Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Network Membership Estimation Under Severe Degree Heterogeneity

Real networks often have severe degree heterogeneity. We are interested in studying the effect of degree heterogeneity on estimation of the underlying community structure. We consider the degree-corrected mixed membership model (DCMM) for a symmetric network with n nodes and K communities, where each node i has a degree parameter θi and a mixed membership vector πi. The level of degree heterogeneity is captured by Fn(·) – the empirical distribution associated with n (scaled) degree parameters. We first show that the optimal rate of convergence for the `1-loss of estimating πi’s depends on an integral with respect to Fn(·). We call a method optimally adaptive to degree heterogeneity (in short, optimally adaptive) if it attains the optimal rate for arbitrary Fn(·). Unfortunately, none of the existing methods satisfy this requirement. We propose a new spectral method that is optimally adaptive, the core idea behind which is using a pre-PCA normalization to yield the optimal signal-to-noise ratio simultaneously at all entries of each leading empirical eigenvector. As one technical contribution, we derive a new row-wise large-deviation bound for eigenvectors of the regularized graph Laplacian.
Exploring dust growth in the episodic WCd system WR140

The wind collision region (WCR) in a colliding wind binary (CWB) is a particularly violent place, as such, it is surprising that it is also a region where significant quantities of interstellar dust can form. In extreme cases, approximately 30 per cent of the total mass loss rate of a system can be converted into dust. These regions are poorly understood, as observation and simulation of these systems are difficult. In our previous paper we simulated dust growth in CWB systems using an advected scalar model and found our model to be suitable for qualitative study. For this paper we simulated the periodic dust forming CWB (WCd) system WR140 with our dust model, to determine how dust growth changes over the systems periastron passage. We found that dust production increases significantly at periastron passage, which is consistent with IR emission of the surrounding dusty shell. We also find that the dust production rate of the system decreases rapidly as the stars recede from each other, though the rate of decrease is significantly lower than the rate of increase during periastron passage. This was found to be due to strong cooling and its associated thermal instabilities, resulting in cool, high-density pockets of gas in the WCR where dust forms. The WCR also shows a degree of hysteresis, resulting in a radiative post-shock flow even when the stars are separated enough for the region to behave adiabatically.
Origin of primeval seed magnetism in spinning astrophysical bodies

We show that a primeval seed magnetic ﬁeld arises as a natural consequence of spin-degeneracy breaking of fermions caused by the dragging of inertial frames in the curved spacetime of spinning astrophysical bodies. This seed magnetism would arise even due to electrically neutral fermions such as neutrons. As an example, we show that an ideal neutron star spinning at 500 revolutions per second, having mass 0 . 83 M (cid:12) and described by an ensemble of degenerate neutrons would have 0 . 12 Gauss seed magnetic ﬁeld at its center arising through the breaking of spin-degeneracy.
EXPLICIT MINIMAL POSITIVE SOLUTION TO LINEAR COEFFICIENT NON-HOMOGENEOUS SECOND ORDER RECURRENCE RELATION WITH CONSTANT NON-HOMOGENITY.

Second order recurrence relations of real numbers arise form various applica-tions in discrete time dynamical systems as well as in the context on Markov chains. Solutions to the recurrence relations are fully deﬁned by the ﬁrst two initial values. In particular, in the context of Markov chains, the notion of minimal positive solutions arises, when ﬁxing only the ﬁrst initial value to be 0 and leaving the second one unknown. We calculate in this work the minimal positive solution ( a i ) i ∈ N to non-homogeneous second order recurrence relation with aﬃne coeﬃcients when the non-homogeneity is constant and negative, and the ﬁrst initial value equals a 0 = 0. We obtain an explicit expression of the initial value a 1 which leads to the minimal positive solution as well as the minimal positive solution ( a i ) i ∈ N itself. Additionally, we prove that this sequence converges to 0 and obtain the convergence speed in O ( i − 1 ). We show in the last section that a choice of rational parameters in the recurrence relation imply that all a i are transcendental numbers and we make a link to the impossibility of obtaining computer based visualizations of the minimal positive solution ( a i ) i ∈ N .
Penrose inequality in asymptotically anti-de Sitter spacetime

: Considering static asymptotically Schwarzschild-AdS black holes, we conjecture that the null energy condition can guarantee the Penrose inequality when the cross-section of horizon has planar or spherical topology, but the weak energy condition can even guarantee the inequality for hyperbolic topology. We prove this conjecture under planar/spherically/hyperbolically symmetric cases. We also propose two candidates of generalized Penrose inequality in charged cases. If black holes are asymptotically AdS but not asymptotically Schwarzschild-AdS, the total mass is deﬁned according to holographic renormalization and may not be determined completely by bulk geometry. We construct an asymptotically AdS black hole coupled to a scalar ﬁeld to check the Penrose inequality in such case. It is found that whether the Penrose inequality holds or not will depend on what kind of quantization scheme we employ.
A Novel Scalable Apache Spark Based Feature Extraction Approaches for Huge Protein Sequence and their Clustering Performance Analysis

