Miracle-dz/UniFAR-FTUs huggingface dataset:
Nilearn-based Multimodal Fusion Analysis for Integrating Brain Image Analysis

The integration of multiple brain imaging modalities has become a prominent research approach for understanding complex neural processes. The present study focuses on the application of Nilearn, a Python library for neuroimaging analysis in multimodal fusion analysis. The aim is to combine data from different modalities, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG), to gain comprehensive insights into brain function. The research article presents a detailed workflow using Nilearn for multimodal fusion analysis. Two key multimodal fusion techniques, joint independent component analysis (jICA) and connectome-based fusion, implemented using Nilearn, are discussed in detail in this study. The workflow presented provides researchers with a systematic approach to integrate and interpret brain imaging data, facilitating a deeper understanding of the complex neural mechanisms underlying brain function and connectivity.
Addressing Drawbacks of MART and Enhancing Predictive Performance*

Boosting, a successful machine learning algorithm, is often used for regression and classification tasks. Boosted ensemble algorithms, particularly when combined with tree base learners, can generate highly accurate and robust predictive models. This study explores the weaknesses of the well-known approach, Multiple Additive Regression Trees (MART), and proposes a new approach called Laplace Additive Regression Trees (LART) that addresses this issue by adding random Laplace noises. The algorithm is then improved upon by combining it with Dropout and multiple Additive Regression Trees, resulting in DLART [1]. In an experiment using a previously studied dataset, we compared the performance of LART, DLART, MART, and DART. The results were promising, as LART and DLART produced significant gains in accuracy for this particular task. For future studies, it would be beneficial to assess the effectiveness of LART on complex data sets and various other machine learning tasks, such as those related to data mining [2]
Abstractive Multi-Document Summarization: Exploiting Maximal Marginal Relevance and Pretrained Models

The increasing demand for effective multi-document summarization (MDS) due to the rapid expansion of information availability has triggered this investigation. Focused on the fusion of Maximal Marginal Relevance (MMR) with pretrained models, we circumvented the often-cumbersome fine-tuning process, and introduced a novel methodology. We applied this to various text datasets like Multi-News and WCEP, and it resulted in significant improvement of ROUGE scores, illustrating promising results. For example, the ROUGE-1 score of our method on the Multi-News dataset was 46.23, and the ROUGE-1 score on the WCEP dataset was 30.74. In this discussion, the research implications, including better n-gram optimization and model selection strategies, were brought forward. This study had substantial implications for natural language processing, propelling us toward more advanced text summarization applications, presenting potential avenues for future research, such as refining the role of n-grams in summaries and optimizing model selection processes.
Automatic Vehicle Identification by Plate Recognition

Automatic Vehicle Identification (AVI) has many applications in traffic systems (highway electronic toll collection, red light violation enforcement, border and customs checkpoints, etc.). License Plate Recognition is an effective form of AVI systems. In this study, a smart and simple algorithm is presented for vehicle’s license plate recognition system. The proposed algorithm consists of three major parts: Extraction of plate region, segmentation of characters and recognition of plate characters. For extracting the plate region, edge detection algorithms and smearing algorithms are used. In segmentation part, smearing algorithms, filtering and some morphological algorithms are used. And finally statistical based template matching is used for recognition of plate characters. The performance of the proposed algorithm has been tested on real images. Based on the experimental results, we noted that our algorithm shows superior performance in car license plate recognition. Keywords—Character recognizer, license plate recognition, plate region extraction, segmentation, smearing, template matching.
Convolutional Neural Networks for Automated Glaucoma Detection: Performance and Limitations

Glaucoma is a set of eye disorders that, if left untreated, can cause optic nerve damage, resulting in vision loss and blindness. While glaucoma is often linked with high eye stress, it can also develop with normal or low pressure. The most common variety, primary open-angle glaucoma, is known as the silent thief of sight because it causes slow vision loss with no symptoms. Ethnicity, age, diabetes, bloodline, and hypertension are all dangerous factors. Regular eye exams are crucial for early detection. To aid in glaucoma detection, a model utilizing eye fundus images is proposed. Fundus images provide valuable information about the optic nerve’s health and abnormalities. The model employs a Convolutional Neural Network (CNN) to classify fundus images and detect glaucoma. By automating the process, the proposed system aims to improve accuracy. This CNN-based model has the potential to enhance glaucoma detection, enabling prompt interventions and better patient outcomes.
Holistically Budgeting Processing Graphs

