Miracle-dz/UniFAR-FTUs huggingface dataset:
Curriculum Learning-Based Approaches for End-to-End Gas Recognition

Deep learning has demonstrated great potential in the field of gas recognition using an electronic nose. However, owing to the limited resources of embedded devices, they can only meet the computing and storage requirements of lightweight deep neural network (DNN) models, which reduces the classification accuracy. This study proposes three curriculum learning (CL) approaches to improve the performance of lightweight DNN models for end-to-end gas recognition, including a domain knowledge-based approach and two approaches that automatically arrange the curriculum. Among the two automated methods, the first uses the correlation strength between sample responses to measure the difficulty of the training samples, and the second is based on the classification difficulty and disagreements among multiple teacher models. Furthermore, our approaches are combined with a mixup-based data augmentation method to apply CL to small datasets. The superiority of the proposed approaches was verified using 14 electronic nose datasets. The experimental results demonstrate that these approaches can be used on different types of datasets while boosting the gas recognition accuracy and $F1$ score of various DNN models by 9.9% and 10.9% on average, respectively, which can guide the future design of high-accuracy gas sensing embedded systems.
A Topology Based Discretization of PDAEs Describing Water Transportation Networks

We consider partial differential algebraic systems (PDAEs) describing water transportation networks. Similar to the approach in [6], we follow the method of lines for the discretization. However, we do not consider free surface flow models but pressure flow models covering hydraulic shocks. Moreover, we include switching models reflecting the on/off state of pumpes and valves. Aiming at a stable numerical simulation of the PDAEs we present a topology based spatial discretization that results in a differential algebraic system (DAE) of index 1. Furthermore we show that the DAE index can be higher than 1 if the spatial discretization is not adapted to the position of reservoirs and demand nodes within the network. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Neural Networks-Based Detection of Line Overflow Cyberattacks on AC State-Estimation of Smart Grids

A new category of false data injection (FDI) cyberattacks was recently proposed that can result in line overflow and cascading failures in electric grids by bypassing ac state-estimation. However, these attacks are bounded by multiple nonlinear constraints that provide different temporal features in the bad data that existing FDI detectors might fail to identify. In this article, a cyberattack model using stealthy FDIs on load or generation units is formulated that may result in multiple transmission line overflows without being detected by bad data detection (BDD) algorithms embedded in ac state-estimation. A detection framework based on neural networks is developed that uses bad data generated from the attack model for training purposes and detects the stealthy FDIs. Results confirm the detection of stealthy FDIs with accuracy and precision higher than 99% with 0–15% measurement noise levels. The developed detection framework outperforms existing BDD algorithms of ac state-estimators.
Integrating Text Mining into the Curation of Disease Maps

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.
PROBLEMA E ALGORITMOS DE COLORAÇÃO EM GRAFOS - EXATOS E HEURÍSTICOS

Context: Given a graph and an integer the graph coloring problem consists to use a set of colors to assign to each node of a certain color, so that adjacent vertices have distinct color. Despite the existence of accurate methods capable of determining the staining solution to the problem, it is observed that these techniques have an exponential complexity in some cases. The limitations presented by exact methods when applied to instances of greater complexity motivated the development of several heuristics, able to indicate satisfactory solutions for graphs in general. This paper describes the problem of coloring graphs, presents some of the techniques that can be used in their determination, besides presenting some real problems that can be modeled as a graph and solved based on the result achieved by applying a coloring on.
Right-handed Neutrinos in $\nu_{\mu} + e \to \nu_{\mu} + e$ Elastic Scattering

In this paper a scenario admitting the participation of the exotic right-handed scalar $S_{R}$ coupling in addition to the standard left-handed $(V, A)_{L}$ couplings in the low energy neutrino-electron scattering is considered. The main goal is to show how the presence of the right-handed neutrinos in the $(\nu e)$ scattering changes the laboratory differential cross section in relation to the Standard Model prediction. We consider the $(\nu e)$ process at the level of the four-fermion point interaction. Muon-neutrinos are assumed to be massive and to be polarized Dirac fermions. In the laboratory differential cross section the new interference term between the the standard $V_{L}$ coupling of the left-handed neutrino and exotic $S_{R}$ coupling of the right-handed neutrino appears which does not vanish in the limit of massless neutrino. This new contribution, including the information about the transverse neutrino polarization, generates the azimuthal assymetry in the angular distribution of the recoil electrons. This regularity would be the proof of the presence of R-handed neutrinos in the $\nu e$ scattering.
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics

