Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center

Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the autoencoder on normal training data, anomalous inputs will exhibit a significant reconstruction error. Consequently, this enables a clear differentiation between normal and anomalous samples. In practice, however, it is observed that autoencoders can generalize beyond the normal class and achieve a small reconstruction error on some of the anomalous samples. To improve the performance, various techniques propose additional components and more sophisticated training procedures. In this work, we propose a remarkably straightforward alternative: instead of adding neural network components, involved computations, and cumbersome training, we complement the reconstruction loss with a computationally light term that regulates the norm of representations in the latent space. The simplicity of our approach minimizes the requirement for hyperparameter tuning and customization for new applications which, paired with its permissive data modality constraint, enhances the potential for successful adoption across a broad range of applications. We test the method on various visual and tabular benchmarks and demonstrate that the technique matches and frequently outperforms more complex alternatives. We further demonstrate that implementing this idea in the context of state-of-the-art methods can further improve their performance. We also provide a theoretical analysis and numerical simulations that help demonstrate the underlying process that unfolds during training and how it helps with anomaly detection. This mitigates the black-box nature of autoencoder-based anomaly detection algorithms and offers an avenue for further investigation of advantages, fail cases, and potential new directions.
Comprehensive Analysis of a Symbiotic Candidate V503 Her

V503 Her was previously proposed as an eclipsing symbiotic candidate based on photometric behavior and spectroscopic appearance indicating the composite optical spectrum. To investigate its nature, we analyzed long-term photometric observations covering 100 yr of its photometric history and new low-resolution optical spectroscopic data, supplemented with the multifrequency measurements collected from several surveys and satellites. Based on the analysis presented in this paper, we claim that V503 Her is not an eclipsing binary star. The optical and infrared wavelengths are dominated by a K-type bright giant with an effective temperature of 4500 K, luminosity of 1900 L ⊙, and subsolar metallicity on the asymptotic giant branch showing semiregular complex multiperiodic pulsation behavior. V503 Her does not show the characteristics of strongly interacting symbiotic variables, but some pieces of evidence suggest that it could still be one of the “hidden” accreting-only symbiotic systems. However, the currently available data do not allow us to fully confirm or constrain the parameters of a possible companion.
Outlier-robust Estimation of a Sparse Linear Model Using Invexity

In this paper, we study problem of estimating a sparse regression vector with correct support in the presence of outlier samples. The inconsistency of lasso-type methods is well known in this scenario. We propose a combinatorial version of outlier-robust lasso which also identifies clean samples. Subsequently, we use these clean samples to make a good estimation. We also provide a novel invex relaxation for the combinatorial problem and provide provable theoretical guarantees for this relaxation. Finally, we conduct experiments to validate our theory and compare our results against standard lasso.
Deep Dynamic Epidemiological Modelling for COVID-19 Forecasting in Multi-level Districts

Objective: COVID-19 has spread worldwide and made a huge influence across the world. Modeling the infectious spread situation of COVID-19 is essential to understand the current condition and to formulate intervention measurements. Epidemiological equations based on the SEIR model simulate disease development. The traditional parameter estimation method to solve SEIR equations could not precisely fit real-world data due to different situations, such as social distancing policies and intervention strategies. Additionally, learning-based models achieve outstanding fitting performance, but cannot visualize mechanisms. Methods: Thus, we propose a deep dynamic epidemiological (DDE) method that combines epidemiological equations and deep-learning advantages to obtain high accuracy and visualization. The DDE contains deep networks to fit the effect function to simulate the ever-changing situations based on the neural ODE method in solving variants' equations, ensuring the fitting performance of multi-level areas. Results: We introduce four SEIR variants to fit different situations in different countries and regions. We compare our DDE method with traditional parameter estimation methods (Nelder-Mead, BFGS, Powell, Truncated Newton Conjugate-Gradient, Neural ODE) in fitting the real-world data in the cases of countries (the USA, Columbia, South Africa) and regions (Wuhan in China, Piedmont in Italy). Our DDE method achieves the best Mean Square Error and Pearson coefficient in all five areas. Further, compared with the state-of-art learning-based approaches, the DDE outperforms all techniques, including LSTM, RNN, GRU, Random Forest, Extremely Random Trees, and Decision Tree. Conclusion: DDE presents outstanding predictive ability and visualized display of the changes in infection rates in different regions and countries.
Geometric Graphs with Unbounded Flip-Width

