Miracle-dz/UniFAR-FTUs huggingface dataset:
CRC Codes as Error Correction Codes

CRC codes have long since been adopted in a vast range of applications. The established notion that they are suitable primarily for error detection can be set aside through use of the recently proposed Guessing Random Additive Noise Decoding (GRAND). Hard-detection (GRAND-SOS) and soft-detection (ORBGRAND) variants can decode any short, high-rate block code, making them suitable for error correction of CRC-coded data. When decoded with GRAND, short CRC codes have error correction capability that is at least as good as popular codes such as BCH codes, but with no restriction on either code length or rate.The state-of-the-art CA-Polar codes are concatenated CRC and Polar codes. For error correction, we find that the CRC is a better short code than either Polar or CA-Polar codes. Moreover, the standard CA-SCL decoder only uses the CRC for error detection and therefore suffers severe performance degradation in short, high rate settings when compared with the performance GRAND provides, which uses all of the CA-Polar bits for error correction.Using GRAND, existing systems can be upgraded from error detection to low-latency error correction without re-engineering the encoder, and additional applications of CRCs can be found in IoT, Ultra-Reliable Low Latency Communication (URLLC), and beyond. The universality of GRAND, its ready parallelized implementation in hardware, and the good performance of CRC as codes make their combination a viable solution for low-latency applications.
Joint AP Association and PCS Threshold Selection in Dense Full-duplex Wireless Networks

Joint access point (AP) association and physical carrier sensing (PCS) threshold selection has the potential to improve the performance in high density wireless LANs (WLANs) under high contention, interference and self-interference (SI) limited transmissions. Using tools from stochastic geometry, user and AP locations are independent realizations of spatial point processes. Considering the inherent effects of the channel access protocol, the spatial density of throughput (SDT), which depends on channel access probability and coverage rate, is derived as the performance objective. Leveraging spatial statistics of the network, a throughput-utility maximization problem is formulated to seek AP association and PCS threshold selection policies that jointly maximize SDT. The AP association and the PCS threshold selection policies are derived analytically while an algorithm is proposed for numerical solution. Under simulated scenarios involving full-duplex (FD) nodes, optimizing AP association yields performance gains for low to high node density in large-scale wireless networks. Considering PCS threshold selection optimization jointly with AP association is shown to improve performance by effectively separating concurrent transmissions in space. It is shown that AP association in FD WLANs groups users into minimal contention domains and PCS threshold optimization reduces the interference domain of user groups for additional performance gains.
Influence of Must Processing on Iron and Copper Contents of Experimental Wines

Grape musts of four varieties were dejuiced and the drained musts pressed to yield a series of juices for each grape lot. Both original and clarified juices were fermented into wines and the latter analyzed for iron and copper. Juices high (10-38%) in suspended solids yielded wines that were generally high (10-20 ppm) in iron and low (0.1-0.2 ppm) in copper. Wines from juices lower in suspended solids and from centrifuged juices were lower (2-5 ppm) in iron and higher (0.2-0.7 ppm) in copper. The load-to-load variability in grapes caused large differences in the wine analyses. If juices were clarified promptly, the method of dejuicing and the severity of pomace-pressing had little influence on the iron and copper contents of the wines. During fermentation, iron passed from the suspended solids into the wine. The suspended solids are believed to constitute a source of sulfur that yeasts reduce to sulfide, which, in turn, precipitates copper as copper sulfide. The removal of suspended solids from juice thus contributes to improved wine quality in several ways.
Path Analysis for Binary Random Variables

The decomposition of the overall effect of a treatment into direct and indirect effects is here investigated with reference to a recursive system of binary random variables. We show how, for the single mediator context, the marginal effect measured on the log odds scale can be written as the sum of the indirect and direct effects plus a residual term that vanishes under some specific conditions. We then extend our definitions to situations involving multiple mediators and address research questions concerning the decomposition of the total effect when some mediators on the pathway from the treatment to the outcome are marginalized over. Connections to the counterfactual definitions of the effects are also made. Data coming from an encouragement design on students’ attitude to visit museums in Florence, Italy, are reanalyzed. The estimates of the defined quantities are reported together with their standard errors to compute p values and form confidence intervals.
Surface multigrid via intrinsic prolongation

This paper introduces a novel geometric multigrid solver for unstructured curved surfaces. Multigrid methods are highly efficient iterative methods for solving systems of linear equations. Despite the success in solving problems defined on structured domains, generalizing multigrid to unstructured curved domains remains a challenging problem. The critical missing ingredient is a prolongation operator to transfer functions across different multigrid levels. We propose a novel method for computing the prolongation for triangulated surfaces based on intrinsic geometry, enabling an efficient geometric multigrid solver for curved surfaces. Our surface multigrid solver achieves better convergence than existing multigrid methods. Compared to direct solvers, our solver is orders of magnitude faster. We evaluate our method on many geometry processing applications and a wide variety of complex shapes with and without boundaries. By simply replacing the direct solver, we upgrade existing algorithms to interactive frame rates, and shift the computational bottleneck away from solving linear systems.
Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data

