Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest

Clustering algorithms are among the most widely used data mining methods due to their exploratory power and being an initial preprocessing step that paves the way for other techniques. But the problem of calculating the optimal number of clusters (say k) is one of the significant challenges for such methods. The most widely used clustering algorithms like k-means and k-shape in time series data mining also need the ground truth for the number of clusters that need to be generated. In this work, we extended the Symbolic Pattern Forest algorithm, another time series clustering algorithm, to determine the optimal number of clusters for the time series datasets. We used SPF to generate the clusters from the datasets and chose the optimal number of clusters based on the Silhouette Coefficient, a metric used to calculate the goodness of a clustering technique. Silhouette was calculated on both the bag of word vectors and the tf-idf vectors generated from the SAX words of each time series. We tested our approach on the UCR archive datasets, and our experimental results so far showed significant improvement over the baseline.
Tit-for-Token: Understanding Fairness when Forwarding Data by Incentivized Peers in Decentralized Storage Networks

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. The challenge is to conceive incentives that are fair in regard to the income distribution among peers. Despite many systems using tokens to incentivize forwarding data, like Swarm, little is known about the interplay between incentives, storage-, and network-parameters. This paper aims to help fill this gap by developing Tit-for-Token (Tit4Tok), a framework to understand fairness. Tit4Tok realizes a triad of altruism (acts of kindness such as debt forgiveness), reciprocity (Tit-for-Tat's mirroring cooperation), and monetary rewards as desired in the free market. Tit4Tok sheds light on incentives across the accounting and settlement layers. We present a comprehensive exploration of different factors when incentivized peers share bandwidth in a libp2p-based network, including uneven distributions emerging when gateways provide data to users outside the network. We quantified the Income-Fairness with the Gini coefficient, using multiple model instantiations and diverse approaches for debt cancellation. We propose regular changes to the gateway neighborhood and show that our shuffling method improves the Income-Fairness from 0.66 to 0.16. We quantified the non-negligible cost of tolerating free-riding (altruism). The performance is evaluated by extensive computer simulations and using an IPFS workload to study the effects of caching.
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training

Monocular 3D object detection plays a crucial role in autonomous driving. However, existing monocular 3D detection algorithms depend on 3D labels derived from LiDAR measurements, which are costly to acquire for new datasets and challenging to deploy in novel environments. Specifically, this study investigates the pipeline for training a monocular 3D object detection model on a diverse collection of 3D and 2D datasets. The proposed framework comprises three components: (1) a robust monocular 3D model capable of functioning across various camera settings, (2) a selective-training strategy to accommodate datasets with differing class annotations, and (3) a pseudo 3D training approach using 2D labels to enhance detection performance in scenes containing only 2D labels. With this framework, we could train models on a joint set of various open 3D/2D datasets to obtain models with significantly stronger generalization capability and enhanced performance on new dataset with only 2D labels. We conduct extensive experiments on KITTI/nuScenes/ONCE/Cityscapes/BDD100K datasets to demonstrate the scaling ability of the proposed method.
Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents

Previous studies have used prescriptive process monitoring to find actionable policies in business processes and conducted case studies in similar domains, such as the loan application process and the traffic fine process. However, care processes tend to be more dynamic and complex. For example, at any stage of a care process, a multitude of actions is possible. In this paper, we follow the reinforcement approach and train a Markov decision process using event data from a care process. The goal was to find optimal policies for staff members when clients are displaying any type of aggressive behavior. We used the reinforcement learning algorithms Q-learning and SARSA to find optimal policies. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used but provide the staff members with a few more options in certain situations.
African Easterly Waves and Their Association with Precipitation

[1] Summer tropical synoptic-scale waves over West Africa are quantified by the 850 mb meridional wind component from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project. Their relationships with surface precipitation patterns are explored by applying the data from the Tropical Rainfall Measuring Mission satellite in combination with other satellite observations during 1998–2002. Evident wavelet spectral power peaks are seen within a period of 2.5–6 days in both meridional wind and precipitation. The most intense wave signals in meridional wind are concentrated along 15°N–25°N. Wave signals in precipitation and corresponding wavelet cross-spectral signals between these two variables, however, are primarily located at 5°N–15°N, the latitudes of major summer rain events. Southerly wind perturbations tend to lag (lead) precipitation signals south (north) of 15°N. In some cases either an in-phase or out-of-phase relationship can even be found, suggesting two distinct relationships between the waves and convection. Moreover, the lagging relationship (and/or the out-of-phase tendency) is only observed south of 15°N during July–September, indicating a strong seasonal preference. This phase relationship is generally consistent with the horizontal wave structures from a composite analysis.
Optimal rates for ranking a permuted isotonic matrix in polynomial time