We consider the flip-width of geometric graphs, a notion of graph width recently introduced by Toru\'nczyk. We prove that many different types of geometric graphs have unbounded flip-width. These include interval graphs, permutation graphs, circle graphs, intersection graphs of axis-aligned line segments or axis-aligned unit squares, unit distance graphs, unit disk graphs, visibility graphs of simple polygons, $\beta$-skeletons, 4-polytopes, rectangle of influence graphs, and 3d Delaunay triangulations.
Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of CMBs is a time-consuming and error-prone process because of their sparse and tiny structural properties. The detection of CMBs is commonly affected by the presence of many CMB mimics that cause a high false-positive rate (FPR), such as calcification and pial vessels. This paper proposes a novel 3D deep learning framework that does not only detect CMBs but also inform their anatomical location in the brain (i.e., lobar, deep, and infratentorial regions). For the CMB detection task, we propose a single end-to-end model by leveraging the U-Net as a backbone with Region Proposal Network (RPN). To significantly reduce the FPs within the same single model, we develop a new scheme, containing Feature Fusion Module (FFM) that detects small candidates utilizing contextual information and Hard Sample Prototype Learning (HSPL) that mines CMB mimics and generates additional loss term called concentration loss using Convolutional Prototype Learning (CPL). The anatomical localization task does not only tell to which region the CMBs belong but also eliminate some FPs from the detection task by utilizing anatomical information. The results show that the proposed RPN that utilizes the FFM and HSPL outperforms the vanilla RPN and achieves a sensitivity of 94.66% vs. 93.33% and an average number of false positives per subject (FPavg) of 0.86 vs. 14.73. Also, the anatomical localization task further improves the detection performance by reducing the FPavg to 0.56 while maintaining the sensitivity of 94.66%.
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness

Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia package for neural network models that are constructed to naturally satisfy a set of user-defined robustness constraints. The package is based on the recently proposed Recurrent Equilibrium Network (REN) and Lipschitz-Bounded Deep Network (LBDN) model classes, and is designed to interface directly with Julia's most widely-used machine learning package, Flux.jl. We discuss the theory behind our model parameterization, give an overview of the package, and provide a tutorial demonstrating its use in image classification, reinforcement learning, and nonlinear state-observer design.
Preparation and characterization of sulfonated carbon nanotube/Nafion IPMC actuators

In this paper, we developed a new kind of ionic polymer metal composite (IPMC) actuator by doping sulfonated carbon nanotube (SCNT) into Nafion matrix to overcome some major drawbacks, such as low output force and short air-operation time, which restrict applications of conventional Nafion IPMC actuators. Firstly, SCNT was synthesized by coupled reaction of multi-walled carbon nanotubes and azo compounds and then doped into Nafion matrix by casting method. Subsequently, several key parameters of the SCNT-reinforced Nation matrix, water uptake ratio and equivalent stiffness, were revealed and the inner morphology of the membranes were observed by scanning electron microscopy. Finally, the effects of the SCNT on the electromechanical properties of IPMC actuators, especially the actuating performance, were evaluated experimentally and analyzed systematically. The results showed that SCNT was evenly dispersed in Nafion matrix and a small amount of SCNT could improve the performance of IPMC actuators significantly.
Finding Agency Amidst Gender Violence

Those of us with embodied experiences of gender diversity and sexually assault have and continue to be subject to psychiatric diagnosis and categorization that pathologize our acts of dissociation within a medical framework. In this paper we adopt Barad, Karen. 2007. Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham and London: Duke University Press new materialist ontology of agential realism to argue that agentially cutting psychiatric discourse on dissociative symptoms could materialize new realities for embodied people which have been excluded to the psychiatric realm of abjection via gender diversity and sexual assault. Specifically, we explore how approaching dissociative symptoms not as dysfunction but as forms of agential dislocation from hegemonic norms of race and gender could open new political horizons by naming relations of dominance.
The Presentation of the Anti-Vaccination Movement in Social Networking Sites

Introduction: The anti-vaccination movement has started to develop into a major public health problem. After 1998, measles re-emerged in the UK and subsequently caused further outbreaks in Europe. Methods: MEDLINE, EMBASE and CINAHL databases were searched with keywords antivaccine, media, internet, social media. The results were ranked according to their content. The anti-vaccination movement’s online media presence was reviewed on websites and search engines and social media such as facebook, twitter, instagram, pinterest and the older chatrooms and myspace. Results: While Web 1 was static, Web 2 and its future evolution, the Semantic Web is mutable and interactive. Social media offers the possibility to publish not only text but also image and video increasing the power and immediacy of communication. Users can upload content such as videos, pages and interact with each other. Thus, the roles of producers and consumers of news alternate, without the need for an intermediary. Thus, anyone is at the same time a channel owner, a broadcast producer without being subject to ethical rules. Conclusions: The anti-vaccination movement is not new. But has evolved with the advancement of technology and managed to follow the trends of the time by exploiting the nature of interactive media. The rhetoric of the anti-vaccination movement uses the power of image and video, the emotional charge of the personal stories they invoke. His argument is rooted in safeguarding personal liberty, conspiracy theories, and concern about adverse reactions are the main arguments used by vaccine hesitancy groups and should be addressed in communication with the health care service users.
Cellular and Non-cellular Antioxidant Properties of Vitamin E–Loaded Metallic-Quercetin/Polycaprolactone Nanoparticles for the Treatment of Melanogenesis

