Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Multifractal Scaling of the Principal Cryptocurrencies: Examination using a Self-Explainable Machine Learning

Multifractal analysis is a forecasting technique used to study the scaling regularity properties of financial returns, to analyze the long-term memory and predictability of financial markets. In this paper, we propose a novel structural detrended multifractal fluctuation analysis (S-MF-DFA) to investigate the efficiency of the main cryptocurrencies. The new methodology generalizes the conventional approach by allowing it to proceed on the different fluctuation regimes previously determined using a change-points detection test. In this framework, the characterization of the various exogenous factors influencing the scaling behavior is performed on the basis of a single-factor model, thus creating a kind of self-explainable machine learning for price forecasting. The proposal is tested on the daily data of the three among the main cryptocurrencies in order to examine whether the digital market has experienced upheavals in recent years and whether this has in some ways led to a structured multifractal behavior. The sampled period ranges from April 2017 to December 2022. We especially detect common periods of local scaling for the three prices with a decreasing multifractality after 2018. Complementary tests on shuffled and surrogate data prove that the distribution, linear correlation, and nonlinear structure also explain at some level the structural multifractality. Finally, prediction experiments based on neural networks fed with multi-fractionally differentiated data show the interest of this new self-explained algorithm, thus giving decision-makers and investors the ability to use it for more accurate and interpretable forecasts.
S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences

Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications. However, they typically require domain expertise, and labor-intensive landmark annotations to construct. We address these shortcomings by proposing an unsupervised method that leverages deep geometric features and functional correspondences to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods, even on highly irregular surface topologies. We demonstrate this for two different anatomical structures: the thyroid and a multi-chamber heart dataset. Furthermore, our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations without manual segmentation annotation.
Use of accelerometers in determining risk of falls in individuals post-stroke: A systematic review

Objective The aim of this systematic review was to determine if the diagnostic capabilities of wearable accelerometers enhanced, provided earlier detection, or improved fall risk assessment when evaluating individuals with chronic stroke. Data sources CINAHL and PubMed databases were searched for articles from 2015 to 2023 utilizing key terms. Review methods A team of researchers reviewed articles for bias via the Quality in Prognostic Studies tool, and further analyzed the data to answer the research question. Results Four studies were included in the systematic review. When utilizing an accelerometer, the vertical axis was most predictive of falls, followed by the medio-lateral axis and the anterior-posterior axis. L2-3 was the most common accelerometer placement for fall risk assessment, however no uniformity existed in the literature on placement, number of accelerometers, or type. It was determined that gait symmetry, the Timed Up and Go, Berg Balance Scale, and Longitudinal Aging Study Amsterdam best predicted falls risk. Conclusion Based on limited available evidence, clinicians should continue to perform a comprehensive examination and evaluation for fall risk, that includes the use of a combination of evidence-based outcome measures and gait characteristics to develop an individualized plan of care for individuals post-stroke. However, further research is necessary to determine the added value of accelerometers as well as type, applicability of data, and placement.
Assessment of physicochemical parameters, bioactive compounds, biological activities, and nutritional value of the most two commercialized pollen types of date palm (Phoenix dactylifera L.) in Morocco.

The pollen of date palm (Phoenix dactylifera L.) is known for its nutritional value and implications as a health-promoting component. Due to its low cost, date palm pollen crushed with its spadix is more widely commercialized and used in Morocco than pure date palm pollen free of spadix. Thus, this study aimed to assess the physicochemical and phytochemical parameters, biological activities, and nutritional value of the two pollen types: Pure date palm pollen and date palm pollen crushed with its spadix. Various physicochemical parameters were determined, including humidity, water activity (aw), total soluble solids, ash content, and color parameters (L*, a*, b*, C*ab, and hab). Additionally, the phenolic compound profiles were analyzed, and the in vitro antioxidant, enzyme inhibitory, and antidiabetic activities were assessed for both pure date palm pollen and date palm pollen crushed with its spadix. Furthermore, the nutritional value was evaluated by determining protein and carbohydrate contents, and mineral and fatty acid profiles. The results have revealed that pure date palm pollen had higher humidity, aw, L*, and hab color parameters than date palm pollen crushed with its spadix, but lower total soluble solid and ash contents. The main phenolic compounds in pure date palm pollen were ellagic acid, rutin, fisetin, and quercetin, whereas date palm pollen crushed with its spadix contained mainly catechin, chlorogenic acid, p-coumaric acid, ferulic acid, and rutin. Moreover, pure date palm pollen showed greater in vitro antioxidant activity, while date palm pollen crushed with its spadix had higher enzyme inhibitory and antidiabetic activities. PDPS was the richest source of proteins, carbohydrates, minerals, and saturated fatty acids, while date palm pollen crushed with its spadix was a better source of unsaturated fatty acids, which are mainly represented by linoleic acid. In conclusion, although date palm pollen crushed with its spadix is the most widely consumed type, its nutritional value is lower than that of pure date palm pollen. Thus, pure date palm pollen could potentially serve as a better source of many bioactive compounds, making it a viable supplement for various health applications.
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges

Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and the exchanged texts with the edges. To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory. To address both issues, we introduce Deep-LPTM, a model-based clustering strategy relying on a variational graph auto-encoder approach as well as a probabilistic model to characterise the topics of discussion. Deep-LPTM allows to build a joint representation of the nodes and of the edges in two embeddings spaces. The parameters are inferred using a variational inference algorithm. We also introduce IC2L, a model selection criterion specifically designed to choose models with relevant clustering and visualisation properties. An extensive benchmark study on synthetic data is provided. In particular, we find that Deep-LPTM better recovers the partitions of the nodes than the state-of-the art ETSBM and STBM. Eventually, the emails of the Enron company are analysed and visualisations of the results are presented, with meaningful highlights of the graph structure.
A ZX-Calculus Approach for the Construction of Graph Codes

Quantum Error-Correcting Codes (QECCs) play a crucial role in enhancing the robustness of quantum computing and communication systems against errors. Within the realm of QECCs, stabilizer codes, and specifically graph codes, stand out for their distinct attributes and promising utility in quantum technologies. This study underscores the significance of devising expansive QECCs and adopts the ZX-calculus a graphical language adept at quantum computational reasoning-to depict the encoders of graph codes effectively. Through the integration of ZX-calculus with established encoder frameworks, we present a nuanced approach that leverages this graphical representation to facilitate the construction of large-scale QECCs. Our methodology is rigorously applied to examine the intricacies of concatenated graph codes and the development of holographic codes, thus demonstrating the practicality of our graphical approach in addressing complex quantum error correction challenges. This research contributes to the theoretical understanding of quantum error correction and offers practical tools for its application, providing objective advancements in the field of quantum computing.
Effective implementation of the High Performance Conjugate Gradient benchmark on GraphBLAS

Applications in High-Performance Computing (HPC) environments face challenges due to increasing complexity. Among them, the increasing usage of sparse data pushes the limits of data structures and programming models and hampers the efficient usage of existing, highly parallel hardware. The GraphBLAS specification tackles these challenges by proposing a set of data containers and primitives, coupled with a semantics based on abstract algebraic concepts: this allows multiple applications on sparse data to be described with a small set of primitives and benefit from the many optimizations of a compile-time-known algebraic specification. Among HPC applications, the High Performance Conjugate Gradient (HPCG) benchmark is an important representative of a large body of sparse workloads, and its structure poses several programmability and performance challenges. This work tackles them by proposing and evaluating an implementation on GraphBLAS of HPCG, highlighting the main changes to its kernels. The results for shared-memory systems outperforms the reference, while results in distributed systems highlight fundamental limitations of GraphBLAS-compliant implementations, which suggests several future directions.
Multicast Scheduling over Multiple Channels: A Distribution-Embedding Deep Reinforcement Learning Method

Multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). Multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module: The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO; and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones and achieves comparable performance to the derived benchmark.
Melanoma stem cell vaccine induces effective tumor immunity against melanoma

ABSTRACT Melanoma stem cells (MSCs)-based vaccine strategies have been a potent immunotherapeutic approach for melanoma treatment, which aimed at inducing specific anti-tumor immunity and targeting cancer stem-like cells. As the main cancer-fighting immune cells, CD8+T cells play an important role in vaccine-induced antitumor immunity. Here, we developed a novel MSC vaccine that induces CD8+T cells to target melanoma stem cells specifically. The MSC vaccine was prepared for our study in order to determine the effectiveness of antitumor immunity. The proportion and activity of CD8+T cells were examined in the spleen after immunization, in particular, the expression and cytotoxicity of the immune checkpoint of spleen lymphocytes were detected by flow cytometry and ELISA, moreover, tumor size and the number of lung metastasis nodules were observed and the specific killing effect of the vaccine was evaluated in immunized mice. We found that the MSC vaccine could promote DCs maturation, activate CD8+T cells, suppress the expression of CTLA-4, PD-1, and Tim-3, and increase the expression of IFN-γ and GzmB of CD8+T cells. Melanoma growth and metastasis were inhibited by the vaccine’s specific targeted killing effect. The vaccines based on melanoma stem cells (MSCs) delay the progression of melanoma by inducing anti-tumor immune responses in CD8+T cells.
Comparison of TRMM Precipitation Radar and Airborne Radar Data