Abstract. Process-based ecological models are essential tools to quantify and predict forest growth and carbon cycles under the background of climate change. The accurate description of phenology and tree growth processes enables an improved understanding and predictive modeling of forest dynamics. An individual tree-based carbon model, FORCCHN2 (Forest Ecosystem Carbon Budget Model for China version 2.0), used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. This model performed well in reducing uncertainty when predicting forest carbon fluxes. Here, we describe the framework in detail and provide the source code of FORCCHN2. We also present a dynamic-link library (DLL) package containing the latest version of FORCCHN2. This package has the advantage of using Fortran as an interface to make the model run fast on a daily step, and the package also allows users to call it with their preferred computer tools (e.g., MATLAB, R, Python). FORCCHN2 can be used directly to predict spring and autumn phenological dates, daily carbon fluxes (including photosynthesis, aboveground and belowground autotrophic respiration, and soil heterotrophic respiration), and biomass on plot, regional, and hemispheric scales. As case studies, we provide an example of FORCCHN2 running model validations in 78 forest sites and an example model application for the carbon dynamics of Northern Hemisphere forests. We demonstrate that FORCCHN2 can produce a reasonable agreement with flux observations. Given the potential importance of the application of this ecological model in many studies, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimations.
Feeding behavior innovation increases foraging efficiency in the Amur falcon but may be a threat to Asian particolored bats

Abstract Behavioral innovations are rare and infrequent in the natural world, but they are pivotal for animals to respond to environmental changes. The ecological benefits of these innovations remain unknown, especially in wild populations. Here, two foraging strategies and three eating behaviors of the Amur falcon (Falco amurensis) were observed during predation on Asian particolored bats (Vespertilio sinensis) across 3 years. We demonstrated that an eating behavioral innovation in F. amurensis increased the foraging efficiency of V. sinensis more than twofold during 3 consecutive years. This showed that changes in feeding behavior by a bird strongly influenced the rate of energy intake. Since predation on bats by falcons mainly occurred during the lactation and post‐lactation of bats, this may have a certain level of negative effect on the bat population.
Assessment of uncertainties in QRPA $0\nu\beta\beta$-decay nuclear matrix elements

The nuclear matrix elements $M^{0\nu}$ of the neutrinoless double beta decay ($0\nu\beta\beta$) of most nuclei with known $2\nu\beta\beta$-decay rates are systematically evaluated using the Quasiparticle Random Phase Approximation (QRPA) and Renormalized QRPA (RQRPA). The experimental $2\nu\beta\beta$-decay rate is used to adjust the most relevant parameter, the strength of the particle-particle interaction. New results confirm that with such procedure the $M^{0\nu}$ values become essentially independent on the size of the single-particle basis. Furthermore, the matrix elements are shown to be also rather stable with respect to the possible quenching of the axial vector strength parametrized by reducing the coupling constant $g_A$, as well as to the uncertainties of parameters describing the short range nucleon correlations. Theoretical arguments in favor of the adopted way of determining the interaction parameters are presented. Furthermore, a discussion of other implicit and explicit parameters, inherent to the QRPA method, is presented. Comparison is made of the ways these factors are chosen by different authors. It is suggested that most of the spread among the published $0\nu\beta\beta$ decay nuclear matrix elements can be ascribed to these choices.
Correlation of PCSK1 with nonalcoholic fatty liver disease in a Han Chinese population: a case-control observational study

