Miracle-dz/UniFAR-FTUs huggingface dataset:
Abs Algorithms for Linear Equations and Abspack

We present the main results obtained during a research on ABS methods in the framework of the project Analisi Numerica e Matematica Computazionale. 1. – The ABS algorithms. ABS algorithms were introduced by Abaffy, Broyden and Spedicato (1984), to solve linear equations first in the form of the basic ABS class, later generalized as the scaled ABS class and applied to linear least squares, nonlinear equations and optimization problems, see e.g. the monographs by Abaffy and Spedicato (1989) and Zhang, Xia and Feng (1999), or the bibliography by Nicolai and Spedicato (1997) listing over 300 ABS papers. In this paper we consider some new results obtained in the framework of project Analisi Numerica e Matematica Computazionale, including the performance of several codes of ABSPACK, a FORTRAN package under development. For later reference, we recall the scaled ABS algorithm for solving the following determined or underdetermined linear system, where rank(A) is arbitrary and A
Performance Evaluation of Blind Source Separation Schemes for Separating Sensor Signals in a Distributed Network

In this paper we consider the problem of sensing the mixture of sources in a distributed network of sensors. We assume that sensors have low computation power. They quantize the mixtures and send them to the server for separation. We investigate the system performance using two low complexity blind source separation methods, FastICA and JADE. FastICA works based on minimization of negentropy of mixture signals, and JADE works based on diagonalizing covariance matrix of two the mixture signals. For simulation we mixed two memoryless Laplacian sources. We encode the mixtures using entropy coded deadzone quantizer at different bit-rates, and separate their encoded mixture signals. We compare the mean square error of reconstructed sources after separation. Results show that JADE has better performance especially for mixtures encoded at low bit-rates.
A Secure Deployment Framework of NEMO (Network Mobility)with Firewall Traversal and AAA Server

The massive production and usage of mobile devices have urged the rapid growth of network mobility technologies. Network mobility (NEMO) protocol offers seamless Internet connectivity to the intended users. It allows any or a group of network nodes to change their point of attachment to the network while maintaining their connections. This mechanism is suitable in a scenario where Internet services are provided inside a train or bus for their passengers. However, a secure deployment of NEMO into the real environment should be considered with the usage of firewall and AAA server. The proposed framework will provide an access control mechanism between the network nodes and the service providers at the same time securing each network entity to some extent with the presence of firewalls and AAA.
Chronology of fluvial terraces and fault activities in and around the Sekigahara region, central Japan

関ヶ原周辺地域の段丘面の編年を行い，活断層の変位速度を求め，近畿三角帯北東縁の断層活動について検討した．空中写真判読および現地調査により，7段（H面群，M1～2面，L1～4面）の段丘面を認定した．段丘構成層および被覆層中に鬼界アカホヤ火山灰（K-Ah），姶良Tn火山灰（AT），鬼界原火山灰（K-Tz）の層準が認められた．火山灰分析と段丘面の地形学的特徴から，形成年代をM1面（90～130 ka），M2面（50～70 ka），L1面（20～30 ka），L2面（15～20 ka），L3面（10～15 ka），L4面（10 ka以降）と推定した．さらに，活断層の上下変位速度を，段丘面の変位量と形成年代から求め，水平変位速度は河谷の屈曲量と上流の長さから推定した．その結果，本地域の活断層の多くは活動度B級であり，一部はA級になることが明らかとなった．近畿三角帯北東縁の活断層の変位速度分布から，本地域の濃尾平野側での上下変位速度の和と水平変位速度の和の比は1 : 0.4～1. 2で，近江盆地側では1 : 2.1～8. 7となり，南部の逆断層帯から本地域を通じて北部の横ずれ断層帯へ移行している可能性が高い．また，近畿三角帯北東縁の断層活動は，沈み込むフィリピン海プレートの形状や深度が影響を及ぼしている可能性がある．
The variability of "nature's limit" in South Australia