We consider a ranking problem where we have noisy observations from a matrix with isotonic columns whose rows have been permuted by some permutation $\pi$ *. This encompasses many models, including crowd-labeling and ranking in tournaments by pair-wise comparisons. In this work, we provide an optimal and polynomial-time procedure for recovering $\pi$ * , settling an open problem in [7]. As a byproduct, our procedure is used to improve the state-of-the art for ranking problems in the stochastically transitive model (SST). Our approach is based on iterative pairwise comparisons by suitable data-driven weighted means of the columns. These weights are built using a combination of spectral methods with new dimension-reduction techniques. In order to deal with the important case of missing data, we establish a new concentration inequality for sparse and centered rectangular Wishart-type matrices.
Modeling the Galactic center gamma-ray emission with more realistic cosmic-ray dynamics

Very-high-energy gamma-ray observations of the Galactic center (GC) show extended emission that is strongly correlated with the morphology of the central molecular zone (CMZ). The best explanation for that emission is a hadronic interaction between cosmic rays (CRs) and ambient gas, where a CR central and continuous source accelerates protons up to 1 PeV ("PeVatron"). However, current models assume very simplistic CR dynamics. Our goal is to verify if more realistic CR dynamics for the GC environment are consistent with current gamma-ray observations, and whether they could be constrained by upcoming observations with the Cherenkov Telescope Array (CTA). We generated synthetic gamma-ray maps using a CR transport model with spherical injection, different diffusion regimes (in and out of the CMZ), polar advection, and mono-energetic particles of 1 PeV, and including different CR populations injected from the Arches, Quintuplet, and nuclear clusters of young massive stars, plus supernova Sgr A East. We adopted two different 3D gas distributions consistent with the observed gas column density, either with or without an inner cavity. In order to reproduce the existing observations detected by the High Energy Stereoscopic System (HESS), a ring-like gas distribution, with its mass set by the standard Galactic CO-to-H$_2$ conversion factor, and CR acceleration from all relevant sources are required. For a conversion factor one order of magnitude lower, injection rates that are ten times higher are needed. We show that CTA will be able to differentiate between models with different CR dynamics, proton sources, and CMZ morphology, owing to its unprecedented sensitivity and angular resolution. More realistic CR dynamics suggest that the CMZ has a large inner cavity and that the GC PeVatron is a composite CR population accelerated by the Arches, Quintuplet, and nuclear star clusters, and Sgr A East.
Unveiling Technorelief: Enhancing Neurodiverse Collaboration with Media Capabilities

As the workforce settles into flexible work arrangements, researchers have focused on the collaborative and psychological consequences of the shift. While nearly a fifth of the world's population is estimated to be neurodivergent, the implications of remote collaboration on the cognitive, sensory, and socio-affective experiences of autistic workers are poorly understood. Prior literature suggests that information and communication technologies (ICTs) introduce major psychological stressors. Theoretically, these stressors ought to be exceptionally straining considering autistic traits $\unicode{x2013}$ yet, studies describe a strong attraction to ICTs. We thus ask: how do digital technologies alleviate autistic workers' experiences of their collaborative work environment? Thirty-three interviews were conducted to address this question. Findings suggest that digital media present capabilities that filter input from the environment, turning it into a virtual stage that lets workers"time out". The resulting"technorelief"enables autistic workers to tune into their perceptions and regain control of their collaborative experiences.
Zones of Progressive Regional Metamorphism in the Moine Schists of the Western Highlands of Scotland

Abstract The Moine Schists of Western Inverness-shire and North-West Argyll show a progressive increase in metamorphism when traced from the outcrop of the Moine Thrust-plane eastwards towards the central area of regional injection and migrnatitization. It is possible to distinguish four zones of progressive metamorphism characterized by the development of distinctive mineral assemblages in the very subordinate, but widely distributed and highly distinctive calc-silicate granulites. The easterly increase in grade is attributed to the thermal effect of the great injection complexes, and it is suggested that the Torridonian, the Tarskavaig Moine Schists, and the Moine Schists proper merely represent successive stages in the regional metamorphism of one formation.
An Efficient Approach to Mitigate Numerical Instability in Backpropagation for 16-bit Neural Network Training

