuous estimates of take-over times without delay, suitable for complex real-world scenarios.
Varying fundamental constants principal component analysis: additional hints about the Hubble tension

Varying fundamental constants (VFC) [e.g. the fine-structure constant, αEM] can arise in numerous extended cosmologies. Through their effect on the decoupling of baryons and photons during last scattering and reionisation, these models can be directly constrained using measurements of the cosmic microwave background (CMB) temperature and polarization anisotropies. Previous investigations focused mainly on time-independent changes to the values of fundamental constants. Here we generalize to time-dependent variations. Instead of directly studying various VFC parameterizations, we perform a model-independent principal component analysis (PCA), directly using an eigenmode decomposition of the varying constant during recombination. After developing the formalism, we use Planck 2018 data to obtain new VFC limits, showing that three independent VFC modes can be constrained at present. No indications for significant departures from the standard model are found with Planck data. Cosmic variance limited modes are also compared and simple forecasts for The Simons Observatory are carried out, showing that in the future improvements of the current constraints by a factor of ≃ 3 can be anticipated. Our modes focus solely on VFC at redshifts z ≥ 300. This implies that they do not capture some of the degrees of freedom relating to the reionisation era. This aspect provides important new insights into the possible origin of the Hubble tension, hinting that indeed a combined modification of recombination and reionisation physics could be at work. An extended PCA, covering both recombination and reionisation simultaneously, could shed more light on this question, as we emphasize here.
Gradient-based Accelerated Adaptation of Deep Networks

Humans’ mysteriously remarkable ability to adapt to new tasks, based on limited prior experience is based on their ability to leverage past experience with tasks that inherently share a similar "structure". For example, opening a water bottle is very similar in nature to opening a jar of jam, or even crushing pepper. Undoubtedly, deep learning has achieved significant progress in this area, but this performance is usually accompanied by obstructively large amounts of data and computation cost.
Cancer trends in women in Italy

Cancer trends in Italy are traced and studied through population-based Cancer Registries (CRs): data is used to support the planning of health interventions but, so far, attention to gender differences has been relatively limited. The aim of this work is to describe time trends in cancer incidence and the prevalence of the main risk and protective factors in females in Italy, by geographic areas. The data produced and published by the CRs and the National Health Interview Survey (The Italian National Institute of Statistics – Istat) were analyzed by sex. In Italy, an estimated 377,000 new cancer cases were diagnosed in 2020 (52% in males and 48% in females), while 173,000 deaths from malignant cancer (55% in males and 45% in females) were registered. Breast cancer alone accounts for 30% of all neoplasms in women, followed by colorectal cancer (11%) and lung cancer (7%). Overall, the 5-year female survival in all three cancers was 63%; however, in older women and in those living in the Southern regions this percentage was lower for all three cancers. In women, the trend in all cancers was substantially heterogeneous across the Italian regions, with the Southern ones experiencing stable incidence and a slight decrease in mortality, and the Northern ones showing a marked decreasing trend in both incidence and mortality. Obesity and low levels of physical activity are more prevalent among women in the South than in the North, and screening coverage remains lower in the South. The prevalence of smoking is similar in young and middle-aged women throughout Italy, but is lower among older women in the Southern regions. A late implementation of oncological screening and a limited awareness of the most correct lifestyles still constitute health disadvantages for women living in Southern regions.
The Realistic Dilemma and Implementation Countermeasures of the Integration of Industry and Education in the Big Data Era

The integration of industry and education in the era of big data is not only a policy requirement, but also in line with the development trend of technological change. However, from the perspective of colleges, teachers and enterprises, there are many practical difficulties in the integration of industry and education in the current big data era: the level of informatization construction of vocational colleges needs to be improved; the informatization capabilities of teachers cannot meet the development needs of industry and education integration; enterprises there is insufficient motivation to promote the integration of industry and education. This article uses research methods of literature and questionnaire survey, based on big data thinking: use user thinking, centering on the needs of industries and enterprises; use cross-border thinking to promote cross-border integration of talent training; use social thinking to reshape the relationship between industry and education; use iterative thinking to constantly innovate; use platform thinking to build a cloud service platform for integration of industry and education.
Heat conduction due to laser beam heating: effect of surface geometry