Objective: This study aimed to investigate the association between single-nucleotide polymorphisms (SNPs) of PCSK1 (proprotein convertase subtilisin/kexin type 1) related to obesity and nonalcoholic fatty liver disease (NAFLD). Methods: In this case-control observational study, four candidate SNPs (rs6234, rs155971, rs6232, rs3811951) of PCSK1 were genotyped in 732 NAFLD patients and 823 healthy control participants, all of whom were of ethnic Han Chinese descent. All participants came from Shanghai, China, and joined our study during 2015 to 2016. The frequencies of each allele and genotype, paired linkage disequilibrium, and haplotype were calculated on the SHEsis platform. In addition to SHEsis, five different genetic models (codominant, dominant, recessive, overdominant, and log-additive) were employed to identify the correlation between genotype frequency and NAFLD. This study was approved by the Medical Ethics Committee of Shanghai University of Traditional Chinese Medicine on December 18, 2017 (approved No. 2017LCSY069). Results: In a comparison of NAFLD patients and healthy participants, none of the four PCSK1 SNPs were significantly correlated with the occurrence of NAFLD (P > 0.05), in either genotypic or allelic distribution. The recessive model of rs3811951 appeared to show a correlation (odds ratio = 1.077; 95% confidence interval = 0.924–1.256; P = 0.04), but there was no statistical significance after Bonferroni correction (Pcorr > 0.0125). Conclusions: Four obesity-related PCSK1 SNPs (rs6234, rs155971, rs6232, rs3811951) showed no significant correlation with the development of NAFLD in a Han Chinese population.
Ship Detection of Polarimetric SAR Images Using a Nonlocal Spatial Information-Guided Method

Ship detection of polarimetric synthetic aperture radar (PolSAR) plays an important role in marine monitoring and ocean protection. Over the past years, local spatial information around pixels has been successfully applied to this task. However, few works have been done on PolSAR ship detection using nonlocal spatial information (NSI). Within this context, we here propose one NSI-guided ship detection method PMR. Briefly, the feature power difference (PD) is first constructed by computing the total PD between the center pixel <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> and its most similar nonlocal pixel <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> within a <inline-formula> <tex-math notation="LaTeX">$7 \times 7$ </tex-math></inline-formula> window. Then, the polarimetric feature reflection symmetry (RS) is introduced into PD to construct the method PMR (i.e., PD Multiply RS) for further enhancing the target-to-clutter ratio (TCR) and improving the ship detection accuracy. Experiments carried out on three real PolSAR datasets show that, in comparison with some other methods, especially the recently proposed local neighborhood information-based ship detector PWF<inline-formula> <tex-math notation="LaTeX">$_{N}$ </tex-math></inline-formula>, PMR is more apt for ship detection. On average, its figure of merit (FoM) and TCR values respectively surpass PWF<inline-formula> <tex-math notation="LaTeX">$_{N}~0.24$ </tex-math></inline-formula> and 12.83 dB.
A Method for Estimating the Amplitude–Frequency Characteristics of the Cable Line Model With Distributed Parameters

The amplitude–frequency characteristics play an essential role in analyzing transmission power grids, such as harmonic amplification using the amplitude–frequency characteristics as the harmonic amplification factor, harmonic resonance, etc. However, when the cable line with distributed parameters is connected to powers, the model would be troublesome to be calculated for the amplitude–frequency characteristics by the existing methods. To fill this gap, we proposed a method of variable covariance probabilistic data association (PDA) Kalman filtering deconvolution. First, a typical high-voltage cable transmission system (HVCTS) and its dynamic model are established. Next, a square wave impulse excitation (SWIR) is injected into the system, and the square wave impulse response is extracted at the measurement ports. Then the variable covariance PDA Kalman filter (VCPDAKF) algorithm is proposed to denoise the noisy square wave impulse response. Meanwhile, the deconvolution method is used to obtain the amplitude–frequency characteristics based on the convolution model. Finally, it is verified through simulation and experiment that the VCPDAKF algorithm could effectively denoise the noisy square wave impulse response. The effectiveness of the proposed method for the amplitude–frequency characteristics is verified.
Adaptive Minimum Mean Square Error Forecast of Missile Trajectory Using Stochastic Difference Equation Models.

Abstract : Data projection (forecasting) methods are illustrated using missile data supplied by Quality Evaluation Division of White Sands Missile Range. The process of model building making iterative use of identification (using the autocorrelation function) fitting (maximum likelihood estimation) and diagnostic checking (analysis residuals) is illustrated in the building of an appropriate stochastic difference equation model. It is shown in detail how all the following may be calculated directly from the model: the projection (forecast) function, the memory function, the error of the projected value at any lead time; and the updating of the projection function.
Seascapes and foraging success: Movement and resource discovery by a benthic marine herbivore