Abstract The first spaceborne weather radar is the precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM), which was launched in 1997. As part of the TRMM calibration and validation effort, an airborne rain-mapping radar (ARMAR) was used to make underflights of TRMM during the B portion of the Texas and Florida Underflights (TEFLUN-B) and the third Convection and Moisture Experiment (CAMEX-3) in 1998 and the Kwajalein Experiment (KWAJEX) in 1999. The TRMM PR and ARMAR both operate at 14 GHz, and both instruments use a downward-looking, cross-track scanning geometry, which allows direct comparison of data. Nearly simultaneous PR and ARMAR data were acquired in seven separate cases. These data are compared to examine the effects of larger resolution volume and lower sensitivity in the PR data relative to ARMAR. The PR and ARMAR data show similar structures, although the PR data tend to have lower maximum reflectivities and path attenuations because of nonuniform beam-filling effects. Non.
Patient Adaptable ECG Beat Classifier Using LDC and EMC Algorithms

Electrocardiogram (ECG) waveforms are used to find both regular and irregular patterns in cardiac cycles of patients. In this work, the beats like normal, supraventricular and ventricular beats are identified and classified to the corresponding classes based on combining the decisions of both linear discriminant classifier (LDC) algorithm and Clustering algorithm. Classifications of beats are done by considering both R-R interval features and morphological descriptor features. First the Linear Discriminant Classifier is trained for the three different class and the training beats are taken from three different databases namely, MIT-BIH Arrhythmia, MIT-BIH Supraventricular and MIT-BIH ST Change. Expectation Maximization clustering algorithm (EM) forms patient specific clusters which is based on MOG (mixture of expert) model. Finally the beats in the cluster output is labeled & verified to corresponding classes with the aid of linear discriminant analysis function.
PPM1D activity promotes the replication stress caused by cyclin E1 overexpression

Oncogene‐induced replication stress has been recognized as a major cause of genome instability in cancer cells. Increased expression of cyclin E1 caused by amplification of the CCNE1 gene is a common cause of replication stress in various cancers. Protein phosphatase magnesium‐dependent 1 delta (PPM1D) is a negative regulator of p53 and has been implicated in termination of the cell cycle checkpoint. Amplification of the PPM1D gene or frameshift mutations in its final exon promote tumorigenesis. Here, we show that PPM1D activity further increases the replication stress caused by overexpression of cyclin E1. In particular, we demonstrate that cells expressing a truncated mutant of PPM1D progress faster from G1 to S phase and fail to complete licensing of the replication origins. In addition, we show that transcription–replication collisions and replication fork slowing caused by CCNE1 overexpression are exaggerated in cells expressing the truncated PPM1D. Finally, replication speed and accumulation of focal DNA copy number alterations caused by induction of CCNE1 expression was rescued by pharmacological inhibition of PPM1D. We propose that increased activity of PPM1D suppresses the checkpoint function of p53 and thus promotes genome instability in cells expressing the CCNE1 oncogene.
Cost-effective 3D one-pass depth migration

A crucial point in the processing of 3D seismic data is the migration step, both because of its 3D nature and the computational cost involved. The efficiency and accuracy of 3D migration are determined by the wavefield extrapolation technique employed. Wavefield extrapolation based on second-order differential operators of variable-length is very efficient and accurate at the same time. Compared to migration based on the McClellan transform and operator splitting, the use of variable-length second-order differential operators offers significant advantages. The 3D migration operator has an almost perfect circular symmetry. No positioning errors in the 45° azimuth between the in-line and cross-line directions are evident
Remarks on global attractors for the 3D Navier--Stokes equations with horizontal filtering

