Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Filter Pruning for Efficient Automatic Surface Defect Detection

Surface defect detection is a critical task in industrial production, and numerous methods have been proposed to achieve high detection accuracy. Although deep-learning-based approaches have achieved state-of-the-art (SOTA) performances, their vast computational cost and high memory footprint prevent their deployment in resource-constrained environments. To address this problem, we propose a collaborative filter pruning method for the defect detection model, which significantly reduces the number of required calculations and parameters while maintaining high performance, even in cases with tasks suffering from the class imbalance problem. Our method aims to obtain lightweight pruned models by removing unimportant filters according to their importance evaluated by both structural similarity and detail richness of corresponding feature maps. Moreover, to improve the performance of pruned models, we propose a knowledge-fused fine-tuning approach that fuses the knowledge derived from two teacher networks to look after both representation learning and classifier learning, alleviating the class imbalance problem. Experimental results on four public datasets demonstrate that the proposed approach performs favorably relative to the SOTA methods. In particular, the proposed method achieves 39× and 59× parameter compression for VGG-16 and ResNet-50, respectively, on the NEU-CLS dataset, with a very small detection accuracy loss (<0.2%).
A Weighted Soft-Max PNLMS Algorithm for Sparse System Identification

This paper presents a new Proportionate Normalized Least Mean Square (PNLMS) adaptive algorithm using a soft maximum operator for sparse system identification. To provide a high rate of convergence, soft maximum operator is employed along with a weighting factor, which is proportional to an estimation of output mean square error (MSE). Simulation results show the superiority of the proposed algorithm over its PNLMS-based counterparts.
The Role of Financial Distress on Company Life Cycle and Stock Return

This study aims to see how the company life cycle affects stock returns with financial distress as a moderating variable in companies listed on the Indonesia Stock Exchange between 2017 and 2021. Purposive sampling was used to obtain a sample of 71 companies. A panel regression analysis with a fixed effect model was used for the analysis. The study's findings show that the decline stage of a company's life cycle hurts stock returns, whereas the introduction, growth, and maturity stages do not affect stock returns. Financial distress can mitigate the impact of the company's decline stage on stock returns. However, it needs to mitigate the impact of the company's introduction, growth, and maturity stages on stock returns.
The circumstellar matter distribution of massive young stellar objects

A multiwavelength study of the circumstellar matter distribution of massive young stellar objects (MYSOs) was conducted. First, the potential of the new Herschel 70 micron data to resolve MYSOs in the Hi-GAL survey was analysed. These data have the highest resolution achieved at far infrared wavelengths where the spectral energy distribution of MYSOs peaks. These data showed that relatively isolated sources with high L^0.5/d, where L is the luminosity and d the distance of the source, are resolved at 70 micron. The analysis of these data and 1-D spherically symmetric radiative transfer modelling of three sources in the l=30 deg and 59 deg fields showed that they have a shallower density power law index than expected for infalling material. This suggests that the far-IR emission may be dominated by warm dust from the outflow cavity walls rather than rotational flattening as suggested by earlier studies. In order to explain the 70 micron observations, the circumstellar matter of the proto-typical MYSO AFGL 2591 was studied by utilising and modelling full resolution Herschel data from the HOBYS survey and other multi-wavelength dust continuum observations including high-resolution near-IR and mm interferometric data. A 2-D axi-symmetric radiative transfer model was used to find the density and temperature distributions that better reproduce the observations. The model that best fits the continuum observations has a rotationally flattened envelope, paraboloidal outflow cavities and a flared disc with a mass of 1 solar mass. As a result it was found that the extended emission observed at 70 micron can be explained in part by dust emission from the envelope outflow cavity walls. The modelling was able to reproduce most of the other multi-wavelength observations. Finally, the velocity structure of gas in the envelope of AFGL 2591 was studied by modelling methyl cyanide observations in the CH3CN J=12-11 transition at 1.3 mm. The transition K-ladder was fitted assuming a constant density and isothermal distribution of gas, and an excitation temperature ranging between 100-300 K was found. In addition, the first moment (velocity) maps are consistent with rotation of the inner envelope, and its linear velocity gradient is slower than the one observed at smaller scales. The radiative transfer modelling of the methyl cyanide data with a velocity structure of a rotating and infalling envelope suggests that rotation is faster than predicted by the model. This may be solved by magnetic fields transporting angular momentum from the accretion disc.
Partitions of finite frames