—In this research, we delve into the intricacies of the numerical instability observed in 16-bit computations of machine learning models, particularly when employing popular optimization algorithms such as RMSProp and Adam. This instability is commonly experienced during the training phase of deep neural networks, leading to disrupted learning processes and hindering the effective deployment of such models. We identify the single hyperparameter, epsilon, as the main culprit behind this numerical instability. An in-depth exploration of the role of epsilon in these optimizers within 16-bit computations reveals that a minor adjustment of its value can restore the functionality of RMSProp and Adam, consequently enabling the effective utilization of 16-bit neural networks. We propose a novel method to mitigate the identified numerical instability issues. This method capitalizes on the updates from the Adam optimizer and significantly improves the robustness of the learning process in 16-bit computations. This study contributes to better understanding of optimization in low-precision computations and provides an effective solution to a longstanding issue in training deep neural networks, opening new avenues for more efficient and stable model training.
An exploratory review of resiliency assessments after brain injury

Objective: To identify resiliency measures which have been established for use with people after acquired brain injury, using the process-based Traumatic Brain Injury Resiliency Model as the guiding conceptual framework. Method: Databases CINAHL, EMBASE, Medline, and PsychINFO were searched in August 2023. Using COnsensus based Standards for the selection of Health guidelines of health status Measurement INstruments (COSMIN) guidelines for reporting, articles providing data on psychometric properties for measures of resilience for people with brain injury were retrieved and presented using the Traumatic Brain Injury Resiliency Model. Psychometric properties and clinical utility (number of items, scoring details) were summarized. Results: Nine articles were retrieved, including 9 measures of resiliency. Conclusion: There are established measures of resiliency in brain injury rehabilitation, though no measure contained all three resiliency outcomes identified by the Traumatic Brain Injury Resiliency Model. Future work may explore use of these measures in a clinical context and implementation of rehabilitation goals for improving resiliency after brain injury.
Evolution of five environmentally responsive gene families in a pine‐feeding sawfly, Neodiprion lecontei (Hymenoptera: Diprionidae)

Abstract A central goal in evolutionary biology is to determine the predictability of adaptive genetic changes. Despite many documented cases of convergent evolution at individual loci, little is known about the repeatability of gene family expansions and contractions. To address this void, we examined gene family evolution in the redheaded pine sawfly Neodiprion lecontei, a noneusocial hymenopteran and exemplar of a pine‐specialized lineage evolved from angiosperm‐feeding ancestors. After assembling and annotating a draft genome, we manually annotated multiple gene families with chemosensory, detoxification, or immunity functions before characterizing their genomic distributions and molecular evolution. We find evidence of recent expansions of bitter gustatory receptor, clan 3 cytochrome P450, olfactory receptor, and antimicrobial peptide subfamilies, with strong evidence of positive selection among paralogs in a clade of gustatory receptors possibly involved in the detection of bitter compounds. In contrast, these gene families had little evidence of recent contraction via pseudogenization. Overall, our results are consistent with the hypothesis that in response to novel selection pressures, gene families that mediate ecological interactions may expand and contract predictably. Testing this hypothesis will require the comparative analysis of high‐quality annotation data from phylogenetically and ecologically diverse insect species and functionally diverse gene families. To this end, increasing sampling in under‐sampled hymenopteran lineages and environmentally responsive gene families and standardizing manual annotation methods should be prioritized.
Poor Sleep Quality May Independently Predict Suicidal Risk in Covid-19 Survivors: A 2-Year Longitudinal Study

