Miracle-dz/UniFAR-FTUs huggingface dataset:
Huitlacoche (Ustilago maydis), an Iconic Mexican Fungal Resource: Biocultural Importance, Nutritional Content, Bioactive Compounds, and Potential Biotechnological Applications

Worldwide, the fungus known as huitlacoche (Ustilago maydis (DC.) Corda) is a phytopathogen of maize plants that causes important economic losses in different countries. Conversely, it is an iconic edible fungus of Mexican culture and cuisine, and it has high commercial value in the domestic market, though recently there has been a growing interest in the international market. Huitlacoche is an excellent source of nutritional compounds such as protein, dietary fiber, fatty acids, minerals, and vitamins. It is also an important source of bioactive compounds with health-enhancing properties. Furthermore, scientific evidence shows that extracts or compounds isolated from huitlacoche have antioxidant, antimicrobial, anti-inflammatory, antimutagenic, antiplatelet, and dopaminergic properties. Additionally, the technological uses of huitlacoche include stabilizing and capping agents for inorganic nanoparticle synthesis, removing heavy metals from aqueous media, having biocontrol properties for wine production, and containing biosurfactant compounds and enzymes with potential industrial applications. Furthermore, huitlacoche has been used as a functional ingredient to develop foods with potential health-promoting benefits. The present review focuses on the biocultural importance, nutritional content, and phytochemical profile of huitlacoche and its related biological properties as a strategy to contribute to global food security through food diversification; moreover, the biotechnological uses of huitlacoche are also discussed with the aim of contributing to the use, propagation, and conservation of this valuable but overlooked fungal resource.
Detecting Double-Identity Fingerprint Attacks

Double-identity biometrics, that is the combination of two subjects’ features into a single template, was demonstrated to be a serious threat against existing biometric systems. In fact, well-synthetized samples can fool state-of-the-art biometric verification systems, leading them to falsely accept both the contributing subjects. This work proposes one of the first techniques to defy existing double-identity fingerprint attacks. The proposed approach inspects the regions where the two aligned fingerprints overlap but minutiae cannot be consistently paired. If the quality of these regions is good enough to minimize the risk of false or miss minutiae detection, then the alarm score is increased. Experimental results carried out on two fingerprint databases, with two different techniques to generate double-identity fingerprints, validate the effectiveness of the proposed approach.
Effectiveness of Lifestyle Nutrition and Physical Activity Interventions for Childhood Obesity and Associated Comorbidities among Children from Minority Ethnic Groups: A Systematic Review and Meta-Analysis

Lifestyle physical activity (PA) and nutrition are known to be effective interventions in preventing and managing obesity-related comorbidities among adult populations but less so among children and adolescents. We examined the effectiveness of lifestyle interventions in children from minority ethnic populations in Western high-income countries (HICs). Our systematic review included 53 studies, involving 26,045 children from minority ethnic populations who followed lifestyle intervention programmes lasting between 8 weeks and 5 years with the aim of preventing and/or managing childhood obesity and associated comorbidities, including adiposity and cardiometabolic risks. The studies were heterogenous in terms of lifestyle intervention components (nutrition, PA, behavioural counselling) and settings (community vs. schools and after-school settings). Our meta-analysis included 31 eligible studies and showed no significant effects of lifestyle interventions when they focused on body mass index (BMI) outcomes (pooled BMI mean change = −0.09 (95% CI = −0.19, 0.01); p = 0.09). This was irrespective of the intervention programme duration (<6 months vs. ≥6 months), type (PA vs. nutrition/combined intervention) and weight status (overweight or obese vs. normal weight) as all showed nonsignificant effects in the sensitivity analysis. Nonetheless, 19 of the 53 studies reported reductions in BMI, BMI z-score and body fat percentage. However, the majority of lifestyle interventions adopting a quasi-design with combined primary and secondary obesity measures (11 out of 15 studies) were effective in reducing the obesity comorbidities of cardiometabolic risks, including metabolic syndrome, insulin sensitivity and blood pressure, in overweight and obese children. Preventing childhood obesity in high-risk ethnic minority groups is best achieved using combined PA and nutrition intervention approaches, which jointly target preventing obesity and its comorbidities, especially the outcomes of diabetes, hypertension and cardiovascular disease. Therefore, public health stakeholders should integrate cultural and lifestyle factors and contextualise obesity prevention strategies among minority ethnic groups in Western HICs.
Research based on euclid distance with weights of clustering method