Genome sequencing projects are rapidly increasing the number of high-dimensional protein sequence datasets. Clustering a high-dimensional protein sequence dataset using traditional machine learning approaches poses many challenges. Many different feature extraction methods exist and are widely used. However, extracting features from millions of protein sequences becomes impractical because they are not scalable with current algorithms. Therefore, there is a need for an efficient feature extraction approach that extracts significant features. We have proposed two scalable feature extraction approaches for extracting features from huge protein sequences using Apache Spark, which are termed 60d-SPF (60-dimensional Scalable Protein Feature) and 6d-SCPSF (6-dimensional Scalable Co-occurrence-based Probability-Specific Feature). The proposed 60d-SPF and 6d-SCPSF approaches capture the statistical properties of amino acids to create a fixed-length numeric feature vector that represents each protein sequence in terms of 60-dimensional and 6-dimensional features, respectively. The preprocessed huge protein sequences are used as an input in two clustering algorithms, i.e., Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (SRSIO-FCM) and Scalable Literal Fuzzy C-Means (SLFCM) for clustering. We have conducted extensive experiments on various soybean protein datasets to demonstrate the effectiveness of the proposed feature extraction methods, 60d-SPF, 6d-SCPSF, and existing feature extraction methods on SRSIO-FCM and SLFCM clustering algorithms. The reported results in terms of the Silhouette index and the Davies-Bouldin index show that the proposed 60d-SPF extraction method on SRSIO-FCM and SLFCM clustering algorithms achieves significantly better results than the proposed 6d-SCPSF and existing feature extraction approaches.
The impact of radio signal strength on the design of wireless sensor networks

Radio signal strength (RSS) is an attractive property since it can be obtained without additional hardware. Based on various signal propagation models that describe the relationship between RSS and distance, RSS can be employed in network connectivity, localization, and link quality issues in wireless sensor networks (WSNs). However, the deterministic propagation models, in which RSS is only determined by distance, are far from reality. We carefully design a series of experiments using MICA2 nodes in real environments to investigate the parameters besides distance—frequency, variation of transceivers, antenna orientation, battery voltage, temporal-spatial properties of environment, and environmental dynamics. All the parameters contribute to RSS irregularity, which can highly affect the design of localization and topology control algorithms in wireless sensor networks. We derive the log-normal error model for RSS-based distance estimation from the underlying propagation models and find out that the popular noisy disk error model always underestimates distance error in localization. A quality-based localization algorithm is proposed to handle the large error in RSS-based distance estimation. In WSNs, topology control achieves energy-efficiency by turning off redundant nodes, while still satisfying the given network requirement. We first propose several connectivity-based topology control algorithms, which assume that links are either connected or disconnected. As our experiments show that, besides the connected and disconnected region, a large number of links reside in the transitional region with fluctuating link quality. We then propose a link-quality-based topology control algorithm, which employ opportunistic transmission to catch the best transmission opportunities on transitional links. Our simulations demonstrate that opportunistic transmission can significantly improve energy-efficiency in topology control with low communication overhead. To the best of our knowledge, we are the first to consider link quality and apply opportunistic communication in topology control for WSNs.
Source-independent quantum random number generator against detector blinding attacks

Randomness, mainly in the form of random numbers, is the fundamental prerequisite for the security of many cryptographic tasks. Quantum randomness can be extracted even if adversaries are fully aware of the protocol and even control the randomness source. However, an adversary can further manipulate the randomness via detector blinding attacks, which are hacking attacks suﬀered by protocols with trusted detectors. Here, by treating no-click events as valid error events, we propose a quantum random number generation protocol that can simultaneously address source vulnerability and ferocious detector blinding attacks. The method can be extended to high-dimensional random number generation. We experimentally demonstrate the ability of our protocol to generate random numbers for two-dimensional measurement with a generation speed of 0.515 Mbps, which is two orders of magnitude higher than that of device-independent protocols that can address both issues of imperfect sources and imperfect detectors.
Reliability and long term stability of MEMS

Particular attention to MEMS failure modes is important because failure modes that are negligible at a macroscopic scale become significant on the microscale. Potential modes of failure for MEMS structures include static overload, delamination, creep, and fatigue. We have been examining long term stability through the design of MEMS for accelerated testing. These include cantilever and membrane structures for both static and cyclic loading and stiction. We have identified a potential failure mode in silicon devices that was not previously known. This failure mechanism results from the effect of water on the initiation and propagation of cracks in both single and polysilicon MEMS.
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language