Objective Multiple symptoms of psychiatric, neurological, and physical illnesses may be part of Post-COVID conditions and may pose COVID-19 survivors a high suicidal risk. Accordingly, we aimed to study factors contributing to suicidal risk in Post COVID-19 patients. Method Consecutive patients with post COVID-19 conditions were followed for 2 years at the University Hospital of Ferrara at baseline (T0), 6 (T1), 12 (T2), and 24 (T3) months. Demographics, and clinical data for all patients included: disease severity, hospital length of stay, comorbidity, clinical complications, sleep quality, cognitive complaints, anxiety and stress-related symptoms, depressive symptoms, and suicidal ideation. Results The final sample included 81 patients with post COVID survivors. The mean age was 64 + 10,6 years, 35,8% were females, 65,4% had medical comorbidities, and 69,1% had WHO severe form of COVID forms. At T0 more than 90% of patients showed poor sleep quality, 59.3% reported moderate/severe depressive symptoms, and 51. % experienced anxiety, 25.9% experienced post-traumatic stress symptoms. At T0 suicidal ideation, interested 6.1% and at T3 it increased to 7.4%. In the regression analysis, suicidal ideation at baseline was best predicted by poor sleep quality (O.R. 1.71, p=0.044) and, after 2 years, suicidal ideation was best predicted by poor sleep quality experienced at baseline (OR 67.3, p=0.001). Conclusions Poor sleep quality may play as an independent predictor of suicidal risk in post-COVID survivors. Evaluating and targeting sleep disturbances in COVID survivors is important to prevent the consequences of disrupted sleep in mental health.
Zoledronate loaded polylactic acid/polycaprolactone/hydroxyapatite scaffold accelerates regeneration and led to enhance structural performance and functional ability of the radial bone defect in rat

Abstract Background: One of the most common concerns in the regeneration of massive bone defects necessitating surgery and bone grafts is the application of tissue engineering using drug delivery. Zoledronate is a well-known effective drug for the healing bone fractures in osteoporotic patients. Aims: An attempt was made to design a more efficient bone scaffold with polycaprolactone, polylactic acid, and hydroxyapatite. Methods: The scaffold was fabricated by freeze-drying and indirect 3D printing approaches. X-ray diffraction, Fourier transform infrared spectroscopy, rheometry, scanning electron microscopy, and neutral red tests were performed to characterize the scaffold. qRT-PCR was also done to define the osteoinductivity and angiogenic induction capacity of this scaffold. Forty rats were selected and randomly divided into four groups: the control group, which received no treatment, the autograft group, scaffold group, and Zol-loaded scaffold group (n=10 in each group). The injured area was studied by radiology, biomechanical analysis, histopathology, histomorphometry, immunohistochemistry, and CT scan analyses. Results: The qRT-PCR results demonstrated significantly higher expression levels of OPN, OCN, and CD31 markers in the scaffold group when compared to the control group (P<0.05). Histopathologically, the newly formed bone tissue was significantly detected in the Zol-loaded scaffold and autograft groups in comparison with the non-treated group (P<0.001). The immunohistochemistry (OC marker), biomechanical, and histomorphometric results indicated a significant improvement in the regeneration of the injured area in the groups treated with autologous bone and Zol-loaded scaffold compared to the non-treated group (P<0.05). Conclusion: The Zol-loaded scaffold accelerated bone regeneration, and led to enhanced structural performance and functional ability of the injured radial bone in rats.
Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan

Background: The choroid plexus functions as the blood-cerebrospinal fluid barrier, plays an important role in neurofluid production and circulation, and has gained increased attention in light of the recent elucidation of neurofluid circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan. Methods: Fully convolutional neural networks were trained from 3-D T1-weighted, 3-D T2-weighted, and 2-D T2-weighted FLAIR MRI and gold-standard manual segmentations in healthy and neurodegenerative participants across the lifespan (n=50; age=21–85 years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p<0.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of healthy adults (n=98; age=21–89 years) to provide an exemplar of choroid plexus volumetry values across the lifespan. Results: Deep learning results yielded Dice coefficient=0.72, Hausdorff distance=1.97 mm, AUC=0.87 for T1-weighted MRI, Dice coefficient=0.72, Hausdorff distance=2.22 mm, AUC=0.87 for T2-weighted MRI, and Dice coefficient=0.74, Hausdorff distance=1.69 mm, AUC=0.87 for T2-weighted FLAIR MRI; values did not differ significantly between2 MRI sequences and were statistically improved compared to current commercially-available algorithms (p<0.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T1-weighted and T2-FLAIR, T1-weighted and T2-weighted, and T2-weighted and T2-FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20±1.4 cm3; a significant, positive relationship (R2=0.54; slope=0.047) was observed between participant age and choroid plexus volume for all MRI sequences (p<0.001). Conclusions: Findings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function (https://github.com/hettk/chp_seg).