A Wiener-Hopf factorization related potential measure for spectrally negative Lévy process

For spectrally negative Lévy process (SNLP), we find an expression, in terms of scale functions, for a potential measure involving the maximum and the last time of reaching the maximum up to a draw-down time. As applications, we obtain a potential measure for the reflected SNLP and recover a joint Laplace transform for the Wiener-Hopf factorization for SNLP.

Efficient Clustering Network Based on Matrix Factorization

Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

Wiener-Hopf Factorization of a Class of Matrix Functions and Corona Theorem

By using the Riemann-Hilbert method and the Corona theorem, Wiener-Hopf factorization for a class of matrix functions is studied. Under appropriate assumption, a sufficient and neces- sary condition for the existence of the matrix function admitting canonical factorization is obtained and the solution to a class of non-linear Riemann-Hilbert problems is also given. Furthermore, by means of non-standard Corona theorem partial estimation of the general factorization can be obtained.

NFA:A neural factorization autoencoder based online telephony fraud detection

The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

Functions of nuclear factor Y in nervous system development,function and health

Nuclear factor Y is a ubiquitous heterotrimeric transcription factor complex conserved across eukaryotes that binds to CCAAT boxes,one of the most common motifs found in gene promoters and enhancers.Over the last 30 years,research has revealed that the nuclear factor Y complex controls many aspects of brain development,including differentiation,axon guidance,homeostasis,disease,and most recently regeneration.However,a complete understanding of transcriptional regulatory networks,including how the nuclear factor Y complex binds to specific CCAAT boxes to perform its function remains elusive.In this review,we explore the nuclear factor Y complex’s role and mode of action during brain development,as well as how genomic technologies may expand understanding of this key regulator of gene expression.

The effects of exercise interventions on brain-derived neurotrophic factor levels in children and adolescents:a meta-analysis

Brain-derived neurotrophic factor is a crucial neurotrophic factor that plays a significant role in brain health. Although the vast majority of meta-analyses have confirmed that exercise interventions can increase brain-derived neurotrophic factor levels in children and adolescents, the effects of specific types of exercise on brain-derived neurotrophic factor levels are still controversial. To address this issue, we used meta-analytic methods to quantitatively evaluate, analyze, and integrate relevant studies. Our goals were to formulate general conclusions regarding the use of exercise interventions, explore the physiological mechanisms by which exercise improves brain health and cognitive ability in children and adolescents, and provide a reliable foundation for follow-up research. We used the Pub Med, Web of Science, Science Direct, Springer, Wiley Online Library, Weipu, Wanfang, and China National Knowledge Infrastructure databases to search for randomized controlled trials examining the influences of exercise interventions on brain-derived neurotrophic factor levels in children and adolescents. The extracted data were analyzed using Review Manager 5.3. According to the inclusion criteria, we assessed randomized controlled trials in which the samples were mainly children and adolescents, and the outcome indicators were measured before and after the intervention. We excluded animal experiments, studies that lacked a control group, and those that did not report quantitative results. The mean difference(MD;before versus after intervention) was used to evaluate the effect of exercise on brain-derived neurotrophic factor levels in children and adolescents. Overall, 531 participants(60 children and 471 adolescents, 10.9–16.1 years) were included from 13 randomized controlled trials. Heterogeneity was evaluated using the Q statistic and I^(2) test provided by Review Manager software. The meta-analysis showed that there was no heterogeneity among the studies(P = 0.67, I^(2) = 0.00%). The combined effect of the interventions was significant(MD = 2.88, 95% CI: 1.53–4.22, P < 0.0001), indicating that the brain-derived neurotrophic factor levels of the children and adolescents in the exercise group were significantly higher than those in the control group. In conclusion, different types of exercise interventions significantly increased brain-derived neurotrophic factor levels in children and adolescents. However, because of the small sample size of this meta-analysis, more high-quality research is needed to verify our conclusions. This metaanalysis was registered at PROSPERO(registration ID: CRD42023439408).