Inhibition of melanogenesis by quercetin and vitamin E is extensively reported in the literature, independently, with limitations in antioxidant potential owing to less permeation, solubility, decreased bioavailability, and reduced stability. Thus, the aim of the present study was to synthesize a novel complex of metal ions (copper and zinc) with quercetin to enhance antioxidant properties which were confirmed by docking studies. Polycaprolactone-based nanoparticles of the synthesized complex (PCL-NPs, Q-PCL-NPs, Zn-Q-PCL-NPs, Cu-Q-PCL-NPs) were made later loaded with vitamin E which made the study more interesting in enhancing antioxidant profile. Nanoparticles were characterized for zeta size, charge, and polydispersity index, while physiochemical analysis of nanoparticles was strengthened by FTIR. Cu-Q-PCL-NPs-E showed maximum in vitro release of vitamin E, i.e., 80 ± 0.54%. Non-cellular antioxidant effect by 2,2-diphenyl-1-picrylhydrazyl was observed at 93 ± 0.23% in Cu-Q-PCL-NPs-E which was twofold as compared to Zn-Q-PCL-NPs-E. Michigan Cancer Foundation-7 (MCF-7) cancer cell lines were used to investigate the anticancer and cellular antioxidant profile of loaded and unloaded nanoparticles. Results revealed reactive oxygen species activity of 90 ± 0.32% with the addition of 89 ± 0.64% of its anticancer behavior shown by Cu-Q-PCL-NPs-E after 6 and 24h. Similarly, 80 ± 0.53% inhibition of melanocyte cells and 95 ± 0.54% increase of keratinocyte cells were also shown by Cu-Q-PCL-NPs-E that confirmed the tyrosinase enzyme inhibitory effect. Conclusively, the use of zinc and copper complex in unloaded and vitamin E–loaded nanoparticles can provide enhanced antioxidant properties with inhibition of melanin, which can be used for treating diseases of melanogenesis.
Frustrated ground states of a generalized XY model and their mapping to nonmagnetic structural analogs

Ground state phases of a generalized XY model with magnetic and generalized nematic couplings on a non-bipartite triangular lattice are investigated in the exchange interactions parameter space. We demonstrate that the model displays a number of ordered and quasi-ordered phases as a result of geometrical frustration and/or competition between the magnetic and generalized nematic interactions. The nature and the extent of the respective phases depend on the parameter $q$, characterizing the higher-order harmonics term in the Hamiltonian. Motivated by the recent discovery of the experimental realization of the model with $q=2$ in the seemingly unrelated field of the systems chemistry [A.B. Cairns et al., Nature Chemistry 8, 442 (2016)], the model for $q>2$ is discussed in the context of the prediction of structural phases of a class of bimetalic cyanides based on a mapping between the two systems.
Evaluating a Conceptual Model for Measuring Gaming Experience: A Case Study of Stranded Away Platformer Game

Video games have become a ubiquitous form of entertainment and have been enjoyed by people of all ages around the world. The gaming industry has evolved rapidly, with new games being released every year that push the boundaries of technology and creativity. To ensure that video games are not just technically advanced, but also enjoyable and engaging, measuring the gaming experience is essential because it helps game designers understand how players interact with the game and identify areas for its improvement. The objective of this paper is to examine an interplay of gaming experience dimensions in the context of platform video games and to determine the extent to which they contribute to players’ behavioral intentions. To fulfil this objective, an empirical study was undertaken, involving participants with diverse gaming backgrounds. They were requested to engage in the gameplay of the Stranded Away platformer game and subsequently respond to a post-use questionnaire. The psychometric features of the introduced conceptual model were evaluated with the partial least squares structural equation modeling (PLS-SEM) method. The reported findings demonstrate the importance of evaluating different facets of the gaming experience in video games and showcase the potential of the proposed model and measuring instrument as tools for game designers to enhance the overall quality of their products.
Potential Therapeutic Functions of PU-91 and Quercetin in Personalized Cybrids Derived from Patients with Age-Related Macular Degeneration, Keratoconus, and Glaucoma