We consider a Large Eddy Simulation model for a homogeneous incompressible Newtonian fluid in a box space domain with periodic boundary conditions on the lateral boundaries and homogeneous Dirichlet conditions on the top and bottom boundaries, thus simulating a horizontal channel. The model is obtained through the application of an anisotropic horizontal filter, which is known to be less memory consuming from a numerical point of view, but provides less regularity with respect to the standard isotropic one defined as the inverse of the Helmholtz operator. It is known that there exists a unique regular weak solution to this model that depends weakly continuously on the initial datum. We show the existence of the global attractor for the semiflow given by the time-shift in the space of paths. We prove the continuity of the horizontal components of the flow under periodicity in all directions and discuss the possibility to introduce a solution semiflow.
ROP16-mediated activation of STAT6 enhances cyst development of type III Toxoplasma gondii in neurons

Toxoplasma gondii establishes a long-lived latent infection in the central nervous system (CNS) of its hosts. Reactivation in immunocompromised individuals can lead to life threatening disease. Latent infection is driven by the ability of the parasite to convert from the acute-stage tachyzoite to the latent-stage bradyzoite which resides in long-lived intracellular cysts. While much work has focused on the parasitic factors that drive cyst development, the host factors that influence encystment are not well defined. Here we show that a polymorphic secreted parasite kinase (ROP16), that phosphorylates host cell proteins, mediates efficient encystment of T. gondii in a stress-induced model of encystment and primary neuronal cell cultures (PNCs) in a strain-specific manner. Using short-hairpin RNA (shRNA) knockdowns in human foreskin fibroblasts (HFFs) and PNCs from transgenic mice, we determined that ROP16’s cyst enhancing abilities are mediated, in part, by phosphorylation—and therefore activation—of the host cell transcription factor STAT6. To test the role of STAT6 in vivo, we infected wild-type (WT) and STAT6KO mice, finding that, compared to WT mice, STAT6KO mice have a decrease in CNS cyst burden but not overall parasite burden or dissemination to the CNS. Finally, we found a similar ROP16-dependent encystment defect in human pluripotent stem cell-derived neurons. Together, these findings identify a host cell factor (STAT6) that T. gondii manipulates in a strain-specific manner to generate a favorable encystment environment.
Analysis of the 2007-2008 Writer’s Guild Strike with Game Theory

In 2007-2008, the writer’s guild of America went on strike in order to receive a better outcome from management. We built a game to analyze the situation. The Nash equilibrium of that game says that the writers should not strike and that management should maintain the status quo. The equilibrium is quite unattractive to the writers leading to a strike and forcing management to negotiate. We illustrate the results in order to gain insights into the process. We demonstrate finding the Nash equilibrium with both ordinal and then cardinal values. We demonstrate a method to find the cardinal values using the analytical hierarchy processes to measure the utility for the strategies. We show finding the prudential strategies and security levels as well as finding threat levels in this example. We show using the threat level in Nash arbitration leads to a better solution for the writers than using the security levels.
Estimated daily intake and health risk assessment of toxic elements in infant formulas

Abstract In this study, the heavy metal (Al, Mn, Co, Cu, Zn, As, Se, Cd, Sn, Pb and Hg) concentrations were determined in a total of seventy-two infant formula samples manufactured by sixteen different brands in Türkiye. During the analyses, inductively coupled plasma MS was used in evaluating the nutritional profile and the toxicological risk associated with the consumption of these products. Given the analysis results, the highest Pb content was found in milk-based ‘beginner’ formulas (0–6 months, three samples) packed in metal containers. The highest concentration of Mn was found in powdered infant formula (Brand 3) that is suitable for 9–12-month-olds. Mn level was found to be above the limit values in nine samples (12·5 %). Cd level exceeded the limit values in two infant formula samples of Brand 3 (0·038 µg/g) and Brand 15 (0·023 µg/g). Therefore, the mean Cd concentration found here reaches the maximum limit set by the European Union commission legislation. Cu was detected in all infant formulas. The highest concentration was determined in Brand 1 (9–12 months, seven samples) and found to be 2·637 (sd 1·928) µg/g. This value is much higher than the reference values set in the national and international standards. Based on the results achieved here, the estimated daily intake (EDI) and target hazard quotient values for all the metals in infant formulas were found lower than < 1. These findings suggest that the baby foods examined would not pose any health risk. The daily intakes exceeding the baby nutrition values recommended by the WHO would pose health risk since they would exceed the EDI levels.