Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Sort and its Parallel Implementation

In this paper, a sorting technique is presented that takes as input a data set whose primary key domain is known to the sorting algorithm, and works with an time efficiency of O(n+k), where k is the primary key domain. It is shown that the algorithm has applicability over a wide range of data sets. Later, a parallel formulation of the same is proposed and its effectiveness is argued. Though this algorithm is applicable over a wide range of general data sets, it finds special application (much superior to others) in places where sorting information that arrives in parts and in cases where input data is huge in size.
A time-accurate, adaptive discretization for fluid flow problems

This report presents a low computational and cognitive complexity, stable, time accurate and adaptive method for the Navier-Stokes equations. The improved method requires a minimally intrusive modification to an existing program based on the fully implicit / backward Euler time discretization, does not add to the computational complexity, and is conceptually simple. The backward Euler approximation is simply post-processed with a two-step, linear time filter. The time filter additionally removes the overdamping of Backward Euler while remaining unconditionally energy stable, proven herein. Even for constant stepsizes, the method does not reduce to a standard / named time stepping method but is related to a known 2-parameter family of A-stable, two step, second order methods. Numerical tests confirm the predicted convergence rates and the improved predictions of flow quantities such as drag and lift.
Torus Principal Component Analysis with an Application to RNA Structures

There are several cutting edge applications needing PCA methods for data on tori and we propose a novel torus-PCA method with important properties that can be generally applied. There are two existing general methods: tangent space PCA and geodesic PCA. However, unlike tangent space PCA, our torus-PCA honors the cyclic topology of the data space whereas, unlike geodesic PCA, our torus-PCA produces a variety of non-winding, non-dense descriptors. This is achieved by deforming tori into spheres and then using a variant of the recently developed principle nested spheres analysis. This PCA analysis involves a step of small sphere fitting and we provide an improved test to avoid overfitting. However, deforming tori into spheres creates singularities. We introduce a data-adaptive pre-clustering technique to keep the singularities away from the data. For the frequently encountered case that the residual variance around the PCA main component is small, we use a post-mode hunting technique for more fine-grained clustering. Thus in general, there are three successive interrelated key steps of torus-PCA in practice: pre-clustering, deformation, and post-mode hunting. We illustrate our method with two recently studied RNA structure (tori) data sets: one is a small RNA data set which is established as the benchmark for PCA and we validate our method through this data. Another is a large RNA data set (containing the small RNA data set) for which we show that our method provides interpretable principal components as well as giving further insight into its structure.
A New Interpretation of Amdahl's Law and Geometric Scalability

The multiprocessor effect refers to the loss of computing cycles due to processing overhead. Amdahl’s law and the Multiprocessing Factor (MPF) are two scaling models used in industry and academia for estimating multiprocessor capacity in the presence of this multiprocessor effect. Both models express different laws of diminishing returns. Amdahl’s law identifies diminishing processor capacity with a fixed degree of serialization in the workload, while the MPF model treats it as a constant geometric ratio. The utility of both models for performance evaluation stems from the presence of a single parameter that can be determined easily from a small set of benchmark measurements. This utility, however, is marred by a dilemma. The two models produce different results, especially for large processor configurations that are so important for today’s applications. The question naturally arises: Which of these two models is the correct one to use? Ignoring this question merely reduces capacity prediction to arbitrary curve-fitting. Removing the dilemma requires a dynamical interpretation of these scaling models. We present a physical interpretation based on queueing theory and show that Amdahl’s law corresponds to synchronous queueing in a bus model while the MPF model belongs to a Coxian server model. The latter exhibits unphysical effects such as sublinear response times hence, we caution against its use for large multiprocessor configurations.
A novel deep learning model for detection of denial of service attacks in HTTP traffic over internet