Abstract Spatially concentrated resources result in patch‐based foraging, wherein the detection and choice of patches as well as the process of locating and exploiting resource patches involve moving through an explicit landscape composed of both resources and barriers to movement. An understanding of behavioral responses to resources and barriers is key to interpreting observed ecological patterns. We examined the process of resource discovery in the context of a heterogeneous seascape using sea urchins and drift kelp in urchin barrens as a model system. Under field conditions, we manipulated both the presence of a highly valuable resource (drift kelp) and a barrier to movement (sandy substratum) to test the interacting influence of these two factors on the process of resource discovery in barren grounds by urchins. We removed all foraging urchins (Strongylocentrotus droebachiensis) from replicate areas and monitored urchin recolonization and kelp consumption. We tested two hypotheses: (1) unstable substratum is a barrier to urchin movement and (2) the movement behavior of sea urchins is modified by the presence of drift kelp. Very few urchins were found on sand, sand was a permeable barrier to urchin movement, and the permeability of this barrier varied between sites. In general, partial recolonization occurred strikingly rapidly, but sand slowed the consumption of drift kelp by limiting the number of urchins. Differences in the permeability of sand barriers between sites could be driven by differences in the size structure of urchin populations, indicating size‐specific environmental effects on foraging behavior. We demonstrate the influence of patchy seascapes in modulating grazing intensity in barren grounds through modifications of foraging behavior. Behavioral processes modified by environmental barriers play an important role in determining grazing pressure, the existence of refuges for new algal recruits, and ultimately the dynamics of urchin‐algal interactions in barren grounds.
Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.
The Geometry of the H I of Several Members of the M81 Group: The H I Is Fractal

We use the box-counting method on single H I contours and the perimeter-area method on families of contours to show that the projected H I distributions of seven members of the M81 group are fractal. All seven have fractal dimensions that cover a small range, 1.5 ≥ D ≥ 1.2. This is nearly the same dimension as that of CO clouds in the Milky Way and suggests that the ISM may have a fractal dimension that stays within a limited range at all scales. Reliable determination of fractal dimension requires that observations be made with high angular and frequency resolution. If theorems of projection of opaque fractals apply to H I, then the dimension of the deprojected H I distributions may be 1.5 ≥ D ≥ 1.2 as well. Fractal structures have much more surface area than smooth structures, allowing heating, cooling, and material exchange to proceed much more rapidly.
Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor

With the growing demand for processing deep learning applications on edge devices, on-device DNN training has become a major workload to execute a variety of vision tasks suited for users. Therefore, architectures employed with algorithm co-design to accelerate the training process have been steadily studied. However, previous solutions are mostly supported by extended versions of the inference studies, such as sparsity, data flow, quantization, etc. Moreover, most works examine their schemes on from-the-scratch training that cannot tolerate inaccurate computing. Accordingly, there are still factors that hinder the overall speed of the DNN training process that has not been addressed in practical workloads. In this work, we propose a runtime convergence monitor to achieve massive computational savings in the practical on-device training workloads (i.e., transfer-learning-based task adaptation). By monitoring the network output data, we determine the training intensity of incoming tasks and adaptively detect the convergence in iteration intervals for training diverse datasets. Furthermore, we enable the computation skip of converged images determined by the monitored prediction probability to enhance the training speed within an iteration. As a result, we perform an accurate but fast convergence in model training for the task adaptation with minimal overhead. Unlike the previous approximation methods, our monitoring system enables runtime optimization and can be easily applicable to any type of accelerator attaining significant speedup. Evaluation results on various datasets show geomean of $2.2\times $ speedup when applied in any systolic architectures and further enhancement of $3.6\times $ when applied in accelerators dedicated for on-device training.
Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Commentary. Authors' reply

We provide sufficient conditions for estimating from longitudinal data the causal effect of a time-dependent exposure or treatment on the marginal probability of response for a dichotomous outcome. We then show how one can estimate this effect under these conditions using the g-computation algorithm of Robins. We also derive the conditions under which some current approaches to the analysis of longitudinal data, such as the generalized estimating equations (GEE) approach of Zeger and Liang, the feedback model techniques of Liang and Zeger, and within-subject conditional methods, can provide valid tests and estimates of causal effects. We use out methods to estimate the causal effect of maternal stress on the marginal probability of a child's illness from the Mothers' Stress and Children's Morbidity data and compare out results with those previously obtained by Zeger and Liang using a GEE approach.