To certify the schedulability of a system, valid per-task worst-case execution-time (WCET) estimates are almost always required. Unfortunately, on multicore machines, deriving WCET estimates through static analysis that is not highly pessimistic may never be a practical reality. The alternative is to determine WCETs via a measurement process, but such a process cannot correctly produce accurate WCET estimates with certainty. This lack of certainty necessitates the use of overrun-handling mechanisms, such as budget-enforcement techniques, to preserve temporal correctness at runtime. In many systems of interest today, tasks are interconnected to form processing graphs, which can be quite large. The simplest (and perhaps most common) approach to budget enforcement in this case is to abort an entire graph invocation whenever any node (task) overruns its budget. However, such an approach can result in a high abort rate at the graph level even when the per-node abort rate is low. To remedy this situation, this paper presents a holistic budget-management strategy for directed acyclic graphs (DAGs) that involves reallocating per-node budgets to overrunning nodes to avoid DAG-Ievel aborts. To enable the effects of aborts to be studied analytically, a probabilistic analysis is presented to derive a DAG's abort rate under the proposed budget-management strategy. Experimental results are also presented to demonstrate the utility of budgeting graphs holistically.
Fine-grained Unsupervised Domain Adaptation for Gait Recognition

Gait recognition has emerged as a promising technique for the long-range retrieval of pedestrians, providing numerous advantages such as accurate identification in challenging conditions and non-intrusiveness, making it highly desirable for improving public safety and security. However, the high cost of labeling datasets, which is a prerequisite for most existing fully supervised approaches, poses a significant obstacle to the development of gait recognition. Recently, some unsupervised methods for gait recognition have shown promising results. However, these methods mainly rely on a fine-tuning approach that does not sufficiently consider the relationship between source and target domains, leading to the catastrophic forgetting of source domain knowledge. This paper presents a novel perspective that adjacent-view sequences exhibit overlapping views, which can be leveraged by the network to gradually attain cross-view and cross-dressing capabilities without pre-training on the labeled source domain. Specifically, we propose a fine-grained Unsupervised Domain Adaptation (UDA) framework that iteratively alternates between two stages. The initial stage involves offline clustering, which transfers knowledge from the labeled source domain to the unlabeled target domain and adaptively generates pseudo-labels according to the expressiveness of each part. Subsequently, the second stage encompasses online training, which further achieves cross-dressing capabilities by continuously learning to distinguish numerous features of source and target domains. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on widely-used public gait datasets.
Epigenetics of inﬂammatory bowel disease

Summary Understanding the causes and molecular mechanisms of Crohn’s disease and ulcerative colitis, the two forms of idiopathic chronic inﬂammatory bowel disease (IBD), is a major challenge in gastroenterology research. Signiﬁcant e V ort has been invested in uncovering genetic and environmental factors which may increase the risk of IBD, but progress has been slow, and no IBD speciﬁc factors have been detected so far. In this article we suggest that in addition to mutant genes and a hazardous environment, epigenetic factors may be relevant in understanding the aetiopathogenesis of Crohn’s disease and ulcerative colitis. This hypothesis is based on a number of clinical and molecular ﬁndings in IBD studies such as: (i) the maternal e V ect in transmission of IBD from a V ected parent to o V spring; (ii) parental di V erences in the degree of genetic anticipation; (iii) discordance of monozygotic twins a V ected by IBD; (iv) genetic association studies demonstrating evidence for aberrant regulation of expression of cytokine genes; (v) sex e V ects in association with HLA alleles and haplotypes; and (vi) epigenetic aspects of IBD treatment. It is suggested that inherited and/or acquired epigenetic defects, or epimutations, may be of aetiological and pathogenic importance in IBD. Epigenetic studies of Crohn’s disease and ulcerative colitis may have a signiﬁcant impact on the ﬁeld of IBD research.
Strong Stereo Features for Self-Supervised Practical Stereo Matching

This paper proposes a new approach to the problem of self-supervised dense stereo correspondence. Disparity estimation is an important problem of computer vision but in many situations, correspondence-based reconstruction cannot be accompanied by a ground truth to train supervised methods. This paper proposes to train a siamese feature encoder in a self-supervised permutation framework and then build a cost volume which is fed to a classical stereo algorithm to compute the disparity. In the absence of ground truth disparity, rather than handcrafting features, we suggest a novel and straightforward way to leverage input images to train for features. A key aspect of this method is that all the trainable weights are located inside a feature computation step, which is followed by strong non-trainable constraints that enforce bidirectional correspondence through cross-attention. Validated on real and synthetic datasets and compared to various methods, our proposed approach yields competitive results. Given its high performance, simplicity, and direct integration with current stereo algorithms, we expect this method to further the adoption of deep methods in real life stereo applications.
Cloud Responses to Abrupt Solar and CO2 Forcing Part I: Temperature Mediated Cloud Feedbacks