The aim of this study is to investigate the therapeutic potential of higher doses of PU-91, quercetin, or in combination on transmitochondrial cybrid cell lines with various mtDNA haplogroups derived from patients with age-related macular degeneration (AMD), glaucoma (Glc), keratoconus (KC), and normal (NL) individuals. Cybrids were treated with PU-91 (P) (200 µM) alone, quercetin (Q) (20 µM) alone, or a combination of PU-91 and quercetin (P+Q) for 48 h. Cellular metabolism and the intracellular levels of reactive oxygen species (ROS) were measured by MTT and H2DCFDA assays, respectively. Quantitative real-time PCR was performed to measure the expression levels of genes associated with mitochondrial biogenesis, antioxidant enzymes, inflammation, apoptosis, and senescence pathways. PU-91(P) (i) improves cellular metabolism in AMD cybrids, (ii) decreases ROS production in AMD cybrids, and (iii) downregulates the expression of LMNB1 in AMD cybrids. Combination treatment of PU-91 plus quercetin (P+Q) (i) improves cellular metabolism in AMD, (ii) induces higher expression levels of TFAM, SOD2, IL6, and BAX in AMD cybrids, and (iii) upregulates CDKN1A genes expression in all disease cybrids. Our study demonstrated that the P+Q combination improves cellular metabolism and mitochondrial biogenesis in AMD cybrids, but senescence is greatly exacerbated in all cybrids regardless of disease type by the P+Q combined treatment.
Evaluation of Subtle Auditory Impairments with Multiple Audiological Assessments in Normal Hearing Workers Exposed to Occupational Noise

Recent studies involving guinea pigs have shown that noise can damage the synapses between the inner hair cells and spiral ganglion neurons, even with normal hearing thresholds—which makes it important to investigate this kind of impairment in humans. The aim was to investigate, with multiple audiological assessments, the auditory function of normal hearing workers exposed to occupational noise. Altogether, 60 workers were assessed (30 in the noise-exposure group [NEG], who were exposed to occupational noise, and 30 in the control group [CG], who were not exposed to occupational noise); the workers were matched according to age. The following procedures were used: complete audiological assessment; speech recognition threshold in noise (SRTN); speech in noise (SN) in an acoustic field; gaps-in-noise (GIN); transient evoked otoacoustic emissions (TEOAE) and inhibitory effect of the efferent auditory pathway; auditory brainstem response (ABR); and long-latency auditory evoked potentials (LLAEP). No significant difference was found between the groups in SRTN. In SN, the NEG performed worse than the CG in signal-to-noise ratio (SNR) 0 (p-value 0.023). In GIN, the NEG had a significantly lower percentage of correct answers (p-value 0.042). In TEOAE, the NEG had smaller amplitude values bilaterally (RE p-value 0.048; LE p-value 0.045) and a smaller inhibitory effect of the efferent pathway (p-value 0.009). In ABR, the NEG had greater latencies of wave V (p-value 0.017) and interpeak intervals III-V and I-V in the LE (respective p-values: 0.005 and 0.04). In LLAEP, the NEG had a smaller P3 amplitude bilaterally (RE p-value 0.001; LE p-value 0.002). The NEG performed worse than the CG in most of the assessments, suggesting that the auditory function in individuals exposed to occupational noise is impaired, even with normal audiometric thresholds.
A New Variable Step-size LMS Adaptive Filtering Algorithm and its Performance in Time Delay Estimation

For the contradition of existing variable step-size adaptive algorithms between convergence speed and steady-state error,a new variable step-size least mean square(LMS) adaptive filtering algorithm is proposed.Based on a function of the class S,this new algorithm introduces a reference of adjustment factor P to perform real-time adjustments of the shape of step function,and adjusts the step according to error's autocorrelation mean value so that the convergence is faster innitially and the step changes more smoothly at steady state.The introduction of maximum likelihood weighted algorithm further suppresses the spurious peaks of adaptive filter weights.The new algorithm with maximum likelihood weighted is applied in adaptive time delay estimation experiment,and the result shows under the condition of the existing fixed parameters,the newly-proposed algorithm has faster convergence and smaller steady-state error.Meanwhile,accurate delay estimation when SNR is above-3 dB can be achieved effectively.
A Review on Contactless Charging for Electric Vehicles

The limited and depleting supply of fossil fuels made the scientists around the world to look for alternatives. One such alternative is the use of Electrical Vehicles instead of conventional internal combustion engines, which are major consumers of fossil fuels. This fast emergence of electric vehicles worldwide has brought up the need for design of effective and convenient charging methods and systems for their batteries. The commonly used plug-in charging method has some limitations such as safety and operation in certain weather conditions. Researchers are in continuous search of new technology that can overcome these limitations and make the EVs more economical. Therefore, a novel method to charge the battery through induction, without any physical contact has been proposed. This paper presents a literature review on the Contactless Power Transfer technology used in EV charging. A possible future technology to improve the range using roadway electrification and in-motion power transfer concepts, which will further help in reduction in the size and cost requirement of the battery. This will help in reducing the range anxiety and will result in better satisfaction of the customer’s needs and expectations.