Miracle-dz/UniFAR-FTUs huggingface dataset:
Limit Groups over Coherent Right-Angled Artin Groups Are Cyclic Subgroup Separable

We prove that cyclic subgroup separability is preserved under exponential completion for groups that belong to a class that includes all coherent RAAGs and toral relatively hyperbolic groups; we do so by exploiting the structure of these completions as iterated free products with commuting subgroups. From this we deduce that the cyclic subgroups of limit groups over coherent RAAGs are separable, answering a question of Casals-Ruiz, Duncan and Kazachov. We also discuss relations between free products with commuting subgroups and the word problem, and recover the fact that limit groups over coherent RAAGs and toral relatively hyperbolic groups have a solvable word problem.
Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification

Automated classification of animal vocalisations is a potentially powerful wildlife monitoring tool. Training robust classifiers requires sizable annotated datasets, which are not easily recorded in the wild. To circumvent this problem, we recorded four primate species under semi-natural conditions in a wildlife sanctuary in Cameroon with the objective to train a classifier capable of detecting species in the wild. Here, we introduce the collected dataset, describe our approach and initial results of classifier development. To increase the efficiency of the annotation process, we condensed the recordings with an energy/change based automatic vocalisation detection. Segmenting the annotated chunks into training, validation and test sets, initial results reveal up to 82% unweighted average recall (UAR) test set performance in four-class primate species classification.
RewardRating: A Mechanism Design Approach to Improve Rating Systems

Nowadays, rating systems play a crucial role in the attraction of customers to different services. However, as it is difficult to detect a fake rating, fraudulent users can potentially unfairly impact the rating’s aggregated score. This fraudulent behavior can negatively affect customers and businesses. To improve rating systems, in this paper, we take a novel mechanism-design approach to increase the cost of fake ratings while providing incentives for honest ratings. However, designing such a mechanism is a challenging task, as it is not possible to detect fake ratings since raters might rate a same service differently. Our proposed mechanism RewardRating is inspired by the stock market model in which users can invest in their ratings for services and receive a reward on the basis of future ratings. We leverage the fact that, if a service’s rating is affected by a fake rating, then the aggregated rating is biased toward the direction of the fake rating. First, we formally model the problem and discuss budget-balanced and incentive-compatibility specifications. Then, we suggest a profit-sharing scheme to cover the rating system’s requirements. Lastly, we analyze the performance of our proposed mechanism.
Classification of Raman spectra of single cells with autofluorescence suppression by wavelength modulated excitation

Wavelength modulated Raman spectroscopy has recently been shown to suppress the fluorescence background generated by the sample and the substrate. Here we apply this technique to collect wavelength modulated Raman spectra from 697 individual cells for a model system of circulating tumour cells that consists of leukocytes from patient's blood, acute myeloid leukaemia cells (OCI-AML3), and breast tumour cells BT-20 and MCF-7. We study the classification behaviour of wavelength modulated Raman spectra in comparison to a common background correction method in chemometrics. Classifications using a support vector machine with a radial based kernel function were compared for classical Raman spectra, average Raman spectra of each cell and wavelength modulated Raman spectra. The dataset was divided into 80% training spectra and 20% independent validation spectra. The stability of the classification was tested by performing training and validation 200 times with randomly selected datasets. The results are displayed in box whisker plots. Cell identification based on wavelength modulated Raman spectra gives similar classification rates than classical and averaged Raman spectra with a tendency of reduced accuracies and increased modelling variations. Possible explanations and strategies to further improve the wavelength modulated Raman spectroscopy are discussed.
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization

Automatic video summarization is still an unsolved problem due to several challenges. The currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking video dataset called VISIOCITY (VIdeo SummarIzatiOn based on Continuity, Intent and DiversiTY) which comprises of longer videos across six different categories with dense concept annotations capable of supporting different flavors of video summarization and other vision problems. For long videos, human reference summaries necessary for supervised video summarization techniques are difficult to obtain. We explore strategies to automatically generate multiple reference summaries from indirect ground truth present in VISIOCITY. We show that these summaries are at par with human summaries. We also present a study of different desired characteristics of a good summary and demonstrate how it is normal to have two good summaries with different characteristics. Thus we argue that evaluating a summary against one or more human summaries and using a single measure has its shortcomings. We propose an evaluation framework for better quantitative assessment of summary quality which is closer to human judgment. Lastly, we present insights into how a model can be enhanced to yield better summaries. Sepcifically, when multiple diverse ground truth summaries can exist, learning from them individually and using a combination of loss functions measuring different characteristics is better than learning from a single combined (oracle) ground truth summary using a single loss function. We demonstrate the effectiveness of doing so as compared to some of the representative state of the art techniques tested on VISIOCITY. We release VISIOCITY as a benchmarking dataset and invite researchers to test the effectiveness of their video summarization algorithms on VISIOCITY.
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization

This paper proposes an adversarial attack method to deep neural networks (DNNs) for monocular depth estimation, i.e., estimating the depth from a single image. Single image depth estimation has improved drastically in recent years due to the development of DNNs. However, vulnerabilities of DNNs for image classification have been revealed by adversarial attacks, and DNNs for monocular depth estimation could contain similar vulnerabilities. Therefore, research on vulnerabilities of DNNs for monocular depth estimation has spread rapidly; however, many of them assume white-box conditions where inside information of DNNs is available or are transferability-based black-box attacks that require a substitute DNN model and a training dataset. Utilizing Evolutionary Multi-objective Optimization, the proposed method in this paper analyzes DNNs under the black-box condition where only output depth maps are available. In addition, the proposed method does not require a substitute DNN that has a similar architecture to the target DNN nor any knowledge about training data used to train the target model. Experimental results showed that the proposed method succeeded in attacking two DNN-based methods trained with indoor and outdoor scenes, respectively.
Artificial neural network approach for turbulence models: A local framework

A local artificial neural network (LANN) framework is developed for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by artificial neural network (ANN) based on the local coordinate system which is orthogonal to the curved walls. We verify the proposed model for the flows over periodic hills. The correlation coefficients of the RANS unclosed terms predicted by the LANN model can be made larger than 0.96 in an a priori analysis, and the relative error of the unclosed terms can be made smaller than 18%. In an a posteriori analysis, detailed comparisons are made on the results of RANS simulations using the LANN and Spalart-Allmaras (SA) models. It is shown that the LANN model performs better than the SA model in the prediction of the average velocity, wall-shear stress and average pressure, which gives the results that are essentially indistinguishable from the direct numerical simulation (DNS) data. The LANN model trained in low Reynolds number Re = 2800 can be directly applied in the cases of high Reynolds numbers Re = 5600, 10595, 19000, 37000 with accurate predictions. Furthermore, the LANN model is verified for flows over periodic hills with varying slopes. These results suggest that the LANN framework has a great potential to be applied to complex turbulent flows with curved walls.
Leveraging 3d Information In Unsupervised Brain Mri Segmentation

Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated images for each pathology, a strenuous task. To tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a Variational Autoencoder (VAE). Previous work on UAD adopted 2D approaches, meaning that MRIs are processed as a collection of independent slices. Yet, it does not fully exploit the spatial information contained in MRI. Here, we propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side contribution, we present a new loss function guarantying a robust training. Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions. Experiments demonstrate the interest of 3D methods which outperform their 2D counterparts.
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics. We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation. We further develop an efficient algorithm to directly render our novel neural SDF representation in real-time by querying only the necessary LODs with sparse octree traversal. We show that our representation is 2–3 orders of magnitude more efficient in terms of rendering speed compared to previous works. Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics.
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem

This letter considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each agent locally observes a set of data points, and the agents’ goal is to compute a linear model that best fits the collective data points observed by all the agents. We consider a server-based distributed architecture where the agents interact with a common server to solve the problem; however, the server cannot access the agents’ data points. We consider a practical scenario wherein the system either has observation noise, i.e., the data points observed by the agents are corrupted, or has process noise, i.e., the computations performed by the server and the agents are corrupted. In noise-free systems, the recently proposed distributed linear regression algorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method, has been claimed to converge faster than related methods. In this letter, we study the robustness of the IPG method, against both the observation noise and the process noise. We empirically show that the robustness of the IPG method compares favorably to the state-of-the-art algorithms.
Indoor Group Activity Recognition using Multi-Layered HMMs