The third phase of the Cloud Feedback Model Intercomparison Project requested that modeling centers perform a pair of simulations where the climate system is subjected to an abrupt change of the solar constant by +/- 4%. The forcing is designed to loosely match the amount of radiative forcing incurred by quadrupling atmospheric CO2 concentrations. Using these simulations, we examine how clouds respond to changes in solar forcing and act as a feedback on global surface temperature. Speciﬁcally, in this paper, we study the temperature mediated cloud changes that occur following an abrupt increase and decrease of the solar constant and compare with temperature mediated cloud changes that occur following quadrupling and halving of CO2. We seek to answer two primary questions: 1) How do cloud feedbacks diﬀer in response to abrupt changes in CO2 and solar forcing? And 2) Are there symmetrical (equal and opposite) cloud feedbacks to an increase and a decrease in solar forcing? We ﬁnd that temperature mediated cloud changes are similar from increasing solar and CO2 forcing, with the only robust diﬀerence being that there is a larger reduction of
Open Text Classi�cation Based on Dynamic Boundary Balance

Traditional text classiﬁcation tasks require the test set to contain the same classes (known/seen classes) as the training set. However, when the new classes (unknown/unseen classes) appear during testing, the traditional classiﬁer cannot recognize them eﬀectively. As a result, a qualiﬁed open text classiﬁer should be able to classify known classes as well as identify unknown classes, e.i. , open classﬁcation. But current open text classiﬁers perform poorly on the task. To address the issue, we propose a two-stage learning model based on dynamic balance of decision boundary to complete the open text classiﬁcation task. We call it open text classiﬁcation with dynamic boundary balance (OCD2B). First, we construct a traditional classiﬁer based on BERT(Bidirectional Encoder Representation from Transformers) model to classify the known classes. Second, the prior knowledge of known classes is used to dynamically determine the decision boundaries between known classes and unknown classes. We propose a boundary loss function as boundary balance strategy to simultaneously reduce open space risk and empirical risk. Experiments on two standard datasets show that our method achieves better performance than existing other methods. In particular, OCD2B has even greater advantages over the others especially when the proportion of unknown classes is large.
Non-interactive VSS using Class Groups and Application to DKG

—We put forward the first non-interactive verifiable secret sharing scheme (NI-VSS) using classgroups – we call it cgVSS . Our construction follows the standard framework of encrypting the shares to a set of recipients and generating a non-interactive proof of correct sharing . However, as opposed to prior works, such as Groth’s [Eprint 2021], or Gentry et al.’s [Eurocrypt 2022], we do not require any range proof - this is possible due to the unique structure of class groups, that enables efficient encryption/decryption of large field elements in the exponent of an ElGamal-style encryption scheme. Importantly, this is possible without destroying the additive homomorphic structures, which is required to make the proof-of-correctness highly efficient. This approach not only substantially simplifies the scheme, but also outperforms the state-of-art schemes significantly. Our implementation shows that cgVSS outperforms (a simplified implementation of) Groth’s protocol in overall communication complexity by 5 . 6 x and about 2 . 4 − 2 . 7 x in computation time per node (for a 150 node system). Additionally, we formalize the notion of public verifiability, which enables anyone, possibly outside the participants, to verify the correctness of the dealing. In fact, we re-interpret the notion of public verifiability and extend it to the setting when all recipients may be corrupt and yet can not defy public verifiability – to distinguish with state-of-art we call this strong public verifiability. Our formalization uses the universal composability framework. Finally, through a generic transformation, similar to Groth’s [Eprint 2021], we obtain a NI-DKG scheme for threshold systems, where the secret key is the discrete log of the public key. Our security analysis in the VSS-hybrid model uses a formalization that also considers a (strong) public verifiability notion for DKG, even when more than threshold parties are corrupt. Instantiating with cgVSS we obtain the first NI-DKG scheme from class groups – we call it cgDKG .
The Effect of Energy Framework in Smart Building Integration into a Smart City

Numerous energy frameworks have been established to assess the criteria for smart buildings. However, most of these frameworks fail to account for the specific standards and needs of individual cities. This study aims to address the gap that exists in many energy frameworks used for evaluating buildings. One of these frameworks, known as the Information and Communication Technology (ICT) Framework, has been explored to bridge the gap and establish connections between the domains of buildings and the unique contexts of the cities in which these buildings are situated. As part of this investigation, a case study was conducted on the Arfa Tower in Vilnius, Lithuania. Additionally, a survey study was undertaken to evaluate energy performance in Jordanian buildings, using a questionnaire that encompasses the key aspects of the ICT-Framework. The results of the study revealed significant progress in the areas of smart energy and smart environment. However, there are challenges in the smart data field, primarily due to the absence of data management technology in buildings. In the Jordanian context, the study found that 30% of the participants do not utilize insulation materials, and 43% do not have insulated windows. This implies an increased rate of heat transfer in these buildings. On the other hand, 61% of homes use Light Emitting Diode (LED) lighting systems, while 81% still rely on traditional lighting control systems. This suggests a lack of modern technologies like sensors for lighting control. Notably, 51% of the surveyed population had heard about smart building technology techniques; however, they did not fully grasp their significance. This highlights the need for energy associations and government agencies to raise awareness about the importance of modern technology in buildings and to activate energy auditing programs for buildings in Jordan.
Edinburgh Research Explorer Age Differences in Intra-Individual Variability in Simple and Choice Reaction Time: Systematic Review and Meta-Analysis