More than 43% of the languages spoken in the world are endangered, and language loss currently occurs at an accelerated rate because of globalization and neocolonialism. Saving and revitalizing endangered languages has become very important for maintaining the cultural diversity on our planet. In this work, we focus on discussing how NLP can help revitalize endangered languages. We first suggest three principles that may help NLP practitioners to foster mutual understanding and collaboration with language communities, and we discuss three ways in which NLP can potentially assist in language education. We then take Cherokee, a severely-endangered Native American language, as a case study. After reviewing the language’s history, linguistic features, and existing resources, we (in collaboration with Cherokee community members) arrive at a few meaningful ways NLP practitioners can collaborate with community partners. We suggest two approaches to enrich the Cherokee language’s resources with machine-in-the-loop processing, and discuss several NLP tools that people from the Cherokee community have shown interest in. We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general.
Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique

: Background: Suggesting similar questions for a user query has many applications ranging from reducing search time of users on e-commerce websites, training of employees in companies to holistic learning for students. The use of Natural Language Processing techniques for suggesting similar questions is prevalent over the existing architecture. Mainly two approaches are studied for finding text similarity namely syntactic and semantic, however each has its draw-backs and fail to provide the desired outcome. In this article, a self-learning combined approach is proposed for determining textual similarity that introduces a robust weighted syntactic and semantic similarity index for determining similar questions from a predetermined database, this approach learns the optimal combination of the mentioned approaches for a database under consideration. Comprehensive analysis has been carried out to justify the efficiency and efficacy of the proposed approach over the existing literature. Experimental results on 50K+ queries suggest that proposed novel method of combining syntactic and semantic systems is to similar queries as well as syntactic
Robust Dual-Graph Regularized Moving Object Detection

Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be naturally decomposed into a low-rank background and a sparse foreground. Many regularization techniques, such as matrix nuclear norm, can therefore be imposed on the background. In the meanwhile, sparsity or smoothness based regularizations, such as total variation and $\ell_{1}$, can be imposed on the foreground. Moreover, graph Laplacians are further used to capture the complicated geometry of background images. Recently, weighted regularization techniques including the weighted nuclear norm regularization have been proposed in the image processing community to promote adaptive sparsity while achieving efficient performance. In this paper, we propose a robust dual-graph regularized moving object detection model based on a new weighted nuclear norm regularization and spatiotemporal graph Laplacians, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic body movement data sets have demonstrated the effectiveness of this method in separating moving objects from background, and the great potential in robotic applications.
A dynamic that evolves toward a Nash equilibrium

In this paper, we study an exponentiated multiplicative weights dynamic based on Hedge, a well-known algorithm in theoretical machine learning and algorithmic game theory. The empirical average (arithmetic mean) of the iterates Hedge generates is known to approach a minimax equilibrium in zero-sum games. We generalize that result to show that a weighted version of the empirical average converges to an equilibrium in the class of symmetric bimatrix games for a diminishing learning rate parameter. Our dynamic is the first dynamical system (whether continuous or discrete) shown to evolve toward a Nash equilibrium without assuming monotonicity of the payoff structure or that a potential function exists. Although our setting is somewhat restricted, it is also general as the class of symmetric bimatrix games captures the entire computational complexity of the PPAD class (even to approximate an equilibrium).
Meta-AF: Meta-Learning for Adaptive Filters

Adaptive filtering algorithms are pervasive throughout signal processing and have had a material impact on a wide variety of domains including audio processing, telecommunications, biomedical sensing, astrophysics and cosmology, seismology, and many more. Adaptive filters typically operate via specialized online, iterative optimization methods such as least-mean squares or recursive least squares and aim to process signals in unknown or nonstationary environments. Such algorithms, however, can be slow and laborious to develop, require domain expertise to create, and necessitate mathematical insight for improvement. In this work, we seek to improve upon hand-derived adaptive filter algorithms and present a comprehensive framework for learning online, adaptive signal processing algorithms or update rules directly from data. To do so, we frame the development of adaptive filters as a meta-learning problem in the context of deep learning and use a form of self-supervision to learn online iterative update rules for adaptive filters. To demonstrate our approach, we focus on audio applications and systematically develop meta-learned adaptive filters for five canonical audio problems including system identification, acoustic echo cancellation, blind equalization, multi-channel dereverberation, and beamforming.We compare our approach against common baselines and/or recent state-of-the-art methods. We show we can learn high-performing adaptive filters that operate in real-time and, in most cases, significantly outperform each method we compare against – all using a single general-purpose configuration of our approach.
On limitations of uniplex networks for modeling multiplex contagion

