Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of homophily and heterophily on preferred-degree networks: mean-field analysis and overwhelming transition

We investigate the long-time properties of a dynamic, out-of-equilibrium network of individuals holding one of two opinions in a population consisting of two communities of different sizes. Here, while the agents’ opinions are fixed, they have a preferred degree which leads them to endlessly create and delete links. Our evolving network is shaped by homophily/heterophily, a form of social interaction by which individuals tend to establish links with others having similar/dissimilar opinions. Using Monte Carlo simulations and a detailed mean-field analysis, we investigate how the sizes of the communities and the degree of homophily/heterophily affect the network structure. In particular, we show that when the network is subject to enough heterophily, an ‘overwhelming transition’ occurs: individuals of the smaller community are overwhelmed by links from the larger group, and their mean degree greatly exceeds the preferred degree. This and related phenomena are characterized by the network’s total and joint degree distributions, as well as the fraction of links across both communities and that of agents having fewer edges than the preferred degree. We use our mean-field theory to discuss the network’s polarization when the group sizes and level of homophily vary.
Large sample spectral analysis of graph-based multi-manifold clustering

In this work we study statistical properties of graph-based algorithms for multi-manifold clustering (MMC). In MMC the goal is to retrieve the multi-manifold structure underlying a given Euclidean data set when this one is assumed to be obtained by sampling a distribution on a union of manifolds $\mathcal{M} = \mathcal{M}_1 \cup\dots \cup \mathcal{M}_N$ that may intersect with each other and that may have different dimensions. We investigate sufficient conditions that similarity graphs on data sets must satisfy in order for their corresponding graph Laplacians to capture the right geometric information to solve the MMC problem. Precisely, we provide high probability error bounds for the spectral approximation of a tensorized Laplacian on $\mathcal{M}$ with a suitable graph Laplacian built from the observations; the recovered tensorized Laplacian contains all geometric information of all the individual underlying manifolds. We provide an example of a family of similarity graphs, which we call annular proximity graphs with angle constraints, satisfying these sufficient conditions. We contrast our family of graphs with other constructions in the literature based on the alignment of tangent planes. Extensive numerical experiments expand the insights that our theory provides on the MMC problem.
Abnormal Behavior Detection Based on Target Analysis

Abnormal behavior detection in surveillance video is a pivotal part of the intelligent city. Most existing methods only consider how to detect anomalies, with less considering to explain the reason of the anomalies. We investigate an orthogonal perspective based on the reason of these abnormal behaviors. To this end, we propose a multivariate fusion method that analyzes each target through three branches: object, action and motion. The object branch focuses on the appearance information, the motion branch focuses on the distribution of the motion features, and the action branch focuses on the action category of the target. The information that these branches focus on is different, and they can complement each other and jointly detect abnormal behavior. The final abnormal score can then be obtained by combining the abnormal scores of the three branches.
Fractional combinatorial Calabi flow on surfaces

Using the fractional discrete Laplace operator for triangle meshes, we introduce a fractional combinatorial Calabi flow for discrete conformal structures on surfaces, which unifies and generalizes Chow-Luo’s combinatorial Ricci flow for Thurston’s circle packings, Luo’s combinatorial Yamabe flow for vertex scaling and the combinatorial Calabi flow for discrete conformal structures on surfaces. For Thurston’s Euclidean and hyperbolic circle packings on triangulated surfaces, we prove the longtime existence and global convergence of the fractional combinatorial Calabi flow. For vertex scalings on polyhedral surfaces, we do surgery on the fractional combinatorial Calabi flow by edge flipping under the Delaunay condition to handle the potential singularities along the flow. Using the discrete conformal theory established in [21, 22], we prove the longtime existence and global convergence of the fractional combinatorial Calabi flow with surgery. MSC (2020): 52C26
ExpertRank: A Multi-level Coarse-grained Expert-based Listwise Ranking Loss

The goal of information retrieval is to recommend a list of document candidates that are most relevant to a given query. Listwise learning trains neural retrieval models by comparing various candidates simultaneously on a large scale, offering much more competitive performance than pairwise and pointwise schemes. Existing listwise ranking losses treat the candidate document list as a whole unit without further inspection. Some candidates with moderate semantic prominence may be ignored by the noisy similarity signals or overshadowed by a few especially pronounced candidates. As a result, existing ranking losses fail to exploit the full potential of neural retrieval models. To address these concerns, we apply the classic pooling technique to conduct multi-level coarse graining and propose ExpertRank, a novel expert-based listwise ranking loss. The proposed scheme has three major advantages: (1) ExpertRank introduces the profound physics concept of coarse graining to information retrieval by selecting prominent candidates at various local levels based on model prediction and inter-document comparison. (2) ExpertRank applies the mixture of experts (MoE) technique to combine different experts effectively by extending the traditional ListNet. (3) Compared to other existing listwise learning approaches, ExpertRank produces much more reliable and competitive performance for various neural retrieval models with different complexities, from traditional models, such as KNRM, ConvKNRM, MatchPyramid, to sophisticated BERT/ALBERT-based retrieval models.
Generalizing Fairness: Discovery and Mitigation of Unknown Sensitive Attributes