Discovery and recognition of Group Activities (GA) based on imagery data processing have significant applications in persistent surveillance systems, which play an important role in some Internet services. The process is involved with analysis of sequential imagery data with spatiotemporal associations. Discretion of video imagery requires a proper inference system capable of discriminating and differentiating cohesive observations and interlinking them to known ontologies. We propose an Ontology based GAR with a proper inference model that is capable of identifying and classifying a sequence of events in group activities. A multi-layered Hidden Markov Model (HMM) is proposed to recognize different levels of abstract GA. The multi-layered HMM consists of N layers of HMMs where each layer comprises of M number of HMMs running in parallel. The number of layers depends on the order of information to be extracted. At each layer, by matching and correlating attributes of detected group events, the model attempts to associate sensory observations to known ontology perceptions. This paper demonstrates and compares performance of three different implementation of HMM, namely, concatenated N-HMM, cascaded C-HMM and hybrid H-HMM for building effective multi-layered HMM.
Social Network Conceptualization and Operationalization of Hierarchy Within Therapeutic Communities

In the United States, the Therapeutic Community (TC) treatment model for substance use disorder recovery relies heavily upon the assumption that peers serve as mentors, affirming behavior consistent with the norms of the treatment model and correcting behavior contrary to those norms. Despite being foundational to the TC model, little work has been done to clearly conceptualize and operationalize this hierarchy. In this manuscript, we fill that gap in the literature, presenting a novel conceptualization of hierarchy within the TC context, complete with a complementary measurement drawing from the field of social network analysis. With this new framework for understanding and studying hierarchy within TCs, we can ask and rigorously answer a new set of questions previously unexamined.
Age-Friendly Health Care: A Systematic Review

Background: Health care provided to older adults must take into account the characteristics of chronic diseases and the comorbidities resulting from ageing. However, health services are still too oriented towards acute situations. To overcome this problem, the World Health Organization (WHO) proposed a set of Age-Friendly Principles that seek to optimize the provision of health care for this population. This article aims to understand how such Principles are considered in the implementation of age-friendly health care worldwide. Methods: A systematic review was conducted to synthesize the literature on age-friendly health care in accordance with the PRISMA recommendations in the PubMed, Web of Science, and Scopus databases. Results: The research identified 34 articles, with only seven recognizing the WHO Principles and only four using the implementation toolkit. In addition, in the context of primary care, three studies recognize the WHO Principles, but only two use the toolkit. Conclusions: The WHO Principles are being implemented in health care, but in a smaller scale than desired, which reveals possible flaws in their dissemination and standardization. Thus, a greater scientific investment in age-friendly health care should be considered, which represents a greater operationalization of the Principles and an evaluation of their effectiveness and impacts.
Cecal volvulus complicated by evisceration case report

Abstract This case of bowel obstruction with multiple postoperative complications provides unique insight into the challenges faced by providers caring for intellectually disabled patients with acute surgical abdominal pathology and poor compliance. In this case, the component separation was utilized as a method of facilitated wound closure and compliance in a postoperative course highlighted by both dehiscence and wound infection. The patient, only able to communicate the presence of abdominal pain due to his disability, was surgically managed for a bowel obstruction secondary to a cecal volvulus. The difficulty in initial communication and patient noncompliance help illustrate the individualized care these patients require. This report will demonstrate both the challenges present in the management of intellectually disabled patients with abdominal wounds, as well as the use of component separation in providing both initial wound closure and continued wound integrity with the goal of reducing postoperative complications in patients with decreased compliance.
Removal of arsenic from simulated groundwater by adsorption using iron-modified rice husk carbon

This study focused on the removal of arsenic from simulated groundwater by batch adsorption using iron-modified rice husk carbon (RH-Fe). The results showed that RH-Fe was very effective in the removal of arsenic not only at low and moderate initial concentrations of arsenic (1.42 and 2.77 mg/L) but also at very high initial concentrations of arsenic (4.61 and 7.38 mg/L). The arsenic adsorption by RH-Fe was dependent on pH and varied with arsenic initial concentration and adsorbent dose. Langmuir isotherm could describe the adsorption equilibrium and the adsorption capacity was found to be 2.24mg/g. The pseudo-second order kinetic model gave the best fit with the experimental data.
Characterization of Ultrafine Particles and VOCs Emitted from a 3D Printer