Background: Intra-individual variability in reaction time (RT IIV) is considered to be an index of central nervous system functioning. Such variability is elevated in neurodegenerative diseases or following traumatic brain injury. It has also been suggested to increase with age in healthy ageing. Objectives: To investigate and quantify age differences in RT IIV in healthy ageing; to examine the effect of different tasks and procedures; to compare raw and mean-adjusted measures of RT IIV. DataSources: Four electronic databases: PsycINFO, Medline, Web of Science and EMBASE, and hand searching of reference lists of relevant studies. StudyEligibility: English language journal articles, books or book chapters, containing quantitative empirical data on simple and/or choice RT IIV. Samples had to include younger (under 60 years) and older (60 years and above) human adults. StudyAppraisalandSynthesis: Studies were evaluated in terms of sample representativeness and data treatment. Relevant data were extracted, using a specially-designed form, from the published report or obtained directly from the study authors. Age-group differences in raw and RT-mean-adjusted measures of simple and choice RT IIV were quantified using random effects meta-analyses. Results: Older adults (60 + years) had greater RT IIV than younger (20–39) and middle-aged (40–59) adults. Age effects were larger in choice RT tasks than in simple RT tasks. For all measures of RT IIV, effect sizes were larger for the comparisons between older and younger adults than between older and middle-aged adults, indicating that the age-related increases in RT IIV are not limited to old age. Effect sizes were also larger for raw than for RT-mean-adjusted RT IIV measures. Conclusions: RT IIV is
Bullying on Nursing Students

Bullying on nursing students occurred in more than half of the population. The incidents of bullying left negative impacts on the victims physically, psychologically, and socially. This study identifies the bullying case on nursing care students during academic learning and the roles of the students when bullying occurred. This descriptive quantitative study applied a survey design. This study used Adolescent Peer Relations Instrument (APRI), distributed via Google Form. The respondents were nursing program students that selected convenience sampling. The sample applied some inclusion criteria: the students had to undergo face-to-face learning and online learning. The sampling stage resulted in 207 respondents from three grades. The study analyzed the data with univariate analysis, in the forms of percentages and frequencies. Verbal bullying occurred with a percentage of 31.4%, social bullying with a percentage of 12.16%, and physical bullying with a percentage of 2.32%. The researchers found that 151 respondents, 78.74%, were bullying victims. A percentage of 93.23% of respondents, 193 respondents were women. A percentage of 85.71%, 12 respondents out of 14 respondents, were male. A percentage of 0.96%, 2 respondents, committed the bullying action. A percentage of 45.89%, 95 respondents defended the victims. 100 respondents or 48.45% of participant in this study were outsiders when bullying happened. This study concluded that most students ever experienced bullying cases with the most incidents of verbal bullying. Most students played the role of outsiders during the incidents of bullying
Conchostráceos com linhas de crescimento recurvadas junto à margem dorsal (famílias Palaeolimnadiopseidae e Perilimnadiidae) da formação rio do rasto, Permiano superior, bacia do Paraná, Brasil

Some Upper Permian conchostracans from the Rio do Rasto Formation (Parana Basin, South Brazil) have very characteristic recurved growth lines at the dorsal margin. All previously described specimens were classified as Palaeolimnadiopsis subalata (Reed) Raymond. However, a re-analysis of these fossils and of additional recently- collected specimens demonstrated that not all can be included in a single species, nor only in the Family Palaeolimnadiopseidae. According to their shape and the size of the umbo, they are classified into three species. The sub-elliptic carapaces with small anterior umbo are maintained in Palaeolimnadiopsis subalata (Reed, 1929) Raymond, 1946. The sub-circular carapaces with small sub-central umbo correspond to the new species Palaeolimnadiopsis riorastensis. The small size of the umbo is a character of the Family Palaeolimnadiopseidae. The small elliptic valves with large anterior umbo are assigned to the new species Falsisca brasiliensis of the Family Perilimnadiidae, which is characterized by large umbos. Palaeolimnadiopsis has a wide chronostratigraphic distribution, but Falsisca is restricted to the Upper Permian-Lower Triassic of Europe and Asia. This interval is in agreement with the probable Late Permian age of the respective strata of the Rio do Rasto Formation. Falsisca was not previously recorded in Gondwana.