Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed-Order Spectral Clustering for Networks

Clustering is fundamental for gaining insights from complex networks, and spectral clustering (SC) is a popular approach. Conventional SC focuses on second-order structures (e.g., edges connecting two nodes) without direct consideration of higher-order structures (e.g., triangles and cliques). This has motivated SC extensions that directly consider higher-order structures. However, both approaches are limited to considering a single order. This paper proposes a new Mixed-Order Spectral Clustering (MOSC) approach to model both second-order and third-order structures simultaneously, with two MOSC methods developed based on Graph Laplacian (GL) and Random Walks (RW). MOSC-GL combines edge and triangle adjacency matrices, with theoretical performance guarantee. MOSC-RW combines first-order and second-order random walks for a probabilistic interpretation. We automatically determine the mixing parameter based on cut criteria or triangle density, and construct new structure-aware error metrics for performance evaluation. Experiments on real-world networks show 1) the superior performance of two MOSC methods over existing SC methods, 2) the effectiveness of the mixing parameter determination strategy, and 3) insights offered by the structure-aware error metrics.
Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit

We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the state-of-the-art dynamic regret, but does not require extra gradient queries for every iteration. Specifically, when functions are $\alpha$ strongly convex and $\beta$ smooth, to achieve the state-of-the-art dynamic regret, the previous work requires $O(\kappa)$ with $\kappa = \frac{\beta}{\alpha}$ queries of gradients at every iteration. But, our framework shows that the query complexity can be improved to be $O(1)$, which does not depend on $\kappa$. The improvement is significant for ill-conditioned problems because that their objective function usually has a large $\kappa$.
Deep learning electromagnetic inversion with convolutional neural networks

Geophysical inversion attempts to estimate the distribution of physical properties in the Earth's interior from observations collected at or above the surface. Inverse problems are commonly posed as least-squares optimization problems in high-dimensional parameter spaces. Existing approaches are largely based on deterministic gradient-based methods, which are limited by nonlinearity and nonuniqueness of the inverse problem. Probabilistic inversion methods, despite their great potential in uncertainty quantification, still remain a formidable computational task. In this paper, I explore the potential of deep learning methods for electromagnetic inversion. This approach does not require calculation of the gradient and provides results instantaneously. Deep neural networks based on fully convolutional architecture are trained on large synthetic datasets obtained by full 3-D simulations. The performance of the method is demonstrated on models of strong practical relevance representing an onshore controlled source electromagnetic CO2 monitoring scenario. The pre-trained networks can reliably estimate the position and lateral dimensions of the anomalies, as well as their resistivity properties. Several fully convolutional network architectures are compared in terms of their accuracy, generalization, and cost of training. Examples with different survey geometry and noise levels confirm the feasibility of the deep learning inversion, opening the possibility to estimate the subsurface resistivity distribution in real time.
bigMap: Big Data Mapping with Parallelized t-SNE

We introduce an improved unsupervised clustering protocol specially suited for large-scale structured data. The protocol follows three steps: a dimensionality reduction of the data, a density estimation over the low dimensional representation of the data, and a final segmentation of the density landscape. For the dimensionality reduction step we introduce a parallelized implementation of the well-known t-Stochastic Neighbouring Embedding (t-SNE) algorithm that significantly alleviates some inherent limitations, while improving its suitability for large datasets. We also introduce a new adaptive Kernel Density Estimation particularly coupled with the t-SNE framework in order to get accurate density estimates out of the embedded data, and a variant of the rainfalling watershed algorithm to identify clusters within the density landscape. The whole mapping protocol is wrapped in the bigMap R package, together with visualization and analysis tools to ease the qualitative and quantitative assessment of the clustering.
Practical Adversarial Attack Against Object Detector

In this paper, we proposed the first practical adversarial attacks against object detectors in realistic situations: the adversarial examples are placed in different angles and distances, especially in the long distance (over 20m) and wide angles 120 degree. To improve the robustness of adversarial examples, we proposed the nested adversarial examples and introduced the image transformation techniques. Transformation methods aim to simulate the variance factors such as distances, angles, illuminations, etc., in the physical world. Two kinds of attacks were implemented on YOLO V3, a state-of-the-art real-time object detector: hiding attack that fools the detector unable to recognize the object, and appearing attack that fools the detector to recognize the non-existent object. The adversarial examples are evaluated in three environments: indoor lab, outdoor environment, and the real road, and demonstrated to achieve the success rate up to 92.4% based on the distance range from 1m to 25m. In particular, the real road testing of hiding attack on a straight road and a crossing road produced the success rate of 75% and 64% respectively, and the appearing attack obtained the success rates of 63% and 81% respectively, which we believe, should catch the attention of the autonomous driving community.
Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms

Recent methods for learning a linear subspace from data corrupted by outliers are based on convex $\ell_1$ and nuclear norm optimization and require the dimension of the subspace and the number of outliers to be sufficiently small. In sharp contrast, the recently proposed Dual Principal Component Pursuit (DPCP) method can provably handle subspaces of high dimension by solving a non-convex $\ell_1$ optimization problem on the sphere. However, its geometric analysis is based on quantities that are difficult to interpret and are not amenable to statistical analysis. In this paper we provide a refined geometric analysis and a new statistical analysis that show that DPCP can tolerate as many outliers as the square of the number of inliers, thus improving upon other provably correct robust PCA methods. We also propose a scalable Projected Sub-Gradient Method method (DPCP-PSGM) for solving the DPCP problem and show it admits linear convergence even though the underlying optimization problem is non-convex and non-smooth. Experiments on road plane detection from 3D point cloud data demonstrate that DPCP-PSGM can be more efficient than the traditional RANSAC algorithm, which is one of the most popular methods for such computer vision applications.
Reversible Data Hiding in Encrypted Images Based on Multi-MSB Prediction and Huffman Coding

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention as a technology that can: embed additional data in the image encryption domain, ensure that the embedded data can be extracted error-free, and the original image can be restored losslessly. In this paper, a high-capacity RDHEI algorithm based on multi-MSB (most significant bit) prediction and Huffman coding is proposed. At first, multi-MSB of each pixel was predicted adaptively and marked by Huffman coding in the original image. Then, the image was encrypted by a stream cipher method. At last, the vacated space can be used to embed additional data by multi-MSB substitution. Experimental results show that our method achieved higher embedding capacity while comparing with the state-of-the-art methods.
Near-Optimal O(k)-Robust Geometric Spanners

For any constants $d\ge 1$, $\epsilon >0$, $t>1$, and any $n$-point set $P\subset\mathbb{R}^d$, we show that there is a geometric graph $G=(P,E)$ having $O(n\log^2 n\log\log n)$ edges with the following property: For any $F\subseteq P$, there exists $F^+\supseteq F$, $|F^+| \le (1+\epsilon)|F|$ such that, for any pair $p,q\in P\setminus F^+$, the graph $G-F$ contains a path from $p$ to $q$ whose (Euclidean) length is at most $t$ times the Euclidean distance between $p$ and $q$. In the terminology of robust spanners (Bose \et al, SICOMP, 42(4):1720--1736, 2013) the graph $G$ is a $(1+\epsilon)k$-robust $t$-spanner of $P$. This construction is sparser than the recent constructions of Buchin, Olah, and Har-Peled (arXiv:1811.06898) who prove the existence of $(1+\epsilon)k$-robust $t$-spanners with $n\log^{O(d)} n$ edges.
An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams

The writing style of a person can be affirmed as a unique identity indicator; the words used, and the structuring of the sentences are clear measures which can identify the author of a specific work. Stylometry and its subset - Authorship Attribution, have a long history beginning from the 19th century, and we can still find their use in modern times. The emergence of the Internet has shifted the application of attribution studies towards non-standard texts that are comparatively shorter to and different from the long texts on which most research has been done. The aim of this paper focuses on the study of short online texts, retrieved from messaging application called WhatsApp and studying the distinctive features of a macaronic language (Hinglish), using supervised learning methods and then comparing the models. Various features such as word n-gram and character n-gram are compared via methods viz., Naive Bayes Classifier, Support Vector Machine, Conditional Tree, and Random Forest, to find the best discriminator for such corpora. Our results showed that SVM attained a test accuracy of up to 95.079% while similarly, Naive Bayes attained an accuracy of up to 94.455% for the dataset. Conditional Tree & Random Forest failed to perform as well as expected. We also found that word unigram and character 3-grams features were more likely to distinguish authors accurately than other features.
Wibson: A Decentralized Data Marketplace