James M. Rosado PARTITIONS OF FINITE FRAMES 2015-2016 Hieu Nguyen, Ph.D. Master of Arts in Mathematics An open question stated by Marcus, Spielman, and Srivastava [10] asks "whether one can design an efficient algorithm to find the partitions guaranteed by Corollary 1.5. " This corollary states that given a set of vectors in C whose outer products sum to the identity there exists a partition of these vectors such that norms of the outer-product sums of each subset satisfy an inequality bound. Here particular types of vector sets called finite frames are analyzed and constructed to satisfy the inequality described in Corollary 1.5. In this thesis, rigorous proofs and formula-tions of outer-product norms are utilized to find these partitions and to identify constraints on the finite frames in order to satisfy Corollary 1.5.
Wave-current interactions: experiment and modelling

Wave-current interactions have been studied for decades. However, in widely used spectral wave models current-induced e ects still present a challenging topic and are included in a rather simplistic way. Despite many developments of the model source terms, current characteristics have not been incorporated directly and the background mean ow is considered only in the kinematic equations and by modifying the wind vector. In this dissertation the interaction of ocean waves with ambient currents are investigated focusing on the performance of spectral wave models in a variety of conditions where the interaction of waves with the mean ow is present. The e ect of a highly variable current eld on the wave spectrum is studied through a set of laboratory experiments providing new observations of the wave transformations under the in uence of currents. It is observed that currents have a signi cant impact on the wave-wave nonlinear exchanges by suppressing the energy transfer to high frequencies. This process is associated with the detuning of the four-wave resonance conditions. In addition, the directional analysis shows that the presence of currents broadens the directional distribution of waves, which occurs for both coowing and opposing currents and depends on the variability of the current eld. The experimental results point out to physics still not accounted in spectral models. This rst insight into possible e ects of currents on waves is followed by the investigation of the performance of spectral models in eld conditions. The models are tested on a range of scales from the large ocean basin of the Southern Ocean to nearshore regions. In the Southern Ocean the model results are improved when coupled to a global hydrodynamic reanalysis. The typical bias in wave height in southern latitudes is reduced due to an overall decrease in the e ective wind. The simulations of the peak and mean periods are also improved in the presence of currents. Although no improvements have been seen for the directional characteristics, the inclusion of a high resolution grid nested in the global grid shows that modelling wave refraction and associated processes of convergence and divergence of the wave
CaMKIIα neurons in the anterior insular cortex regulate attention behavior in mice

Introduction The insular cortex is involved in multiple physiological processes including working memory, pain, emotion, and interoceptive functions. Previous studies have indicated that the anterior insular cortex (aIC) also mediates interoceptive attention in humans. However, the exact cellular and physiological function of the aIC in the regulation of this process is still elusive. Methods In this study, using the 5-choice serial reaction time task (5-CSRTT) testing paradigm, we assessed the role of the aIC in visuospatial attention and impulsiveness in mice. Results The results showed a dramatic activation of c-Fos in the aIC CaMKIIα neurons after the 5-CSRTT procedure. In vivo fiber photometry revealed enhanced calcium signaling in aIC CaMKIIα neurons when the mice responded correctly. In addition, chemogenetic suppression of aIC CaMKIIα neurons led to increased incorrect responses within the appropriate time. Importantly, pharmacological activation of aIC CaMKIIα neurons enhanced their performance in the 5-CSRTT test. Discussion These results provide compelling evidence that aIC CaMKIIα neurons are essential for the modulation of attentional processing in mice.
Network Aggregation Process in Multilayer Air Transportation Networks

The air transportation network, one of the common multilayer complex systems, is composed of a collection of individual airlines, and each airline corresponds to a different layer. An important question is then how many airlines are really necessary to represent the optimal structure of a multilayer air transportation system. Here we take the Chinese air transportation network (CATN) as an example to explore the nature of multiplex systems through the procedure of network aggregation. Specifically, we propose a series of structural measures to characterize the CATN from the multilayered to the aggregated network level. We show how these measures evolve during the network aggregation process in which layers are gradually merged together and find that there is an evident structural transition that happened in the aggregated network with nine randomly chosen airlines merged, where the network features and construction cost of this network are almost equivalent to those of the present CATN with twenty-two airlines under this condition. These findings could shed some light on network structure optimization and management of the Chinese air transportation system.
Matrix‐Based Sensitivity Assessment of Soil Organic Carbon Storage: A Case Study from the ORCHIDEE‐MICT Model