When deploying artiﬁcial intelligence (AI) in the real world, being able to trust the operation of the AI by characterizing how it performs is an ever-present and important topic. An important and still largely unexplored task in this characterization is determining major factors within the real world that affect the AI’s behavior, such as weather conditions or lighting, and either a) being able to give jus-tiﬁcation for why it may have failed or b) eliminating the inﬂuence the factor has. Determining these sensitive factors heavily relies on collected data that is diverse enough to cover numerous combinations of these factors, which becomes more onerous when having many potential sensitive factors or operating in complex environments. This paper investigates methods that discover and separate out individual semantic sensitive factors from a given dataset to conduct this characterization as well as addressing mitigation of these factors’ sensitivity. We also broaden remediation of fairness, which normally only addresses socially relevant factors, and widen it to deal with the desensitization of AI with regard to all possible aspects of variation in the domain. The proposed methods which discover these major factors reduce the potentially onerous demands of collecting a sufﬁciently diverse dataset. In experiments using the road sign (GTSRB) and facial imagery (CelebA) datasets, we show the promise of using this scheme to perform this characterization and remediation and demonstrate that our approach outperforms state of the art approaches.
Malware Classification Using Transfer Learning

With the rapid growth of the number of devices on the Internet, malware poses a threat not only to the affected devices but also their ability to use said devices to launch attacks on the Internet ecosystem. Rapid malware classification is an important tools to combat that threat. One of the successful approaches to classification is based on malware images and deep learning. While many deep learning architectures are very accurate they usually take a long time to train. In this work we perform experiments on multiple well known, pre-trained, deep network architectures in the context of transfer learning. We show that almost all them classify malware accurately with a very short training period.
Safest Nearby Neighbor Queries in Road Networks

Safety on the roads has become a major concern in recent days. Travellers prefer to avoid road inconveniences that may occur from crime incidents, street harassment, protests or riots during unrest in a country. To facilitate safe travel, we introduce a novel query for road networks called the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> safest nearby neighbor (SNN) query. Given a query location <inline-formula> <tex-math notation="LaTeX">$v_{l}$ </tex-math></inline-formula>, a distance constraint <inline-formula> <tex-math notation="LaTeX">$d_{c}$ </tex-math></inline-formula> and a point of interest <inline-formula> <tex-math notation="LaTeX">$p_{i}$ </tex-math></inline-formula>, we define the safest path from <inline-formula> <tex-math notation="LaTeX">$v_{l}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$p_{i}$ </tex-math></inline-formula> as the path with the highest <italic>path safety score</italic> among all the paths from <inline-formula> <tex-math notation="LaTeX">$v_{l}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$p_{i}$ </tex-math></inline-formula> with length less than <inline-formula> <tex-math notation="LaTeX">$d_{c}$ </tex-math></inline-formula>. The path safety score is computed considering the road safety of each road segment on the path. Given a query location <inline-formula> <tex-math notation="LaTeX">$v_{l}$ </tex-math></inline-formula>, a distance constraint <inline-formula> <tex-math notation="LaTeX">$d_{c}$ </tex-math></inline-formula> and a set of POIs <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>, a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>SNN query returns <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> POIs with the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> highest path safety scores in <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> along with their respective safest paths from the query location. We develop two novel indexing structures called <inline-formula> <tex-math notation="LaTeX">$Ct$ </tex-math></inline-formula>-tree and a safety score based Voronoi diagram (SNVD). We propose two efficient query processing algorithms each exploiting one of the proposed indexes to effectively refine the search space using the properties of the index. Our extensive experimental study on real datasets demonstrates that our solution is on average an order of magnitude faster than the baseline.
Digital Forensic Readiness Implementation in SDN: Issues and Challenges

The continued evolution in computer network technologies has seen the introduction of new paradigms like Software Defined Networking (SDN) which has altered many traditional networking principles in todays business environments. SDN has brought about unprecedented change to the way organisations plan, develop, and enact their networking technology and infrastructure strategies. However, SDN does not only offer new opportunities and abilities for organisations to redesign their entire network infrastructure but also presents a different set of issues and challenges that need to be resolved. One such challenge is the implementation of Digital Forensic Readiness (DFR) in SDN environments. This paper, therefore, examines existing literature and highlights the different issues and challenges impacting the implementation of DFR in SDN. However, the paper also goes further to offer insights on the different countermeasures that organisations can embrace to enhance their ability to respond to cybersecurity incidents as well as help them in implementing DFR in SDN environments
Stochastic billiards with Markovian reflections in generalized parabolic domains