Parts manufactured by additive manufacturing may suffer from anisotropic mechanical properties [1] caused by processing conditions or the geometric properties of the applied powder bed [2]. A reason for this is the formation of pores during processing which leads to a reduced bulk density. In order to optimize the material behavior of such parts a quantitative understanding of the interplay of parameters and material properties is neccessary [3], [4]. In this work a Smoothed Particle Hydrodynamics (SPH) code is used that is capable of simulating the laser induced heat flow in a body. Therefore, the heat conduction is solved together with a volumetric source term by using De-Beer-Lamber’s law. The accuracy of the implemented model is then validated against analytical solutions in a cuboid for a static as well as a moving laser heat source. The temporal evolution of the temperature field in a cuboid is then compared by using a constant and a evolving beam radius depending on the depth of focus. Finally, the temperature field evolution in beneath a hemispherical surface is compared to the temperature field evolution beneath a plane surface.
Brain Tumor Segmentation on Clinical Datasets

Brain tumor segmentation using deep learning is a helpful tool for physicians to rapidly diagnose brain tumors. In this project, an encoder-decoder architecture is utilized to perform brain tumor segmentation. In particular, we investigated several mode reduction techniques to try to limit the number of input modalities needed to achieve good performance. We utilized pre-processed datasets to train and test
CROSSING THE BORDERS BETWEEN MATHEMATICAL DOMAINS: A CONTRIBUTION TO FRAME THE CHOICE OF SUITABLE TASKS IN TEACHER EDUCATION

In mathematics teaching, closed boundaries between mathematics domains may convey to students a “fossilized” image of mathematics, and, in turn, cause difficulties in problem solving. Teachers should promote a “crossing” perspective in their teaching. In order to make teachers able to cross the borders, teacher education must encompass suitable tasks to be experienced and discussed. This paper reports a study aimed at framing the choice of such tasks, and the analysis of related problem solving behaviors. A contribution to framing comes from an adaptation of Habermas' construct of rational behavior. An experimental situation is planned and analyzed according to the resulting framework. Some educational implications are derived.
Anti-nociceptive and anti-inflammatory effects of hydroalcoholic extract and essential oil of Pinus eldarica in animal models

Objective: Several species of Pinus have shown anti-nociceptive and anti-inflammatory properties. This study was aimed to evaluate anti-nociceptive and anti-inflammatory effects of hydroalcoholic extract of bark and essential oil of leaves of P. eldarica in animal models. Materials and Methods: Hydroalcoholic extract of bark and essential oil of leaves of P. eldarica were prepared and phenolic content and essential oil composition were analyzed using Folin-Ciocalteu method and GC/MS, respectively. Anti-nociceptive effect was assessed using acetic acid, formalin and hot plate tests in male Swiss mice (25-30 g) and for evaluation of anti-inflammatory activity, carrageenan test in male Wistar rats (180-200 g) and croton oil-induced ear edema in male mice, were used. Involvement of opioid, α2-adrenergic, 5-HT3 receptors and adenosine triphosphate (ATP)-dependent K+ channels in pain relief was tested using naloxone, ondansetron, yohimbine and glibenclamide. Results: The total phenolic content of the extract in terms of gallic acid equivalent was 404.9±7.7 mg/g of the extract powder. P. eldarica hydroalcoholic extract (200 and 400 mg/kg) and essential oil (100 and 200 µl/kg) significantly (all, p<0.001) decreased pain behavior in acetic acid and formalin tests but not in hot plate test. The extract and essential oil suppressed edema in carrageenan and croton tests. Glibenclamide partially reversed the anti-nociceptive effect of hydroalcoholic extract while the other antagonists were ineffective. Conclusion: Hydroalcoholic extract of bark and essential oil of leaves of P. eldarica significantly decreased acute and chronic pain as well as inflammation. ATP-dependent K+ channels mediate a part of the observed anti-nociceptive effect.
Security System by Face Recognition