Euclid distance is commonly used to measure distance in clustering analysis algorithm.The clustering analysis based on weighted Euclid distance is researched and presented to overcome the existing problems of similarity calculation in clustering analysis based on traditional Euclid distance when we have no any domain knowledge about the data objects.The experiment shows that the method has not only to certain extent overcome limitation of Euclid distance,but also been improving the clustering quality and optimizing performance.
Local flow and loads estimation on wake-affected wind turbines using graph neural networks and PyWake

The estimation of the flow conditions throughout a modern wind farm is a relevant albeit complex problem involving several different modelling scales. In this context, recent years have seen the rise of the demand - normally industry-driven - of quick, computationally inexpensive, wind farm flow field simulators upon which further calculations might be undertaken (e.g. power output, fatigue loads, etc.). Naturally, the trustworthiness of such models hinges upon their ability to accurately estimate wakes, under the evident trade-off between computational time and accuracy. One such simulator that has been recently gaining traction within the wind community is PyWake, a Python-based flow solver, with several possible add-ons and modularity options. Based on runs from PyWake, this contribution studies the applicability of graph neural networks (GNN) as a simultaneous wind turbine fleet, wind inflow and loads surrogate. As graphs are discrete mathematical sets of dependent objects, one can see how the layout-dependent interaction between wind turbines’ aerodynamics, from which wake arises, lends itself to be modelled as a graph. Thus, we introduce the use of GNNs for layout-agnostic joint modelling of rotor-averaged wind speed, turbulence intensity, power production and damage equivalent loads (DEL) on individual turbines of wind farms. To this end, probabilistic samples of inflow conditions from a Weibull distribution for wind speed, uniform wind direction and conditional normal distributions of wind shear and nacelle yaw angles are generated as main inflow properties in the numerical simulations. Additionally, arbitrary wind farm layouts are created based on varied geometric shapes with random parametrization (varying orientations, length/width ratio). Both the arbitrary layouts and the random inflow conditions are then used as inputs for PyWake. PyWake’s output, namely, rotor averaged wind speed, turbulence intensity, power production and DELs of each individual wind turbine in the wind farms, are used to train a general GNN model. We elect to implement an Encode-Process-Decode GNN as a graph learning model. We compare the performance of various approaches of the relational dependency representation (edge-forming techniques) amongst the nodes (wind turbines) on the graph, such as Delaunay triangulation, KNN, radius-based methods and fully connected scheme. In our analysis, we evaluate the accuracy of GNNs and its ability to generalize their joint predictions for unseen layouts and varying inflow conditions.
A non-symmetric Gaussian wake model for lateral wake-to-wake interactions

In this paper, we propose a new non-symmetric Gaussian wake model, which allows for different lateral expansions on the two sides of a wake to account for its interaction with neighbouring wakes. The proposed model is formulated following classical speed-deficit assumptions and momentum conservation. Departing from the existing literature, a non-symmetric Gaussian function is used to represent the velocity deficit in the wake. Accordingly, different wake expansions are assumed on the two sides of the wake, each expressed as a function of the locally prevailing turbulence intensity. The model considers that wake-added turbulence changes with downstream distance; hence, the turbulence intensity on a wake-immersed side of the wake is location dependent. The new model is compared to LES-ALM numerical simulations of three turbines in partial wake overlap. The free parameters of the model describing the wake development are tuned based on the CFD results. Results indicate that the new model provides for a very good agreement of the velocity profiles at different downstream positions, generating an improved representation of merging wakes and their downstream development.
Adaptive and Efficient Data Transmission Method Based on New Satellite Network