We study recurrence and transience for a particle that moves at constant velocity in the interior of an unbounded planar domain, with random reflections at the boundary governed by a Markov kernel producing outgoing angles from incoming angles. Our domains have a single unbounded direction and sublinear growth. We characterize recurrence in terms of the reflection kernel and growth rate of the domain. The results are obtained by transforming the stochastic billiards model to a Markov chain on a half-strip $\mathbb{R}_+ \! \times S$ where $S$ is a compact set. We develop the recurrence classification for such processes in the near-critical regime in which drifts of the $\mathbb{R}_+$ component are of generalized Lamperti type, and the $S$ component is asymptotically Markov; this extends earlier work that dealt with finite $S$.
Naïve Bayes Machine Learning Classification with R Programming: A case study of binary data sets

- This analytical review paper clearly explains Naïve Bayes machine learning techniques for simple probabilistic classification based on bayes theorem with the assumption of independence between the characteristics using r programming. Although there is large gap between which algorithm is suitable for data analysis when there was large categorical variable to be predict the value in research data. The model is trained in the training data set to make predictions on the test data sets for the implementation of the Naïve Bayes classification. The uniqueness of the technique is that gets new information and tries to make a better forecast by considering the new evidence when the input variable is of largely categorical in nature that is quite similar to how our human mind works while selecting proper judgement from various alternative of choices and can be applied in the neuronal network of the human brain does using r programming. Here researcher takes binary.csv data sets of 400 observations of 4 dependent attributes of educational data sets. Admit is dependent variable of gre, score gpa and rank of previous grade which ultimately determine whether student will be admitted or not for next program. Initially the gra and gpa variables has 0.36 percent significant in the association with rank categorical variable. The box plot and density plot demonstrate the data overlap between admitted and not admitted data sets. The naïve Bayes classification model classify the large data with 0.68 percent for not admitted where as 0.31 percent were admitted. The confusion matrix, and the prediction were calculated with 0.68 percent accuracy when 95 percent confidence interval. Similarly, the training accuracy is increased from 29 percent to 32 percent when naïve Bayes algorithm method as use kernel is equal to TRUE that ultimately decrease misclassification errors in the binary data sets.
Crucial Role of Surfactants in Bubble-Propelled Microengines

Bubble-propelled autonomous microengines are expected to perform various tasks in related biomedical and environmental fields. The presence of surfactant in the environment is necessary for movement of microjets because it lowers interfacial tension required for detachment of the bubble from the microjet engine. We study the dependence of the mobility of microjets on the concentration of surfactants and show the different behaviors of microjets in the presence of anionic, cationic, and neutral surfactants. These findings have important implications on the motion of self-propelled microjet engines.
Trends in Sharia-compliant Financial Inclusion Box 1. What Makes a Financial Product Sharia-compliant?

The term " microfinance " as used in this Focus Note refers to the broad array of financial services targeting the poor (and not, as it is often used, to refer to the traditional microcredit sector). 2 Some service providers impose service or other fees often perceived as camouflaged interest. These practices raise questions about the Islamic authenticity of certain products and could present a barrier to customer usage. See, e.g., the discussion on murabaha. S haria-compliant financial inclusion represents the confluence of two rapidly growing sectors: microfinance 1 and Islamic finance. With an estimated 650 million Muslims living on less than $2 a day (Obaidullah and Tariqullay 2008), finding sustainable Islamic models could be the key to providing financial access to millions of Muslim poor who strive to avoid financial products that do not comply with Sharia (Islamic law). Consequently, Sharia-compliant financial inclusion has recently galvanized considerable interest among regulators, financial service providers, and other financial inclusion stakeholders. However, despite a four-fold increase in recent years in the number of poor clients using Sharia-compliant products (estimated at 1.28 million) and a doubling in the number of providers, the nascent sector continues to struggle to find sustainable business models with a broad array of products that can meet the diverse financial needs of religiously observant poor Muslims. Financial tenets enshrined in Sharia challenge the microfinance sector's ability to sustainably provide Sharia-compliant financial products at scale. One such tenet is the widely known prohibition on interest, which makes traditional microloan models technically impossible. 2 A lesser known tenet is the encouragement of wealth creation through equity participation in business activities, which requires risk-sharing by financial service providers that does not guarantee returns. CGAP's 2008 Focus Note on Islamic microfinance explored the state of the sector then and identified key obstacles to its growth (Karim, Tarazi, and Reille 2008). CGAP, in collaboration with the French development agency Agence Française de Développement, conducted a survey in 2011 to better understand the current landscape of Islamic microfinance supply. Based on this survey, the results of which are highlighted herein, we conclude that despite impressive increases in the number of Islamic microfinance providers and clients, the sector is still largely dominated by a few providers in a few countries that rely primarily on only two products. The Islamic microfinance sector needs a concerted, evidence-based effort Islamic finance refers to a system of finance based on …
Beating the Odds – NBA Analytics