The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available. In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our experiments with real-world datasets across several scientific domains, including the official bioinformatics Question Answering over Linked Data (QALD) challenge, as well as the CORDIS dataset of European projects, show that Bio-SODA outperforms publicly available KGQA systems by an F1-score of least 20% and by an even higher factor on more complex bioinformatics datasets.
Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes

As an emerging technology that has attracted huge attention, non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface, with broad application prospects in the fields of autonomous driving, medical imaging, and defense. Despite the challenges of low signal-to-noise ratio (SNR) and high ill-posedness, NLOS imaging has been developed rapidly in recent years. Most current NLOS imaging technologies use conventional physical models, constructing imaging models through active or passive illumination and using reconstruction algorithms to restore hidden scenes. Moreover, deep learning algorithms for NLOS imaging have also received much attention recently. This paper presents a comprehensive overview of both conventional and deep learning-based NLOS imaging techniques. Besides, we also survey new proposed NLOS scenes, and discuss the challenges and prospects of existing technologies. Such a survey can help readers have an overview of different types of NLOS imaging, thus expediting the development of seeing around corners.
Optimal Transport between Gaussian random fields

We consider the optimal transport problem between zero mean Gaussian stationary random fields both in the aperiodic and periodic case. We show that the solution corresponds to a weighted Hellinger distance between the multivariate and multidimensional power spectral densities of the random fields. Then, we show that such a distance defines a geodesic, which depends on the weight function, on the manifold of the multivariate and multidimensional power spectral densities.
Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies

We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate this automatic cell tracking algorithm using recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps. On a batch of 1100 image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the task of generating discharge summaries for a clinical encounter. Summaries in this setting need to be faithful, traceable, and scale to multiple long documents, motivating the use of extract-then-abstract summarization cascades. We introduce two new measures, faithfulness and hallucination rate for evaluation in this task, which complement existing measures for fluency and informativeness. Results across seven medical sections and five models show that a summarization architecture that supports traceability yields promising results, and that a sentence-rewriting approach performs consistently on the measure used for faithfulness (faithfulness-adjusted $F_3$) over a diverse range of generated sections.
PyTorch Tabular: A Framework for Deep Learning with Tabular Data

In spite of showing unreasonable effectiveness in modalities like Text and Image, Deep Learning has always lagged Gradient Boosting in tabular data - both in popularity and performance. But recently there have been newer models created specifically for tabular data, which is pushing the performance bar. But popularity is still a challenge because there is no easy, ready-to-use library like Sci-Kit Learn for deep learning. PyTorch Tabular is a new deep learning library which makes working with Deep Learning and tabular data easy and fast. It is a library built on top of PyTorch and PyTorch Lightning and works on pandas dataframes directly. Many SOTA models like NODE and TabNet are already integrated and implemented in the library with a unified API. PyTorch Tabular is designed to be easily extensible for researchers, simple for practitioners, and robust in industrial deployments.
A unified convergence analysis for the fractional diffusion equation driven by fractional Gaussion noise with Hurst index H∈(0, 1)

Here, we provide a unified framework for numerical analysis of stochastic nonlinear fractional diffusion equation driven by fractional Gaussian noise with Hurst index $H\in(0,1)$. A novel estimate of the second moment of the stochastic integral with respect to fractional Brownian motion is constructed, which greatly contributes to the regularity analyses of the solution in time and space for $H\in(0,1)$. Then we use spectral Galerkin method and backward Euler convolution quadrature to discretize the fractional Laplacian and Riemann-Liouville fractional derivative, respectively. The sharp error estimates of the built numerical scheme are also obtained. Finally, the extensive numerical experiments verify the theoretical results.
Impact of Nisin and Nisin-Producing Lactococcus lactis ssp. lactis on Clostridium tyrobutyricum and Bacterial Ecosystem of Cheese Matrices