Our aim is for Wibson to be a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell information in a trusted environment. The combination of the Wibson token and blockchain-enabled smart contracts hopes to allow Data Sellers and Data Buyers to transact with each other directly while providing individuals the ability to maintain anonymity as desired. Wibson intends that its data marketplace will provide infrastructure and financial incentives for individuals to securely sell personal information without sacrificing personal privacy. Data Buyers receive information from willing and actively participating individuals with the benefit of knowing that the personal information should be accurate and current.
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification

Video-based person re-identification is a crucial task of matching video sequences of a person across multiple camera views. Generally, features directly extracted from a single frame suffer from occlusion, blur, illumination and posture changes. This leads to false activation or missing activation in some regions, which corrupts the appearance and motion representation. How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem. To this end, we propose a Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses noisy parts of the current frame’s features by referring historical frames. With RRU, the quality of each frame’s appearance representation is improved. Then we use the Spatial-Temporal clues Integration Module (STIM) to mine the spatial-temporal information from those upgraded features. Meanwhile, the multilevel training objective is used to enhance the capability of RRU and STIM. Through the cooperation of those modules, the spatial and temporal features mutually promote each other and the final spatial-temporal feature representation is more discriminative and robust. Extensive experiments are conducted on three challenging datasets, i.e., iLIDS-VID, PRID-2011 and MARS. The experimental results demonstrate that our approach outperforms existing state-of-the-art methods of video-based person re-identification on iLIDS-VID and MARS and achieves favorable results on PRID-2011.
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

Abstract We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared byte-pair encoding vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI data set), cross-lingual document classification (MLDoc data set), and parallel corpus mining (BUCC data set) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low- resource languages. Our implementation, the pre-trained encoder, and the multilingual test set are available at https://github.com/facebookresearch/LASER.
A Review on The Use of Deep Learning in Android Malware Detection

Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom. Researchers persistently devise countermeasures strategies to fight back malware. One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection. This necessitates a review to inspect the accomplished work in order to know where the endeavors have been established, identify unresolved problems, and motivate future research directions. In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions, and limitations.
Medicaid Expenditures for Cancer: Evidence from Medicaid-only Beneficiaries in Four States

This study estimates the cost burden of 6 prevalent invasive cancers—breast, cervical, colorectal, lung, melanoma, and prostate—on Medicaid programs in 4 states. The analyses use Medicaid claims and enrollment data for all Medicaid-only beneficiaries over age 18 in Georgia, Illinois, Louisiana, and Maine with at least 1 month of enrollment in fee-for-service Medicaid from 2000 to 2003. We applied ordinary least squares regression analysis to a data set created from Medicaid claims and enrollment data to estimate annual expenditures attributable to each cancer after controlling for age, race, gender, and comorbid conditions. Cancers and comorbid conditions were identified on the basis of claims with an appropriate diagnosis code. Cancers include both incident and prevalent cases. In 2003 dollars, annualized Medicaid expenditures attributable to the 6 cancers combined in the Medicaid-only population were $84.0 million in Georgia, $79.7 million in Illinois, $51.4 million in Louisiana, and $29.4 million in Maine. Attributable annualized per-capita Medicaid expenditures were highest for lung cancer, then colorectal cancer. After adjusting for sociodemographics and comorbidities, only 10% to 50% of medical expenditures among Medicaid-only beneficiaries with cancer were attributable to cancer. Estimates of the costs of care for Medicaid-eligible cancer patients are critical to understanding the implications of cancer for state and federal budgets. The Patient Protection and Affordable Care Act (ACA) of 2010 is expected to substantially expand the adult Medicaid population. These estimates provide important baseline information for assessing the potential effects of increased Medicaid enrollment on Medicaid expenditures for cancer.
A Direct Link between Rényi–Tsallis Entropy and Hölder’s Inequality—Yet Another Proof of Rényi–Tsallis Entropy Maximization

The well-known Hölder’s inequality has been recently utilized as an essential tool for solving several optimization problems. However, such an essential role of Hölder’s inequality does not seem to have been reported in the context of generalized entropy, including Rényi–Tsallis entropy. Here, we identify a direct link between Rényi–Tsallis entropy and Hölder’s inequality. Specifically, we demonstrate yet another elegant proof of the Rényi–Tsallis entropy maximization problem. Especially for the Tsallis entropy maximization problem, only with the equality condition of Hölder’s inequality is the q-Gaussian distribution uniquely specified and also proved to be optimal.
Uso de la microscopia confocal de reflectancia en dermatología