Telencephalic stab wound injury induces regenerative angiogenesis and neurogenesis in zebrafish:unveiling the role of vascular endothelial growth factor signaling and microglia

After brain damage,regenerative angiogenesis and neurogenesis have been shown to occur simultaneously in mammals,suggesting a close link between these processes.However,the mechanisms by which these processes interact are not well understood.In this work,we aimed to study the correlation between angiogenesis and neurogenesis after a telencephalic stab wound injury.To this end,we used zebrafish as a relevant model of neuroplasticity and brain repair mechanisms.First,using the Tg(fli1:EGFP×mpeg1.1:mCherry)zebrafish line,which enables visualization of blood vessels and microglia respectively,we analyzed regenerative angiogenesis from 1 to 21 days post-lesion.In parallel,we monitored brain cell proliferation in neurogenic niches localized in the ventricular zone by using immunohistochemistry.We found that after brain damage,the blood vessel area and width as well as expression of the fli1 transgene and vascular endothelial growth factor(vegfaa and vegfbb)were increased.At the same time,neural stem cell proliferation was also increased,peaking between 3 and 5 days post-lesion in a manner similar to angiogenesis,along with the recruitment of microglia.Then,through pharmacological manipulation by injecting an anti-angiogenic drug(Tivozanib)or Vegf at the lesion site,we demonstrated that blocking or activating Vegf signaling modulated both angiogenic and neurogenic processes,as well as microglial recruitment.Finally,we showed that inhibition of microglia by clodronate-containing liposome injection or dexamethasone treatment impairs regenerative neurogenesis,as previously described,as well as injury-induced angiogenesis.In conclusion,we have described regenerative angiogenesis in zebrafish for the first time and have highlighted the role of inflammation in this process.In addition,we have shown that both angiogenesis and neurogenesis are involved in brain repair and that microglia and inflammation-dependent mechanisms activated by Vegf signaling are important contributors to these processes.This study paves the way for a better understanding of the effect of Vegf on microglia and for studies aimed at promoting angiogenesis to improve brain plasticity after brain injury.

Age-related driving mechanisms of retinal diseases and neuroprotection by transcription factor EB-targeted therapy

Retinal aging has been recognized as a significant risk factor for various retinal disorders,including diabetic retinopathy,age-related macular degeneration,and glaucoma,following a growing understanding of the molecular underpinnings of their development.This comprehensive review explores the mechanisms of retinal aging and investigates potential neuroprotective approaches,focusing on the activation of transcription factor EB.Recent meta-analyses have demonstrated promising outcomes of transcription factor EB-targeted strategies,such as exercise,calorie restriction,rapamycin,and metformin,in patients and animal models of these common retinal diseases.The review critically assesses the role of transcription factor EB in retinal biology during aging,its neuroprotective effects,and its therapeutic potential for retinal disorders.The impact of transcription factor EB on retinal aging is cell-specific,influencing metabolic reprogramming and energy homeostasis in retinal neurons through the regulation of mitochondrial quality control and nutrient-sensing pathways.In vascular endothelial cells,transcription factor EB controls important processes,including endothelial cell proliferation,endothelial tube formation,and nitric oxide levels,thereby influencing the inner blood-retinal barrier,angiogenesis,and retinal microvasculature.Additionally,transcription factor EB affects vascular smooth muscle cells,inhibiting vascular calcification and atherogenesis.In retinal pigment epithelial cells,transcription factor EB modulates functions such as autophagy,lysosomal dynamics,and clearance of the aging pigment lipofuscin,thereby promoting photoreceptor survival and regulating vascular endothelial growth factor A expression involved in neovascularization.These cell-specific functions of transcription factor EB significantly impact retinal aging mechanisms encompassing proteostasis,neuronal synapse plasticity,energy metabolism,microvasculature,and inflammation,ultimately offering protection against retinal aging and diseases.The review emphasizes transcription factor EB as a potential therapeutic target for retinal diseases.Therefore,it is imperative to obtain well-controlled direct experimental evidence to confirm the efficacy of transcription factor EB modulation in retinal diseases while minimizing its risk of adverse effects.

Brain-derived neurotrophic factor signaling in the neuromuscular junction during developmental axonal competition and synapse elimination