Many network contagion processes are inherently multiplex in nature, yet are often reduced to processes on uniplex networks in analytic practice. We therefore examine how data modeling choices can affect the predictions of contagion processes. We demonstrate that multiplex contagion processes are not simply the union of contagion processes over their constituent uniplex networks. We use multiplex network data from two different contexts—(1) a behavioral network to represent their potential for infectious disease transmission using a “simple” epidemiological model, and (2) users from online social network sites to represent their potential for information spread using a threshold-based “complex” contagion process. Our results show that contagion on multiplex data is not captured accurately in models developed from the uniplex networks even when they are combined, and that the nature of the differences between the (combined) uniplex and multiplex results depends on the specific spreading process over these networks.
Signed spectral Tura\'{n} type theorems

A signed graph $\Sigma = (G, \sigma)$ is a graph where the function $\sigma$ assigns either $1$ or $-1$ to each edge of the simple graph $G$. The adjacency matrix of $\Sigma$, denoted by $A(\Sigma)$, is defined canonically. In a recent paper, Wang et al. extended the eigenvalue bounds of Hoffman and Cvetkovi\'{c} for the signed graphs. They proposed an open problem related to the balanced clique number and the largest eigenvalue of a signed graph. We solve a strengthened version of this open problem. As a byproduct, we give alternate proofs for some of the known classical bounds for the least eigenvalues of the unsigned graphs. We extend the Tur\'{a}n's inequality for the signed graphs. Besides, we study the Bollob\'{a}s and Nikiforov conjecture for the signed graphs and show that the conjecture need not be true for the signed graphs. Nevertheless, the conjecture holds for signed graphs under some assumptions. Finally, we study some of the relationships between the number of signed walks and the largest eigenvalue of a signed graph.
A Two Way Group Similarity Analysis Based Hybrid Recommendation System under Temporal Vector

This Review paper describe about recommender system based on hybrid recommendation approach using temporal vector. The recommender system is some kind of predictive system that actually analyzes the users or the customer behavior and based on this analysis; recommend some product or service to the user. The recommender system is also defined as the approach that guide the user in a personalized way so that he can select the most useful item from the large set of available alternatives. In this work, the recommendation will be applied on user side as well as movie side. Hybrid recommendation system will unite the functionality of content based as well as collaborative recommender system. At the later stage, a weighted approach will be defined on content based and collaborative methods to perform the recommendation. This two level hybrid approach will give more precise results.
REGIONAL CRIME ANALYSIS OF TURKEY: A TIME SERIES APPROACH

Bu calismada, Turkiye'nin 7 farkli cografi bolgesindeki en yaygin suc turleri zaman serisi yaklasimi ile analiz edilmistir. Calismanin amaci; her cografi bolge icin en uygun zaman serisi modelini ortaya koymak ve suc oranlari icin tahminler yapmaktir. Analizde; 2001-2010 yillarini kapsayan resmi kaynaklardan elde edilen suc verileri kullanilmistir. 7 bolge verisinin oncelikli olarak duraganliklari test edilerek, her bolge icin en uygun zaman serisi modeli tespit edilmistir. Elde edilen zaman serisi modellerinin gecerliligi test edilmis ve modellerin verdigi sonuclar 2011 yili resmi verileriyle karsilastirilmistir. Zaman serisi modellerinden elde edilen tahminlerin sonuclarinin onemi ve etkinligi ortaya konulmustur. Zaman serileri yaklasiminin suclarin ongorulebilirliginde ve guvenlik planlamalarinda kullanilabilirligi gosterilmistir
Significance of the Number of Intermediate-Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy: A Retrospective Study of 976 Patients

Abstract Background and objectives This study aimed to investigate the impact of intermediate-risk factors (IRFs) on the prognosis of stage I-II cervical cancer (CC) patients, and evaluate the necessity of adjuvant treatment based on investigation. Methods Medical records of 976 negative high-risk factors’ CC patients were retrospectively reviewed. Clinicopathologic characteristics and adjuctive therapy were analyzed using Kaplan–Meier analysis and log-rank tests to identify significant factors. The multivariate Cox proportional hazards regression analysis was performed to identify the independent prognostic factors. Results For patients with none, single and multiple IRFs, the 3-year recurrence-free survival rates were 97.8%, 86.3%, and 68.0% respectively (p < 0.001), and 3-year overall survival rates were 99.3%, 93.6% and 79.0% respectively (p < 0.001). Multivariate analysis showed histological type, differentiation grade, the number of IRFs and adjuvant therapy were independent prognostic factors. Conclusions The number of IRFs was demonstrated with higher predictive efficacy on survival of CC than individual IRF. Patients with multiple IRFs had significantly worse survival outcomes than patients with none or one. Different adjuvant treatment plans should be formulated based on the number of present IRFs. The prognostic management of patients with multiple IRFs should be pay more attention.