Modeling of global soil organic carbon (SOC) is accompanied by large uncertainties. The heavy computational requirement limits our flexibility in disentangling uncertainty sources especially in high latitudes. We build a structured sensitivity analyzing framework through reorganizing the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE)‐aMeliorated Interactions between Carbon and Temperature (MICT) model with vertically discretized SOC into one matrix equation, which brings flexibility in comprehensive sensitivity assessment. Through Sobol's method enabled by the matrix, we systematically rank 34 relevant parameters according to variance explained by each parameter and find a strong control of carbon input and turnover time on long‐term SOC storages. From further analyses for each soil layer and regional assessment, we find that the active layer depth plays a critical role in the vertical distribution of SOC and SOC equilibrium stocks in northern high latitudes (>50°N). However, the impact of active layer depth on SOC is highly interactive and nonlinear, varying across soil layers and grid cells. The stronger impact of active layer depth on SOC comes from regions with shallow active layer depth (e.g., the northernmost part of America, Asia, and some Greenland regions). The model is sensitive to the parameter that controls vertical mixing (cryoturbation rate) but only when the vertical carbon input from vegetation is limited since the effect of vertical mixing is relatively small. And the current model structure may still lack mechanisms that effectively bury nonrecalcitrant SOC. We envision a future with more comprehensive model intercomparisons and assessments with an ensemble of land carbon models adopting the matrix‐based sensitivity framework.
Evaluation of cross-genus transferability of SSR markers from other legumes to two closely related Onobrychis (Fabaceae) taxa

Microsatellite markers previously developed for other leguminous species were tested for cross-genus transferability and evaluated for their potential usefulness in providing an improved assessment of the genetic relationships between two closely related taxa belonging to Onobrychis genus (Fabaceae). Candidate microsatellite markers were tested for polymorphism and replicability in sixteen populations of O. montana DC. subsp. transsilvanica (Simonk.) Jáv. and O. montana. Out of the 23 SSRs, there were identified seven polymorphic loci. In total 32 alleles were detected and the number of alleles per locus varied from two to six. PIC values ranged from 0.375 to 0.6454, and four SSRs displayed a PIC > 0.5. Relative uniform rates of genetic diversity were obtained. In case of O. montana DC. subsp. transsilvanica (Simonk.) Jáv. the observed and expected heterozygosity ranged from 0.100 to 0.952 and from 0.219 to 0.525, respectively, while for O. montana ranged from 0.166 to 0.750 and from 0.083 to 0.375, respectively. Seven polymorphic SSRs with clear and reproducible amplification were identified. These markers proved to be very efficient for unambiguous population discrimination based on both geographic and taxonomic criteria. Hereafter, these SSR markers can be used as tools for evolutionary studies in Onobrychis genus, as well in providing knowledge on patterns of the species phylogeography.
Algebraic Numbers and Topologically Equivalent Measures

A set-theoretical point of view to study algebraic numbers has been introduced. We extend a result of Navarro-Bermudez concerning shift invariant measures in the Cantor space which are topologically equivalent to shift invariant measures which correspond to some algebraic integers. It is known that any transcendental numbers and rational numbers in the unit interval are not binomial. We proved that there are algebraic numbers of degree greater than two so that they are binomial numbers. Algebraic integers of degree 2 are proved not to be binomial numbers. A few compositive relations having to do with algebraic numbers on the unit interval have been studied; for instance, rationally related, integrally related, binomially related, B1-related relations. A formula between binomial numbers and binomial coefficients has been stated. A generalized algebraic equation related to topologically equivalent measures has also been stated.
On finite element modelling of surface tension

In Part I, a finite element model of surface tension has been discussed and used to solve some quasi-static prob- lems. The quasi-static analysis is often required to find not only the initial shape of the liquid but also the static equi- librium state of a liquid body before a dynamic analysis can be carried out. In general, natural and industrial processes in which surface tension force is dominant are of dynamic nature. In this second part of this work, the dynamic effects will be included in the finite element model described in Part I. A fully Lagrangian finite element method is used to solve the free surface flow problem and Newtonian constitutive equations describing the fluid behaviour are approximated over a finite time interval. As a result the momentum equa- tions are function of nodal position instead of velocities. The resulting ordinary differential equation is integrated using Newmark algorithm. To avoid overly distorted elements an adaptive remeshing strategy is adopted. The adaptive strategy employs a remeshing indicator based on viscous dissipation functional and incorporates an appropriate transfer operator. The validation of the model is performed by comparing the finite element solutions to available analytical solutions of a droplet oscillations and experimental results pertaining to stretching of a liquid bridge.
A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments

Abstract As part of the data processing of high-throughput-sequencing experiments count data are produced representing the amount of reads that map to specific genomic regions. Count data also arise in mass spectrometric experiments for the detection of protein-protein interactions. For evaluating new computational methods for the analysis of sequencing count data or spectral count data from proteomics experiments artificial count data is thus required. Although, some methods for the generation of artificial sequencing count data have been proposed, all of them simulate single sequencing runs, omitting thus the correlation structure between the individual genomic features, or they are limited to specific structures. We propose to draw correlated data from the multivariate normal distribution and round these continuous data in order to obtain discrete counts. In our approach, the required distribution parameters can either be constructed in different ways or estimated from real count data. Because rounding affects the correlation structure we evaluate the use of shrinkage estimators that have already been used in the context of artificial expression data from DNA microarrays. Our approach turned out to be useful for the simulation of counts for defined subsets of features such as individual pathways or GO categories.
Effects of Soybean Phosphate Transporter Gene GmPHT2 on Pi Transport and Plant Growth under Limited Pi Supply Condition