The technological advancements in internet and mobile communications bring new dimension to the usage of internet applications and services. The accessibility to the enhanced services is intentionally blocked by the denial of service attacks. This paper proposes a novel deep learning classification model to detect the denial of service attacks in application layer for different network environments, such as wired network, ad hoc network and mobile ad hoc network. The simulation results illustrate that the performance of the proposed deep learning model is proficiently improved compared to existing bio-inspired and machine learning models in terms of detection accuracy and classification metrics.
milr: Multiple-Instance Logistic Regression with Lasso Penalty

The purpose of the milr package is to analyze multiple-instance data. Ordinary multipleinstance data consists of many independent bags, and each bag is composed of several instances. The statuses of bags and instances are binary. Moreover, the statuses of instances are not observed, whereas the statuses of bags are observed. The functions in this package are applicable for analyzing multiple-instance data, simulating data via logistic regression, and selecting important covariates in the regression model. To this end, maximum likelihood estimation with an expectation-maximization algorithm is implemented for model estimation, and a lasso penalty added to the likelihood function is applied for variable selection. Additionally, an "milr" object is applicable to generic functions fitted, predict and summary. Simulated data and a real example are given to demonstrate the features of this package.
3 Feature Selection Algorithm : Backward Elimination

We introduce a framework of feature filtering for supervised learning. It employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between data and labels. The key idea is that good features should maximize such dependence. Feature selection for various supervised learning problems (including binary, multiclass and regression problems) can be unified under this framework, and the solution is approximated using a backward-elimination algorithm. Particularly, for binary problems, HSIC is also related to criteria such as Pearson’s correlation, signal-to-noise ratio, Maximum Mean Discrepancy and the KernelTarget Alignment. We conducted experiments on various real world data, which demonstrate the usefulness of this framework.
Accuracy Versus Complexity for mmWave Ray-Tracing: A Full Stack Perspective

The millimeter wave (mmWave) band will provide multi-gigabits-per-second connectivity in the radio access of future wireless systems. The high propagation loss in this portion of the spectrum calls for the deployment of large antenna arrays to compensate for the loss through high directional gain, thus introducing the need for a spatial dimension in the channel model to accurately represent the performance of a mmWave network. In this perspective, ray tracing can characterize the channel in terms of Multi Path Components (MPCs) to provide a highly accurate model, at the price of extreme computational complexity (e.g., for processing detailed environment information about the propagation), which may limit the scalability of the simulations. In this paper, we present possible simplifications to improve the trade-off between accuracy and complexity in ray-tracing simulations at mmWaves by reducing the total number of MPCs. The effect of such simplifications is evaluated from a full-stack perspective through end-to-end simulations, testing different configuration parameters, propagation scenarios, and higher-layer protocol implementations. We then provide guidelines on the optimal degree of simplification, for which it is possible to reduce the complexity of simulations with a minimal reduction in accuracy for different deployment scenarios.
Minimizing Differences of Convex Functions and Applications to Facility Location and Clustering

In this paper we develop algorithms to solve generalized weighted Fermat-Torricelli problems with positive and negative weights and multifacility location problems involving distances generated by Minkowski gauges. We also introduce a new model of clustering based on squared distances to convex sets. Using the Nesterov smoothing technique and an algorithm for minimizing differences of convex functions called the DCA introduced by Tao and An, we develop effective algorithms for solving these problems.
Dense Human Body Correspondences Using Convolutional Networks

We show that our deep network structure for computing dense correspondences achieves state-of-the-art performance on establishing correspondences between the intraand inter-subject pairs from the FAUST dataset [1]. For each 3D scan in this dataset, we compute a per-vertex feature descriptor by first rendering depth maps from multiple viewpoints and averaging the per-pixel feature descriptors. Correspondences are then established by nearest neighbor search in the feature space. The accuracy of this direct method is already significantly better than all existing global shape matching methods (that do not require initial poses as input), and is comparable to the state-ofthe-art non-rigid registration method proposed by Chen et al. [3], which uses the initial poses of the models to refine correspondences. To make a fair comparison with Chen et al. [3], we use an out-of-the-shelf non-rigid registration algorithm [5] to refine our results. We initialize the registration algorithm with the correspondences established with the nearest-neighbor search and refine their positions after non-rigid alignment. Results obtained with and without this refinement step are reported in Figure 1 and Table 1. It is worth mentioning that per-vertex feature descriptors for each scan are pre-computed. Thus for each pair of scans, we can obtain dense correspondences in less than a second. Though our method is designed for clothed human subjects, our algorithm is far more efficient than all other known methods which rely on local or global geometric properties. References
A proof of the strong converse theorem for Gaussian broadcast channels via the Gaussian Poincaré inequality