The 1864/65 drought that hit South Australia prompted the establishment of Goyder’s Line, which has long been known in the State as the division between land suitable for cropping and land only viable for pasture. Current cropping areas however extend beyond this line, suggesting that either a) the line is not well defined, b) cropping is currently occurring on ‘non-viable’ land or c) the line is variable from year to year. In this study, the 220 mm growing season (April to October) rainfall isohyet is used as a proxy for SA’s cropping boundary in order to assess its temporal and spatial variability. Using indices of the El Nino/Southern Oscillation, Indian Ocean Dipole, Southern Annular Mode and the Subtropical Ridge, it is shown that climate state significantly influences the location of the 220 mm growing season rainfall isohyet. This implies that the boundary between viable and non-viable cropping areas (i.e. "nature’s limit") is non-stationary. These results also indicate the key influences on South Australia’s climate and have important implications globally for agricultural practices operating in or bordering semi-arid environments.
2 Algorithms , Lower Bounds , and Reductions

The 3SUM problem is to decide, given a set of n real numbers, whether any three sum to zero. It is widely conjectured that a trivial Opnq-time algorithm is optimal and over the years the consequences of this conjecture have been revealed. This 3SUM conjecture implies Ωpnq lower bounds on numerous problems in computational geometry and a variant of the conjecture implies strong lower bounds on triangle enumeration, dynamic graph algorithms, and string matching data structures. In this paper we refute the 3SUM conjecture. We prove that the decision tree complexity of 3SUM is Opn3{2 ? log nq and give two subquadratic 3SUM algorithms, a deterministic one running in Opn{plog n{ log log nq2{3q time and a randomized one running in Opnplog log nq{ log nq time with high probability. Our results lead directly to improved bounds for k-variate linear degeneracy testing for all odd k ě 3. The problem is to decide, given a linear function fpx1, . , xkq “ α0 ` ř 1ďiďk αixi and a set A Ă R, whether 0 P fpAq. We show the decision tree complexity of this problem is Opnk{2 ? log nq. Finally, we give a subcubic algorithm for a generalization of the pmin,`q-product over realvalued matrices and apply it to the problem of finding zero-weight triangles in weighted graphs. We give a depth-Opn5{2 ? log nq decision tree for this problem, as well as an algorithm running in time Opnplog log nq{ log nq.
mu-Limit Sets of Cellular Automata from a Computational Complexity Perspective

This paper concerns µ -limit sets of cellular automata: sets of conﬁgu-rations made of words whose probability to appear does not vanish with time, starting from an initial µ -random conﬁguration. More precisely, we investigate the computational complexity of these sets and of related decision problems. Main results: ﬁrst, µ -limit sets can have a Σ 03 -hard language, second, they can contain only α -complex conﬁgurations, third, any non-trivial property concerning them is at least Π 03 -hard. We prove complexity upper bounds, study restrictions of these questions to particular classes of CA, and diﬀerent types of (non-)convergence of the measure of a word during the evolution
Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption

—A benchmark study of modern distributed databases is an important source of information to select the right technology for managing data in the cloud-edge paradigms. To make the right decision, it is required to conduct an extensive experimental study on a variety of hardware infrastructures. While most of the state-of-the-art studies have investigated only response time and scalability of distributed databases, focusing on other various metrics (e.g., energy, bandwidth, and storage consumption) is essential to fully understand the resources consumption of the distributed databases. Also, existing studies have explored the response time and scalability of these databases either in private or public cloud. Hence, there is a paucity of investigation into the evaluation of these databases deployed in a hybrid cloud, which is the seamless integration of public and private cloud. To address these research gaps, in this paper, we investigate energy, bandwidth and storage consumption of the most used and common distributed databases. For this purpose, we have evaluated four open-source databases (Cassandra, Mongo, Redis and MySQL) on the hybrid cloud spanning over local OpenStack and Microsoft Azure, and a variety of edge computing nodes including Raspberry Pi, a cluster of Raspberry Pi, and low and high power servers. Our extensive experimental results reveal several helpful insights for the deployment selection of modern distributed databases in edge-cloud environments.
Contractive interference functions and rates of convergence of distributed power control laws