Clostridium tyrobutyricum spores survive milk pasteurization and cause late blowing of cheeses and significant economic loss. The effectiveness of nisin-producing Lactococcus lactis ssp. lactis 32 as a protective strain for control the C. tyrobutyricum growth in Cheddar cheese slurry was compared to that of encapsulated nisin-A. The encapsulated nisin was more effective, with 1.0 log10 reductions of viable spores after one week at 30 °C and 4 °C. Spores were not detected for three weeks at 4 °C in cheese slurry made with 1.3% salt, or during week 2 with 2% salt. Gas production was observed after one week at 30 °C only in the control slurry made with 1.3% salt. In slurry made with the protective strain, the reduction in C. tyrobutyricum count was 0.6 log10 in the second week at 4 °C with both salt concentration. At 4 °C, nisin production started in week 2 and reached 97 µg/g after four weeks. Metabarcoding analysis targeting the sequencing of 16S rRNA revealed that the genus Lactococcus dominated for four weeks at 4 °C. In cheese slurry made with 2% salt, the relative abundance of the genus Clostridium decreased significantly in the presence of nisin or the protective strain. The results indicated that both strategies are able to control the growth of Clostridium development in Cheddar cheese slurries.
Management and outcome of hypertrophic obstructive cardiomyopathy in pregnant women: a case report

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiomyopathy, with an estimated prevalence of 1 in 500 people. Despite overall favorable outcomes with modern treatment and early diagnosis of disease, adverse complications could occur during times of physiological stress like pregnancy. Complications of HCM include sudden cardiac death, heart failure, and arrhythmia. We report the case of a 32-year-old pregnant woman with obstructive HCM, presenting with recurrent episodes of ventricular arrhythmia despite medical therapy. This case exhibits how close monitoring and proper management during pregnancy according to the latest recommendations, resulted in a successful and uneventful delivery.
Celecoxib Decrease Seizures Susceptibility in a Rat Model of Inflammation by Inhibiting HMGB1 Translocation

The risk of developing epilepsy is strongly linked to peripheral inflammatory disorders in humans. High-mobility group box protein 1 (HMGB1) has the most focus for being a suspect in this scenario. The current study aimed to detect the celecoxib effect, an anti-inflammatory drug, on decreasing seizure susceptibility and organ damage in lipopolysaccharides (LPS)/pilocarpine (PILO) pretreated Wistar rats. Rats were divided into 6 groups (8 each): group 1 (control), group 2 (PILO), group 3 (PILO+LPS), group 4 (PILO+LPS+(VPA) Valproic acid), group 5 (PILO+LPS+Celecoxib), and group 6 (PILO+LPS+VPA+Celecoxib). LPS was used to induce sepsis and PILO to induce seizures. Oxidative stress markers, pro-inflammatory cytokines, and HMGB1 levels in serum and brain homogenate were evaluated. Histopathological studies were conducted on the hippocampus, liver, lung, and kidney. Treatment with celecoxib either alone or in combination with VPA significantly reduced Racine score and delays latency to generalized tonic-clonic seizures onset with a significant decrease in hippocampal levels of pro-inflammatory cytokines, oxidative stress markers, and increase in reduced glutathione. In addition, celecoxib treatment either alone or in combination with VPA suppressed HMGB1translocation into peripheral circulation more than treatment with VPA alone. Furthermore, hippocampus, liver, lung, and kidney histopathological changes were improved in contrast to other epileptic groups. Celecoxib either alone or combined with VPA has antiepileptic and multiorgan protective effects on acute seizures and inflammatory models induced by PILO with LPS. It decreased histopathological findings, oxidative, and inflammatory effects induced by VPA and LPS. This might be due to its anti-oxidative, anti-inflammatory and anti-HMGB1 mediated effects.
The Sleep and Recovery Practices of Athletes

Background: Athletes maintain a balance between stress and recovery and adopt recovery modalities that manage fatigue and enhance recovery and performance. Optimal TST is subject to individual variance. However, 7–9 h sleep is recommended for adults, while elite athletes may require more quality sleep than non-athletes. Methods: A total of 338 (elite n = 115, 74 males and 41 females, aged 23.44 ± 4.91 years; and sub-elite n = 223, 129 males and 94 females aged 25.71 ± 6.27) athletes were recruited from a variety of team and individual sports to complete a battery of previously validated and reliable widely used questionnaires assessing sleep, recovery and nutritional practices. Results: Poor sleep was reported by both the elite and sub-elite athlete groups (i.e., global PSQI score ≥5—elite 64% [n = 74]; sub-elite 65% [n = 146]) and there was a significant difference in sport-specific recovery practices (3.22 ± 0.90 vs. 2.91 ± 0.90; p < 0.001). Relatively high levels of fatigue (2.52 ± 1.32), stress (1.7 ± 1.31) and pain (50%, n = 169) were reported in both groups. A range of supplements were used regularly by athletes in both groups; indeed, whey (elite n = 22 and sub-elite n = 48) was the most commonly used recovery supplement in both groups. Higher alcohol consumption was observed in the sub-elite athletes (12%, n = 26) and they tended to consume more units of alcohol per drinking bout. Conclusion: There is a need for athletes to receive individualised support and education regarding their sleep and recovery practices.
Preparation and Characterization of Nanofibrous Scaffolds of Ag/Vanadate Hydroxyapatite Encapsulated into Polycaprolactone: Morphology, Mechanical, and In Vitro Cells Adhesion