For the satellite to ground data transmission in the space-based satellite network, the worst channel conditions shall be considered, that is, the link budget shall be carried out according to the lowest elevation angle and the farthest transmission distance. It is extremely urgent to make full use of the limited link power resources. An efficient adaptive data transmission method is proposed to make the satellite data transmission rate increase with the increase of elevation angle, so as to maximize the amount of data transmitted from the satellite transit window, the reliability and throughput of information transmission are improved. And the effectiveness of this method is verified by computer simulation.
Alterations of ribosomal RNA pseudouridylation in human breast cancer

Abstract RNA modifications are key regulatory factors for several biological and pathological processes. They are abundantly represented on ribosomal RNA (rRNA), where they contribute to regulate ribosomal function in mRNA translation. Altered RNA modification pathways have been linked to tumorigenesis as well as to other human diseases. In this study we quantitatively evaluated the site-specific pseudouridylation pattern in rRNA in breast cancer samples exploiting the RBS-Seq technique involving RNA bisulfite treatment coupled with a new NGS approach. We found a wide variability among patients at different sites. The most dysregulated positions in tumors turned out to be hypermodified with respect to a reference RNA. As for 2′O-methylation level of rRNA modification, we detected variable and stable pseudouridine sites, with the most stable sites being the most evolutionary conserved. We also observed that pseudouridylation levels at specific sites are related to some clinical and bio-pathological tumor features and they are able to distinguish different patient clusters. This study is the first example of the contribution that newly available high-throughput approaches for site specific pseudouridine detection can provide to the understanding of the intrinsic ribosomal changes occurring in human tumors.
Memory-Centric Computing

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by fundamentally avoiding data movement and reducing data access latency&energy. Many recent studies show that memory-centric computing can greatly improve system performance and energy efficiency. Major industrial vendors and startup companies have also recently introduced memory chips that have sophisticated computation capabilities. This talk describes promising ongoing research and development efforts in memory-centric computing. We classify such efforts into two major fundamental categories: 1) processing using memory, which exploits analog operational properties of memory structures to perform massively-parallel operations in memory, and 2) processing near memory, which integrates processing capability in memory controllers, the logic layer of 3D-stacked memory technologies, or memory chips to enable high-bandwidth and low-latency memory access to near-memory logic. We show both types of architectures (and their combination) can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, databases, machine learning, video processing, climate modeling, genome analysis. We discuss adoption challenges for the memory-centric computing paradigm and conclude with some research&development opportunities.
Neuro-Causal Factor Analysis

Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences. We revisit this classic method from the comparatively new perspective given by advancements in causal discovery and deep learning, introducing a framework for Neuro-Causal Factor Analysis (NCFA). Our approach is fully nonparametric: it identifies factors via latent causal discovery methods and then uses a variational autoencoder (VAE) that is constrained to abide by the Markov factorization of the distribution with respect to the learned graph. We evaluate NCFA on real and synthetic data sets, finding that it performs comparably to standard VAEs on data reconstruction tasks but with the advantages of sparser architecture, lower model complexity, and causal interpretability. Unlike traditional FA methods, our proposed NCFA method allows learning and reasoning about the latent factors underlying observed data from a justifiably causal perspective, even when the relations between factors and measurements are highly nonlinear.
Designing Closed-Loop Models for Task Allocation

Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings in which no oracle exists to assess the quality of human decisions and task assignments made. Instead, we find ourselves in a"closed"decision-making loop in which the same fallible human decisions we rely on in practice must also be used to guide task allocation. How can imperfect and potentially biased human decisions train an accurate allocation model? Our key insight is to exploit weak prior information on human-task similarity to bootstrap model training. We show that the use of such a weak prior can improve task allocation accuracy, even when human decision-makers are fallible and biased. We present both theoretical analysis and empirical evaluation over synthetic data and a social media toxicity detection task. Results demonstrate the efficacy of our approach.
Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator

The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficient hardware accelerators. There is a growing interest in exploring photonics as an alternative technology to digital electronics due to its high energy efficiency and ultra-fast processing speed. Photonic accelerators have shown promising results for CNNs, which mainly rely on weight-static linear operations. However, they encounter issues when efficiently supporting Transformer architectures, questioning the applicability of photonics to advanced ML tasks. The primary hurdle lies in their inefficiency in handling unique workloads in Transformers, i.e., dynamic and full-range tensor multiplication. In this work, we propose Lightening-Transformer, the first light-empowered, high-performance, and energy-efficient photonic Transformer accelerator. To overcome prior designs' fundamental limitations, we introduce a novel dynamically-operated photonic tensor core, DPTC, a crossbar array of interference-based optical vector dot-product engines supporting highly parallel, dynamic, and full-range matrix multiplication. Furthermore, we design a dedicated accelerator that integrates our novel photonic computing cores with photonic interconnects for inter-core data broadcast, fully unleashing the power of optics. Comprehensive evaluations show that ours achieves>2.6x energy and>12x latency reductions compared to prior photonic accelerators and delivers the lowest energy cost and 2 to 3 orders of magnitude lower energy-delay product compared to electronic Transformer accelerators, all while maintaining digital-comparable accuracy. Our work highlights the immense potential of photonics for advanced ML workloads, such as Transformer-backboned LLM. Our work is available at https://github.com/zhuhanqing/Lightening-Transformer.
Axisymmetric pseudoplastic thin films in planar and spherical geometries

A simplified, pseudoplastic rheology characterized by constant viscosity plateaus above and below a transition strain rate is applied to axisymmetric, gravitationally driven spreading of a thin fluid film with constant volume flux source in planar and spherical geometries. The model admits analytical solutions for flow velocity and volume flux. Shear thinning influence on layer evolution is investigated via numerical simulation. Isoviscous, asymptotic behaviors are recovered in small and large transition stress limits. The effect of viscosity ratio on layer extent agrees with scaling arguments. For intermediate transition stress, a flow behavior adjustment is observed consistent with heuristic arguments. Planar and spherical geometry solutions are in agreement for sufficiently small polar angle.
Feature Learning in Image Hierarchies using Functional Maximal Correlation

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in multiview systems. By framing view similarities as dependencies and ensuring contrastivity by imposing orthonormality, HFMCA achieves faster convergence and increased stability in self-supervised learning. HFMCA defines and measures dependencies within image hierarchies, from pixels and patches to full images. We find that the network topology for approximating orthonormal basis functions aligns with a vanilla CNN, enabling the decomposition of density ratios between neighboring layers of feature maps. This approach provides powerful interpretability, revealing the resemblance between supervision and self-supervision through the lens of internal representations.
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data.
Direct learning-based deep spiking neural networks: a review

The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.
Quantitative Analysis of Spectral Impacts on Silicon Photodiode Radiometers: Preprint

Inexpensive broadband pyranometers with silicon photodiode detectors have a non-uniform spectral response over the spectral range of 300-1100 nm. The response region includes only about 70% to 75% of the total energy in the terrestrial solar spectral distribution from 300 nm to 4000 nm. The solar spectrum constantly changes with solar position and atmospheric conditions. Relative spectral distributions of diffuse hemispherical irradiance sky radiation and total global hemispherical irradiance are drastically different. This analysis convolves a typical photodiode response with SMARTS 2.9. 5 spectral model spectra for different sites and atmospheric conditions. Differences in solar component spectra lead to differences on the order of 2% in global hemispherical and 5% or more in diffuse hemispherical irradiances from silicon radiometers. The result is that errors of more than 7% can occur in the computation of direct normal irradiance from global hemispherical irradiance and diffuse hemispherical irradiance using these radiometers.