Currently, widely available three-dimensional (3D) printers are very popular with the public. Previous research has shown that these printers can emit ultrafine particles (UFPs) and volatile organic compounds (VOCs). Several studies have examined the emissivity of filaments from 3D printing, except glycol modified polyethylene terephthalate (PETG) and styrene free co-polyester (NGEN) filaments. The aim of this study was to evaluate UFP and VOC emissions when printing using a commonly available 3D printer (ORIGINAL PRUSA i3 MK2 printer) using PETG and NGEN. The concentrations of UFPs were determined via measurements of particle number concentration and size distribution. A thermal analysis was carried out to ascertain whether signs of fiber decomposition would occur at printing temperatures. The total amount of VOCs was determined using a photoionization detector, and qualitatively analyzed via gas chromatography-mass spectrometry. The total particle concentrations were 3.88 × 1010 particles for PETG and 6.01 × 109 particles for NGEN. VOCs at very low concentrations were detected in both filaments, namely ethylbenzene, toluene, and xylene. In addition, styrene was identified in PETG. On the basis of our results, we recommend conducting additional measurements, to more accurately quantify personal exposure to both UFPs and VOCs, focusing on longer exposure as it can be a source of potential cancer risk.
Strong correspondence between prefrontal and visual representations during emotional perception

Emotion is thought to cause focal enhancement or distortion of certain components of memory, indicating a complex property of emotional modulation on memory rather than simple enhancement. However, the neural basis for detailed modulation of emotional memory contents has remained unclear. Here has been shown that the information processing of the prefrontal cortex differentially affects sensory representations during experience of emotional information compared with neutral information, using functional magnetic resonance imaging (fMRI). It was found that during perception of emotional pictures, information representation in primary visual cortex (V1) significantly corresponded with the representations in dorsolateral prefrontal cortex (dlPFC). This correspondence was not observed for neutral pictures. Furthermore, participants with greater correspondence between visual and prefrontal representations showed better memory for high‐level semantic components but not for low‐level visual components of emotional stimuli. These results suggest that sensory representation during experience of emotional stimuli, compared with neutral stimuli, is more directly influenced by internally generated higher‐order information from the prefrontal cortex.
A live attenuated-vaccine model confers cross-protective immunity against different species of the Leptospira genus

Leptospirosis is the leading zoonotic disease in terms of morbidity and mortality worldwide. Effective prevention is urgently needed as the drivers of disease transmission continue to intensify. The key challenge has been developing a widely applicable vaccine that protects against the >300 serovars that can cause leptospirosis. Live attenuated mutants are enticing vaccine candidates and poorly explored in the field. We evaluated a recently characterized motility-deficient mutant lacking the expression of a flagellar protein, FcpA. Although the fcpA- mutant has lost its ability to cause disease, transient bacteremia was observed. In two animal models, immunization with a single dose of the fcpA- mutant was sufficient to induce a robust anti-protein antibodies response that promoted protection against infection with different pathogenic Leptospira species. Furthermore, characterization of the immune response identified a small repertoire of biologically relevant proteins that are highly conserved among pathogenic Leptospira species and potential correlates of cross-protective immunity.
Strive to Reduce Slurry Erosion and Cavitation in Pumps through Flow Modifications, Design Optimization and Some Other Techniques: Long Term Impact on Process Industry

Centrifugal pumps are being widely used in various industries for moving fluids that carry solids through pipelines where the need of head and flow rate is not high. Slurry erosion and cavitation are an extremely complex and not yet fully understood phenomenon that occur in centrifugal pumps; however, these undesirable phenomena can be reduced to a certain extent. Appropriate design and development of experiments is required to reasonably predict slurry erosion and cavitation. However, CFD methodology complements analytical solutions and experiments whenever testing of equipment has limitations. The current paper highlights the various slurry erosion and cavitation reduction techniques utilized by different researchers. Economic analysis conducted for a case study relevant to centrifugal pump (CP) usage in Pakistan shows that an 8% enhancement in pump efficiency can reduce the life cycle cost to about 17.6%, which could save up to USD 4281 for a single pump annually in Pakistan.