The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of convergence of the iterates. This paper introduces contractive interference functions, a slight reformulation of the standard interference functions that guarantees existence and uniqueness of fixed-points and geometric convergence rates. We show that many power control laws from the literature are contractive and derive, sometimes for the first time, convergence rate estimates for these algorithms. Finally, we show that although standard interference functions are not contractive, they are paracontractions with respect to a certain metric space. Extensions to two-sided scalable interference functions are also discussed.
Low Complexity Coefficient Selection Algorithms for Compute-and-Forward

Compute-and-Forward (C&F) has been proved to be an efficient approach for multiuser, multihop wireless networks. It allows intermediate relays to infer and forward linear combinations of the transmitted messages, in order to achieve high computation rate. The coefficient selection for each relay is a shortest vector problem (SVP). The most common solutions for such a problem are exhaustive search and the Lenstra-LenstraLovsz (LLL) lattice reduction. The former has a high complexity and the latter proves to be suboptimal for large numbers of users. In this paper, we propose two novel algorithms, of which one can be regarded as a simplified version of the exhaustive search, while the other one selects the coefficients iteratively. Numerical results reveal that both methods achieve the same computation rate as the exhaustive search with much lower complexity. Both outperform the LLL algorithm (especially with a large number of users), and the complexities are lower than the LLL except in the very high SNR region.
Regularization after retention in ultrahigh dimensional linear regression models

In ultrahigh dimensional setting, independence screening has been both theoretically and empirically proved a useful variable selection framework with low computation cost. In this work, we propose a two-step framework by using marginal information in a different perspective from independence screening. In particular, we retain significant variables rather than screening out irrelevant ones. The new method is shown to be model selection consistent in the ultrahigh dimensional linear regression model. To improve the finite sample performance, we then introduce a three-step version and characterize its asymptotic behavior. Simulations and real data analysis show advantages of our method over independence screening and its iterative variants in certain regimes.
A Proposed Artificial Neural Network Classifier to Identify Tumor Metastases

In this paper we propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. A non-parametric artificial neural network methodology has been chosen because of the analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables.
Validation of k-Nearest Neighbor Classifiers Using Inclusion and Exclusion

This paper presents a series of PAC exponential error bounds for k-nearest neighbors classifiers, with O(n− r 2r+1 √ k lnn) error bound range for each integer r > 0, where n is the number of insample examples. This shows that k-nn classifiers, in spite of their famously fractured decision boundaries, come close to having Gaussian-style exponential error bounds with O(n− 1 2 ) bound ranges.
Validation of k-Nearest Neighbor Classifiers Using Inclusion and Exclusion

In machine learning, we begin with a set of labeled in-sample examples. We use those examples to develop a classifier, which maps from inputs to labels. The pr imary goal is to develop a classifier that performs well on out-of-sample data. This goal is calle d g neralization. A secondary goal is to evaluate how well the classifier will perform on out-of-sa mple data. This is calledvalidation. We do not want to sacrifice generalization for validation; we want to use all in-sample examples to develop the classifier and perform validation as well. This paper focuses on validation of k-nearest neighbor ( k-nn) classifiers. Ak-nn classifier consists of the in-sample examples and a metric to determine distances between inputs. To label an input, ak-nn classifier first determines which k in-sample examples have inputs closest to the input to be classified. Then the classifier labels the input with the lab l shared by a majority of those k nearest neighbors. (We assume that k is odd.) We also assume binary classification, meaning that there are only two possible labels. The error bounds used to validate classifiers in this paper ar e probably approximately correct (PAC) bounds. PAC bounds consist of a range and a bound on the p robability that the actual out-of-sample error rate is outside the range. An effective PAC bound has a small range and a small bound failure probability. PAC error bounds include b ounds based on Vapnik-Chervonenkis (VC) dimension (Vapnik and Chervonenkis, 1971), bounds for concept learning by Valiant (1984), compression-based bounds by Littlestone and Warmuth (1986 ), Floyd and Warmuth (1995), Blum and Langford
STAR-Vote: A Secure, Transparent, Auditable, and Reliable Voting System