Phosphorus is an essential macronutrient for plant growth and development, but phosphate resources are limited and rapidly depleting due to massive global agricultural demand. This study identified two genes in the phosphate transporter 2 (PHT2) family of soybean by bioinformatics. The expression patterns of two genes by qRT-PCR at leaves and all were induced by low-phosphate stress. After low-phosphate stress, GmPHT2;2 expression was significantly higher than GmPHT2;1, and the same trend was observed throughout the reproductive period. The result of heterologous expression of GmPHT2 in Arabidopsis knockout mutants of atpht2;1 shows that chloroplasts and whole-plant phosphorus content were significantly higher in plants complementation of GmPHT2;2 than in plants complementation of GmPHT2;1. This suggests that GmPHT2;2 may play a more important role in plant phosphorus metabolic homeostasis during low-phosphate stress than GmPHT2;1. In the yeast backfill assay, both genes were able to backfill the ability of the defective yeast to utilize phosphorus. GmPHT2 expression was up-regulated by a low-temperature treatment at 4 °C, implying that GmPHT2;1 may play a role in soybean response to low-temperature stress, in addition to being involved in phosphorus transport processes. GmPHT2;1 and GmPHT2;2 exhibit a cyclic pattern of circadian variation in response to light, with the same pattern of gene expression changes under red, blue, and white light conditions. GmPHT2 protein was found in the chloroplast, according to subcellular localization analysis. We conclude that GmPHT2 is a typical phosphate transporter gene that can improve plant acquisition efficiency.
Controlling Clustering Coefficient of Graphs by Means of 2-Switch Method

—A clustering coe ﬃ cient control algorithm for simple connected undirected graphs is studied. This algo-rithm is based on the 2-switch which is a well-known graph transformation that preserves the degree of each node. We ﬁrst derive an explicit formula for the amount of change in the clustering coe ﬃ cient caused by a single 2-switch. We next show through computer simulations that the clustering coe ﬃ cient of scale-free graphs can be controlled over a wide range by using this algorithm.
Utilizing Word Index Approach with LSTM Architecture for Extracting Adverse Drug Reaction from Medical Reviews

— Adverse Drug Reaction (ADR) detection from social reviews refers to the task of exploring medical online stores and social reviews for extracting any mention of abnormal reactions that occur after consuming a particular medical product by the consumers themselves. A variety of approaches have been used for extracting ADR from social/medical reviews. These approaches include machine learning, dictionary-based and statistical approaches. Yet, these approaches showed either a high dependency on using an external knowledge source for ADR detection or relying on domain-dependent mechanisms that might lose contextual information. This study aims to propose word sequencing with Long Short-Term Memory (LSTM) architecture. A benchmark dataset of MedSyn has been used in the experiments. Then, a word indexing, mapping, and padding method have been used to represent the words within the reviews as fixed sequences. Such sequences have been fed into the LSTM consequentially. Experimental results showed that the proposed LSTM could achieve an F1 score of up to 92%. Comparing such a finding to the baseline studies reveals the superiority of LSTM. The demonstration of the efficacy of the proposed method has taken different forms including the examination of word indexing with different classifiers, the examination of different features with LSTM, and through the comparison against the baseline studies.
A Reinforcement Learning Approach to Autonomous Decision Making of Intelligent Vehicles on Highways

Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a reinforcement learning approach with value function approximation and feature learning is proposed for autonomous decision making of intelligent vehicles on highways. In the proposed approach, the sequential decision making problem for lane changing and overtaking is modeled as a Markov decision process with multiple goals, including safety, speediness, smoothness, etc. In order to learn optimized policies for autonomous decision-making, a multiobjective approximate policy iteration (MO-API) algorithm is presented. The features for value function approximation are learned in a data-driven way, where sparse kernel-based features or manifold-based features can be constructed based on data samples. Compared with previous RL algorithms such as multiobjective Q-learning, the MO-API approach uses data-driven feature representation for value and policy approximation so that better learning efficiency can be achieved. A highway simulation environment using a 14 degree-of-freedom vehicle dynamics model was established to generate training data and test the performance of different decision-making methods for intelligent vehicles on highways. The results illustrate the advantages of the proposed MO-API method under different traffic conditions. Furthermore, we also tested the learned decision policy on a real autonomous vehicle to implement overtaking decision and control under normal traffic on highways. The experimental results also demonstrate the effectiveness of the proposed method.