Series of nanofibrous composites of polycaprolactone (PCL) were fabricated in different compositions of modified hydroxyapatite (HAP). The encapsulated HAP was co-doped with Ag/vanadate ions at different Ag contributions. XRD and FTIR techniques confirmed the powder and fibrous phase formation. Further, the morphological and mechanical behaviors of the electrospun nanofibrous scaffolds containing hydroxyapatite were investigated. The nanofibrous phases were biologically evaluated via studying contact angle, antibacterial, cell viability, and in vitro growth of human fibroblasts cell line (HFB4). It is obvious that silver ions cause gradual deviation in powder grains from wafer-like to cloudy grains. The maximum height of the roughness (Rt) ranged from 902.0 to 956.9 nm, while the valley depth of the roughness (Rv) ranged from 308.3 to 442.8 nm, for the lowest and the highest additional Ag ions for powdered phases. Moreover, the highest contribution of silver through the nanofibrous phases leads to the formation of lowest filaments size ranged from 0.07 to 0.53 µm. Further, the fracture strength was increased exponentially from 2.51 ± 0.35 MPa at zero concentration of silver ions up to 4.23 ± 0.64 MPa at 0.6 Ag/V-HAP@PCL. The fibrous phases were biologically evaluated in terms of antibacterial, cell viability, and in vitro growth of human fibroblasts cell line (HFB4). The nanofibrous composition of 0.8 Ag/V-HAP@PCL reached the maximum potential against E. coli and S. aureus and recorded 20.3 ± 1.1 and 19.8 ± 1.2 mm, respectively. This significant performance of the antibacterial activity and cell viability of co-doped HAP distributed through PCL could recommend these compositions for more research in biological applications, including wound healing.
The Multifaceted Roles of Ku70/80

DNA double-strand breaks (DSBs) are accidental lesions generated by various endogenous or exogenous stresses. DSBs are also genetically programmed events during the V(D)J recombination process, meiosis, or other genome rearrangements, and they are intentionally generated to kill cancer during chemo- and radiotherapy. Most DSBs are processed in mammalian cells by the classical nonhomologous end-joining (c-NHEJ) pathway. Understanding the molecular basis of c-NHEJ has major outcomes in several fields, including radiobiology, cancer therapy, immune disease, and genome editing. The heterodimer Ku70/80 (Ku) is a central actor of the c-NHEJ as it rapidly recognizes broken DNA ends in the cell and protects them from nuclease activity. It subsequently recruits many c-NHEJ effectors, including nucleases, polymerases, and the DNA ligase 4 complex. Beyond its DNA repair function, Ku is also involved in several other DNA metabolism processes. Here, we review the structural and functional data on the DNA and RNA recognition properties of Ku implicated in DNA repair and in telomeres maintenance.
Adenosine A1 Receptor Agonist (R-PIA) before Pilocarpine Modulates Pro- and Anti-Apoptotic Factors in an Animal Model of Epilepsy

We aimed to characterize the mechanisms involved in neuroprotection by R-PIA administered before pilocarpine-induced seizures. Caspase-1 and caspase-3 activities were assayed using fluorimetry, and cathepsin D, HSP-70, and AKT expression levels were assayed using Western Blot of hippocampal samples. R-PIA was injected before pilocarpine (PILO), and four groups were studied at 1 h 30 min and 7 days following initiation of status epilepticus (SE): PILO, R-PIA+PILO, SALINE, and R-PIA+SALINE. At 1 h 30 min, significantly higher activities of caspase-1 and -3 were observed in the PILO group than in the SALINE group. Caspase-1 and -3 activities were higher in the R-PIA+PILO group than in the PILO group. At 7 days following SE, caspase-1 and -3 activities were higher than in the initial post-seizure phase compared to the SALINE group. The pretreatment of rats receiving PILO significantly reduced caspase activities compared to the PILO group. Expression of HSP-70, AKT, and cathepsin D was significantly higher in the PILO group than in the SALINE. In the R-PIA+PILO group, the expression of AKT and HSP-70 was greater than in rats receiving only PILO, while cathepsin D presented decreased expression. Pretreatment with R-PIA in PILO-injected rats strongly inhibited caspase-1 and caspase-3 activities and cathepsin D expression. It also increased expression levels of the neuroprotective proteins HSP-70 and AKT, suggesting an important role in modulating the cellular survival cascade.