STAR-Vote is a collaboration between a number of academics and the Travis County (Austin), Texas elections office, which currently uses a DRE voting system and previously used an optical scan voting system. STAR-Vote represents a rare opportunity for a variety of sophisticated technologies, such as end-to-end cryptography and risk limiting audits, to be designed into a new voting system, from scratch, with a variety of real world constraints, such as election-day vote centers that must support thousands of ballot styles and run all day in the event of a power failure. This paper describes the current design of STAR-Vote which is now largely settled and whose development will soon begin.
Active Learning for Cost-Sensitive Classification

i,y with features x and label y. The computation of this sensitivity value is governed by the actual online update where we compute the derivative of the change in the prediction as a function of the importance weight w for a hypothetical example with cost 0 or cost 1 and the same features. This is possible for essentially all online update rules on importance weighted examples and it corresponds to taking the limit as w ! 0 of the change in prediction due to an update divided by w. By inspection this requires only O(d) time per example, where d is the average number of non-zero features. With these two steps, we obtain approximate minimum and maximum costs using go
Data collection & preprocessing Feature extraction Learning Classification Regression Retrieval Segmentation Reconstruction Synthesis Processed or generated data with semantic information Clustering Inference ... ... Feature learning Belief propagation Bayesian inference Gibbs sampling

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.
Performance Analysis of Energy Detection over Composite    Shadowed Fading Channels

-Energy detection is a reliable non-coherent signal processing technology of spectrum sensing of cognitive radio networks, which thanks to its low complexity, no requirement of priori received information and fast sensing ability etc. Since the excellent performance of energy detection would be actually affected by physical multipath fading, this paper is concentrating on characteristics analysis of energy detection over composite   shadowed fading channels. The small-scale and line-of-sight   fading distribution consists of particular examples such as Rayleigh, Hoyt, Nakagami-m and one sided Gaussian distributions. Based on this, we derive the probability density function of signal envelope and signal-to-noise ratio of the composite   shadowed fading channels, which could accurately present the line-of-sight shadowed fading characterization. Subsequently the exact close-form expressions with infinite series formulation for the appropriate detection probability have been firstly extended to estimate detection capacity of the above-mentioned model by adopting Inverse Gaussian asymptotic distribution. In addition, the absolute truncation error is deduced for evaluating minimum detection efficiency. The established model can be also applied in detection estimation with non-integral fading parameters. Last but not least, the analytical results and quantification performance are approved by numerically evaluation with MATHEMATICA and MATLAB as the power variables of dominant components changes.
Resource Allocation for CoMP in Cellular Networks With Base Station Sleeping

Base station sleeping (BSS) can result in significant reduction in energy consumption of cellular networks during low traffic conditions. We show that the coverage loss due to BSS can be compensated via coordinated multi-point (CoMP) -based transmission in a cluster of base stations. For a BSS with CoMP-based system, we propose various BSS patterns to achieve suitable trade-offs between energy savings and throughput. We formulate the CoMP resource allocation and $\alpha -$ Fair user scheduling as a joint optimization problem. We derive the optimal time fraction and user scheduling for this problem and use it to formulate a simplified BSS with CoMP optimization problem. A heuristic that solves this problem is presented. Through extensive simulations, we show that suitable trade-offs among energy, coverage, and rate can be achieved by appropriately selecting the BSS pattern, CoMP cluster, and rate threshold.
Achieving exact cluster recovery threshold via semidefinite programming