Resumen Normal 0 21 false false false ES-AR X-NONE X-NONE La microscopia confocal de reflectancia (MCR) es una herramienta de imagenes no invasiva que permite la visualizacion in vivo y en tiempo real de estructuras microscopicas de la epidermis, union dermo-epidermica y dermis superficial con una resolucion cuasi histologica. Ha sido utilizada en la evaluacion de lesiones benignas y malignas, demostrando un gran potencial para aplicaciones en investigacion basica y en dermatologia clinica. En el presente trabajo se revisan conceptos acerca del desarrollo y principios basicos del funcionamiento de la MCR, asi como de sus aplicaciones en la practica clinica. (Dermatol. Argent., 2011, 17(3): 230-235). Palabras clave : microscopia confocal de reflectancia, cancer de piel, nevus, melanoma, diagnostico no invasivo Use of confocal reflectance microscopy in dermatology Abstract Reflectance confocal microscopy (RCM) is a noninvasive imaging tool that allows in-vivo and realtime visualization of microscopic structures of the epidermis, dermo-epidermal junction and superficial dermis with a quasi-histological resolution. It has been used in the evaluation of benign and malignant lesions, showing a great potential for applications in basic research and clinical dermatology. This paper reviews concepts about the development and basic principles of the RCM and its applications in clinical practice (Dermatol. Argent., 2011, 17(3): 230-235). Key words : reflectance confocal microscopy, skin cancer, nevus, melanoma, noninvasive imaging. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}
역방향 CDMA 시스템에서 에너지 최적화된 전송기법: 그래프 이론적 접근 ( Energy Optimized Transmission Strategy in CDMA Reverse Link: Graph

We investigate rate scheduling and power allocation problem for a delay constrained CDMA systems. Specifically, we determine an energy efficient scheduling policy, while each user maintains the short term (n time slots) average throughput. We consider a multirate CDMA system where multirate is achieved by multiple codes. Each code can be interpreted as a virtual user. The aim is to schedule the virtual users into each time slot, such that the sum of transmit energy in n time slots is minimized. We then show that the total energy minimization problem can be solved by a shortest path algorithm. We compare the performance of the optimum scheduling with that of TDMA-type scheduling
Two Cases With Antiepileptic Barbexoclone (Maliasin®) Addiction

Two cases with antiepileptic barbexoclone (maliasin®) addiction Barbexoclone (Maliasin®) is an antiepileptic drug, which has been found to be effective in the treatment of epilepsy. Inpatient treatment seeking cases with the diagnosis of barbexoclone abuse accumulate. In this report, we presented 2 inpatient cases in an addiction treatment center with barbexoclone dependence. Also we discussed the non-medical use of prescribed drugs according to these cases.
Detection of unusual optical flow patterns by multilevel hidden Markov models

The analysis of motion information is one of the main tools for the understanding of complex behaviors in video. However, due to the quality of the optical flow of low-cost surveillance camera systems and the complexity of motion, new robust image-processing methods are required to generate reliable higher-level information. In our novel approach there is no need for tracking objects (vehicles, pedestrians) in order to recognize anomalous motion, but dense optical flow information is used to construct mixtures of Gaussians, which are analyzed temporally. We create a multilevel model, where low-level states of non-overlapping image regions are modeled by continuous hidden Markov models (HMMs). From low-level HMMs we compose high-level HMMs to analyze the occurrence of the low-level states. The processing of large numbers of data in traditional HMMs can result in a precision problem due to the multiplication of low probability values. Thus, besides introducing new motion models, we incorporate a scaling technique into the mathematical model of HMMs to avoid precision problems and to get an effective tool for the analysis of large numbers of motion vectors. We illustrate the use of our models with real-life traffic videos.
Statistical models for the estimation of execution time of coarsegrained OpenMP programs

This paper presents a family of statistical models for the estimation of program execution time. The paper discusses the possibilities of how to apply the family to reduce iterative compilation duration and in consequence, software development duration. The discussion is supported with the results of experimental research carried out for program loops selected from the NAS Parallel Benchmarks test suite. Streszczenie. W artykule przedstawiono autorską koncepcję rodziny modeli statystycznych do oszacowania czasu wykonania programu oraz omówiono możliwości wykorzystania jej w celu skrócenia czasu wykonywania kompilacji iteracyjnej (a w konsekwencji czasu wytwarzania oprogramowania). Przedstawiono także wyniki przeprowadzonych badań eksperymentalnych. (Modele statystyczne do oszacowania czasu wykonania aplikacji gruboziarnistych w standardzie OpenMP).