In this paper, we attempt to improve traditional models of NBA game predictions by augmenting box score data with player-speciﬁc RAPTOR data. This project employs two models. First, a novel log-two layer, fully connected neural network and secondly, a transformer model with self-attention, which treats each player as a vector of their statistics in order to learn relationships between players. We ﬁnd that all three models perform similarly, with an average smoothed L1 loss of 10.10 in testing. We introduce a measure of the amount of conﬁdence required in our model in order to bet against the bookies
TEPP Working Papers 2012 12-1 . What drives Health Care Expenditure in France since 1950 ? A time-series study with structural breaks and non-linearity approaches

Performance appraisals have become a widespread practice in OECD member countries. However, whereas the problem of constructing an optimal contract with subjective evaluation receives a lot of attention, firmlevel performance appraisals are strikingly left outside of economic theory. The purpose of this paper is threefold: first, to theoretically define what performance appraisals are; second, to analyze the effects of incentive contracts on effort and wage using performance appraisals; and third, to theoretically quantify the selection effects driven by the implementation of performance appraisals.
Sol-Gel Glass-Encapsulated Crown Ethers for the Separation and Preconcentration of Strontium from Acidic Media

Extraction chromatography employing an inert polymeric support impregnated with a crown ether (typically, 4,4’,(5’)-di-(tert-butylcyclohexano)-18-crown-6 (DtBuCH18C6)), either neat (i.e., undiluted) or as a solution in 1-octanol, has previously been shown to provide an effective means for the isolation of radiostrontium from a variety of sample types for subsequent determination. In this study, sol-gel chemistry has been employed to prepare sorbents in which DtBuCH18C6 is encapsulated in a silica matrix. The resultant materials have been evaluated for their ability to retain strontium ion and compared to a commercially available extraction chromatographic (EXC) resin. Certain of the new materials are shown to provide uptake efficiencies comparable to those obtained with the commercial resins, although unless a porogen is employed, the kinetics of strontium uptake are significantly slower. In contrast to conventional EXC materials, however, strontium uptake by DtBuCH18C6-loaded glasses does not increase in proportion to the amount of extractant present, indicating that not all of the crown ether present is available for interaction with the metal ion.
Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

A quarter of all cancer deaths are due to lung cancer. Studies show that early diagnosis and treatment of this disease are the most effective way to increase patient life expectancy. In this paper, automatic and optimized computer-aided detection is proposed for lung cancer. The method first applies a preprocessing step for normalizing and denoising the input images. Afterward, Kapur entropy maximization is performed along with mathematical morphology to lung area segmentation. Afterward, 19 GLCM features are extracted from the segmented images for the final evaluations. The higher priority images are then selected for decreasing the system complexity. The feature selection is based on a new optimization design, called Improved Thermal Exchange Optimization (ITEO), which is designed to improve the accuracy and convergence abilities. The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO. Simulation is compared with some well-known approaches and the results showed the superiority of the suggested method. The results showed that the proposed method with 92.27% accuracy provides the highest value among the compared methods.
Protocatechualdehyde Inhibits the Osteoclast Differentiation of RAW264.7 and BMM Cells by Regulating NF-κB and MAPK Activity

Protocatechualdehyde (PCA), an important component of Salvia miltiorrhiza, has many activities, such as anti-inflammatory and antisepsis activities. However, the role of PCA in osteoclasts is not clear. We used RAW264.7 cells (a mouse leukemic monocyte/macrophage cell line) and bone marrow macrophages (BMMs) to probe the role of PCA in osteoclasts and the underlying mechanism. The effects of PCA on cell activity were evaluated with CCK-8 assays. TRAP staining detected mature osteoclasts. Corning Osteo Assay Surface plates were used to examine absorption. The levels of RNA and protein were analyzed, respectively, using RT-PCR and Western blotting. PCA (5 μg/ml) was not toxic to the two cell types but reduced the formation of osteoclasts and bone absorption. Furthermore, PCA restrained the expression of mRNAs encoding proteins associated with osteoclasts and reduced the phosphorylation of proteins in important signaling pathways. The results indicate that PCA inhibits osteoclast differentiation by suppressing NF-κB and MAPK activity.