The binary symmetric stochastic block model deals with a random graph of n vertices partitioned into two equal-sized clusters, such that each pair of vertices is connected independently with probability p within clusters and q across clusters. In the asymptotic regime of p = a log n/n and q = b log n/n for fixed a, b and n → ∞, we show that the semidefinite programming relaxation of the maximum likelihood estimator achieves the optimal threshold for exactly recovering the partition from the graph with probability tending to one, resolving a conjecture of Abbe et al. [1]. Furthermore, we show that the semidefinite programming relaxation also achieves the optimal recovery threshold in the planted dense subgraph model containing a single cluster of size proportional to n.
Unobtrusive sleep monitoring using smartphones

How we feel is greatly influenced by how well we sleep. Emerging quantified-self apps and wearable devices allow people to measure and keep track of sleep duration, patterns and quality. However, these approaches are intrusive, placing a burden on the users to modify their daily sleep related habits in order to gain sleep data; for example, users have to wear cumbersome devices (e.g., a headband) or inform the app when they go to sleep and wake up. In this paper, we present a radically different approach for measuring sleep duration based on a novel best effort sleep (BES) model. BES infers sleep using smartphones in a completely unobtrusive way - that is, the user is completely removed from the monitoring process and does not interact with the phone beyond normal user behavior. A sensor-based inference algorithm predicts sleep duration by exploiting a collection of soft hints that tie sleep duration to various smartphone usage patterns (e.g., the time and length of smartphone usage or recharge events) and environmental observations (e.g., prolonged silence and darkness). We perform quantitative and qualitative comparisons between two smartphone only approaches that we developed (i.e., BES model and a sleep-with-the-phone approach) and two popular commercial wearable systems (Le., the Zeo headband and Jawbone wristband). Results from our one-week 8-person study look very promising and show that the BES model can accurately infer sleep duration (± 42 minutes) using a completely "hands off" approach that can cope with the natural variation in users' sleep routines and environments.
HIGH-DIMENSIONAL ANALYSIS OF SEMIDEFINITE RELAXATIONS FOR SPARSE PRINCIPAL COMPONENTS1 BY ARASH

Principal component analysis (PCA) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance matrix. However, PCA is well known to behave poorly in the “large p, small n” setting, in which the problem dimension p is comparable to or larger than the sample size n. This paper studies PCA in this high-dimensional regime, but under the additional assumption that the maximal eigenvector is sparse, say, with at most k nonzero components. We consider a spiked covariance model in which a base matrix is perturbed by adding a k-sparse maximal eigenvector, and we analyze two computationally tractable methods for recovering the support set of this maximal eigenvector, as follows: (a) a simple diagonal thresholding method, which transitions from success to failure as a function of the rescaled sample size θdia(n,p, k) = n/[k2 log(p − k)]; and (b) a more sophisticated semidefinite programming (SDP) relaxation, which succeeds once the rescaled sample size θsdp(n,p, k) = n/[k log(p − k)] is larger than a critical threshold. In addition, we prove that no method, including the best method which has exponential-time complexity, can succeed in recovering the support if the order parameter θsdp(n,p, k) is below a threshold. Our results thus highlight an interesting trade-off between computational and statistical efficiency in high-dimensional inference.
DECAY OF COEFFICIENTS AND APPROXIMATION RATES IN GABOR GAUSSIAN FRAMES

Gabor frames are a standard tool to decompose functions into a discrete sum of “coherent states”, which are localised both in position and Fourier spaces. Such expansions are somehow similar to Fourier expansions, but are more subtle, as Gabor frames do not form orthonormal bases. In this work, we analyze decay properties of the coeﬃcients of functions in these frames in terms of the regularity of the functions and their decay at inﬁnity. These results are analogous to the standard decay properties of Fourier coeﬃcients, and permit to show that a ﬁnite number of coherent states provide a good approximation to any smooth rapidly decaying function. Speciﬁcally, we provide explicit convergence rates in Sobolev norms, as the number of selected coherent states increases. Our results are especially useful in numerical analysis, when Gabor wavelets are employed to discretize PDE problems.