During the development of the nervous system,there is an overproduction of neurons and synapses.Hebbian competition between neighboring nerve endings and synapses performing different activity levels leads to their elimination or strengthening.We have extensively studied the involvement of the brain-derived neurotrophic factor-Tropomyosin-related kinase B receptor neurotrophic retrograde pathway,at the neuromuscular junction,in the axonal development and synapse elimination process versus the synapse consolidation.The purpose of this review is to describe the neurotrophic influence on developmental synapse elimination,in relation to other molecular pathways that we and others have found to regulate this process.In particular,we summarize our published results based on transmitter release analysis and axonal counts to show the different involvement of the presynaptic acetylcholine muscarinic autoreceptors,coupled to downstream serine-threonine protein kinases A and C(PKA and PKC)and voltage-gated calcium channels,at different nerve endings in developmental competition.The dynamic changes that occur simultaneously in several nerve terminals and synapses converge across a postsynaptic site,influence each other,and require careful studies to individualize the mechanisms of specific endings.We describe an activity-dependent balance(related to the extent of transmitter release)between the presynaptic muscarinic subtypes and the neurotrophin-mediated TrkB/p75NTR pathways that can influence the timing and fate of the competitive interactions between the different axon terminals.The downstream displacement of the PKA/PKC activity ratio to lower values,both in competing nerve terminals and at postsynaptic sites,plays a relevant role in controlling the elimination of supernumerary synapses.Finally,calcium entry through L-and P/Q-subtypes of voltage-gated calcium channels(both channels are present,together with the N-type channel in developing nerve terminals)contributes to reduce transmitter release and promote withdrawal of the most unfavorable nerve terminals during elimination(the weakest in acetylcholine release and those that have already become silent).The main findings contribute to a better understanding of punishment-rewarding interactions between nerve endings during development.Identifying the molecular targets and signaling pathways that allow synapse consolidation or withdrawal of synapses in different situations is important for potential therapies in neurodegenerative diseases.

The cGAS-STING-interferon regulatory factor 7 pathway regulates neuroinflammation in Parkinson's disease

Interferon regulatory factor 7 plays a crucial role in the innate immune response.However,whether interferon regulatory factor 7-mediated signaling contributes to Parkinson's disease remains unknown.Here we report that interferon regulatory factor 7 is markedly up-regulated in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced mouse model of Parkinson's disease and co-localizes with microglial cells.Both the selective cyclic guanosine monophosphate adenosine monophosphate synthase inhibitor RU.521 and the stimulator of interferon genes inhibitor H151 effectively suppressed interferon regulatory factor 7 activation in BV2 microglia exposed to 1-methyl-4-phenylpyridinium and inhibited transformation of mouse BV2 microglia into the neurotoxic M1 phenotype.In addition,si RNA-mediated knockdown of interferon regulatory factor 7 expression in BV2 microglia reduced the expression of inducible nitric oxide synthase,tumor necrosis factorα,CD16,CD32,and CD86 and increased the expression of the anti-inflammatory markers ARG1 and YM1.Taken together,our findings indicate that the cyclic guanosine monophosphate adenosine monophosphate synthase-stimulator of interferon genes-interferon regulatory factor 7 pathway plays a crucial role in the pathogenesis of Parkinson's disease.

Sparseness-controlled non-negative tensor factorization and its application in machinery fault diagnosis

Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.

Local hierarchical non-negative tensor factorization and its application in machinery fault diagnosis

Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization（NMF） model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor＇s vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods.

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

ON ORTHOGONAL (0,f)-FACTORIZATIONS

Let G be a graph and f an integer-valued function defined on V(G). It is proved that every (0,mf - m+1)-graph G has a (0,f)-factorization orthogonal to any given subgraph with m edges.

K_(1,p)~k-FACTORIZATION OF COMPLETE BIPARTITE GRAPHS

In this paper, it is shown that a sufficient condition for the existence of a K 1,p k factorization of K m,n , whenever p is a prime number and k is a positive integer, is (1) m≤p kn,(2) n≤p km,(3)p kn-m≡p km-n ≡0(mod( p 2k -1 )) and (4) (p kn-m)(p km-n) ≡0(mod( p k -1)p k×(p 2k -1)(m+n)) .

A Backward Stable Hyperbolic QR Factorization Method for Solving Indefinite Least Squares Problem

We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.

Robust Control for Uncertain Nonlinear Feedback Systems Using Operator-based Right Coprime Factorization

The robust control issue for uncertain nonlinear system is discussed by using the method of right coprime factorization. As it is difficult to obtain the inverse of the right factor due to the high nonlinearity, the proving of the Bezout identity becomes troublesome. Therefore, two sufficient conditions are derived to manage this problem with the nonlinear feedback system as well as that with the uncertain nonlinear feedback system under the definition of Lipschitz norm. A simulation of temperature control is given to demonstrate the validity of the proposed method.

ON(g,f)-FACTORIZATIONS OF GRAPHS

Let G be a graph and g, f be two nonnegative integer-valued functions defined on the vertices set V(G) of G and g less than or equal to f. A (g, f)-factor of a graph G is a spanning subgraph F of G such that g(x)less than or equal to d(F)(x)less than or equal to f(x) for all x is an element of V(G). If G itself is a (g, f)-factor, then it is said that G is a (g, f)-graph. If the edges of G can be decomposed into some edge disjoint (g, f)-factors, then it is called that G is (g, f)-factorable. In this paper, one sufficient condition for a graph to be (g, f)-factorable is given.