Background: Authentication is one of the major challenges of the information systems era. From Among other things, recognizing the human face is one of the known techniques that can be user authentication. Aim: The main purpose of this paper is to study the application of the facial recognition algorithms as a security system for the examination office at Omar Al-Mukhtar University (OMU) for the first time in Libyan universities. It can detect intruders into restricted or highly secure areas, and help reduce human errors. Methods: This system consists of two parts: the hardware part and the software part. The device part consists of a camera, while the software part consists of the face detection and facial recognition algorithms program. The face was detect using the Viola Jones method and face recognition is performed by Use of independent component analysis (ICA). When a person enters the area in question, his photos are taken by the camera and sent to the program for analysis and comparison with an existing database of trusted people. Result: In this study, we used a database of people responsible for the examination office at the College of Science, the sample consisted of 100 images. The ICA algorithm was applied to recognize faces, and it achieved a result of 86.7%, which is considered a good result, as it was compared to applying the PCA algorithm on the same sample, where the result was 76.7%. Conclusion: Independent Component Analysis (ICA) is better than Principal Component Analysis (PCA) in some cases, but requires at least a few seconds as is training time. The face recognition-based presence system achieves the expected goal, but the accuracy of the face recognition still needs improvement
Malware Shall Greatly Increase ...

actually seem like the “good old days. ” What on earth is going on? More to the point, can the number continue to increase the way it has? Is the sky actually falling, or instead of having literally thousands of completely new samples a day, are we actually fighting a few thousand different Trojans dressed in different disguises? In this column, we’ll take a peek behind the raw numbers and try to get a better perspective on what’s actually happening. Understanding our situation today is a two-step process. First, we need to put things in a better historical perspective. When we were counting new viruses per month on the fingers of one hand, what was going on—and what has changed? Once we understand that, we can then examine exactly what we’re calling “new” malware.
Tool requirements for modelling and building ontologies

In this report we provide an overview of languages and tools f or ontology representation, reasoning and management. We start with a definition of ontology, we present the most common languages for ontology representation (focusing, in particular, on Semantic Web languages) and lo gical formalisms to perform efficient reasoning over ontologies (in particular, Description Logics). Moreover, we describe some proposed ontology management systems. Finally, we focus on the service oriented paradigm: wepropose the architecture of a service ontology that organizes services by means of semantic relationships to su pport automatic discovery, composition and invocation of services. Date 04/11/2004 Product type Technical report R1.3. 4 State Draft Responsible Unit UNIBS Involved Units UNIBS Contact author Valeria De Antonellis Version 1.0
Experimental and Quantum Chemical Studies on Corrosion Inhibition Effect of Synthesized Organic Compounds on N80 Steel in Hydrochloric Acid

The inhibition effect of synthesized 2,4-bis(phenyl)-1H-benzodiazepine (BPBD) and 2,4-bis(methoxyphenyl)-1H-benzodiazepine (BMBD) on corrosion of N80 steel in 15% HCl solution was investigated using weight loss measurements, potentiodynamic polarization, and electrochemical impedance spectroscopy techniques. The experimental results show that the inhibition efficiency of studied inhibitors increased with an increase in inhibitors concentration but decreased with an increase in temperature. Corrosion inhibition efficiency of 95.6 and 93.4% were obtained with 200 ppm of BMBD and BPBD, respectively at 303 K. The potentiodynamic polarization measurements indicated that both of the inhibitors are of mixed type. The adsorption of these inhibitors was found to obey Langmuir adsorption isotherm. The mechanism of the inhibition process was discussed in the light of the chemical structure and quantum chemical calculations of the investigated inhibitors. Scanning electron microscopy was used to characterize the surf.
Image Captioning and Visual Question Answering for the Visually Impaired

----------------------------------------------------------------------***------------------------------------------------------------------- Abstract : For people, it's straightforward for us to take a look at an image and give the response to the answer for any questions utilizing our insight. In any case, there additionally are situations, for example, a visually impaired user or an intelligence, any place they need to effectively evoke visual information given a picture. We might want to help blind people to beat their day by day visual difficulties and separate social availability obstructions. The main purpose of our project is Image captioning and VQA, Image captioning is to get a caption for an Image.Image Captioning is to get an inscription or caption for a picture. Picture inscribing needs to decide objects in the picture, activities, their relationship and a couple of quiet highlights that might be absent inside the picture. While distinguishing, the accompanying advance is to get a most relevant and transient description for the picture that must be grammatically and semantically right. It utilizes each CNN for identification of objects and language process ways for description and on its description users (visually impaired) will raise any questions, we tend to propose the task of free-form and open-ended VQA. Given an image and a characteristic language question concerning the picture, the task is to deliver a right regular language answer.