We prove that 2-user Gaussian broadcast channels admit the strong converse. This implies that for every sequence of block codes with an asymptotic maximal error probability smaller than one, the limit points of the corresponding sequence of rate pairs must lie within the capacity region derived by Cover and Bergmans. The main mathematical tool required for our analysis is a logarithmic Sobolev inequality known as the Gaussian Poincaré inequality.
Internet Engineering Task Force (ietf) Specification of the Ip Flow Information Export (ipfix) Protocol for the Exchange of Flow Information

This document specifies the IP Flow Information Export (IPFIX) protocol, which serves as a means for transmitting Traffic Flow information over the network. In order to transmit Traffic Flow information from an Exporting Process to a Collecting Process, a common representation of flow data and a standard means of communicating them are required. This document describes how the IPFIX Data and Template Records are carried over a number of transport protocols from an IPFIX Exporting Process to an IPFIX Collecting Process. This document obsoletes RFC 5101.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES IN COGNITIVE RADIO

Wireless networks are characterized by fixed spectrum policy. With increasing demands for wireless communication efficiently using the spectrum resources has become an essential issue. Cognitive radio is a form of wireless communication which is used to sense the spectrum and find the free spectrum. It is used by unlicensed users without causing interference to the licensed user. Cognitive radio with the dynamic spectrum access is key technology which provides the best solution by allowing a group of Secondary users to share the radio spectrum originally allocated to the primary users. Dynamically accessing the unused spectrum is known as dynamic spectrum access (DSA) which becomes a promising approach to increase the efficiency of spectrum usage. In this paper, DSA models are discussed along with different methods such as game theory based method, a measurement-based model, network coded cognitive control channel, Markovian Queuing model, the Delay performance of threshold policies, fuzzy logic based method and spatio-temporal spectrum management model.
Topic In Elliptic Curves Over Finite Fields: The Groups of Points

This article is a short introduction to the theory of the groups of points of elliptic curves over finite fields. It is concerned with the elementary theory and practice of elliptic curves cryptography, the new generation of public key systems. The material and coverage are focused on the groups of points of elliptic curves and algebraic curves, but not exclusively.
A HMTT : A Hybrid Hardware / Software Tracing System for Bridging the DRAM Access Trace ’ s Semantic Gap

DRAM access traces (i.e., off-chip memory references) can be extremely valuable for the design of memory subsystems and performance tuning of software. Hardware snooping on the off-chip memory interface is an effective and non-intrusive approach to monitoring and collecting real-life DRAM accesses. However, compared with software-based approaches, hardware snooping approaches typically lack semantic information, such as process/function/object identifiers, virtual addresses, and lock contexts, that is essential to the complete understanding of the systems and software under investigation. In this paper, we propose a hybrid hardware/software mechanism that is able to collect off-chip memory reference traces with semantic information. We have designed and implemented a prototype system called HMTT (Hybrid Memory Trace Tool), which uses a custom made DIMM-connector to collect off-chip memory references and a high-level event-encoding scheme to correlate semantic information with memory references. In addition to providing complete, undistorted DRAM access traces, the proposed system is also able to perform various types of low-overhead profiling, such as object-relative accesses and multithread lock accesses.
Survey on Security Issues in Cloud Computing

—Cloud Computing is a new technology that allows organizations and individuals to share resources, information and software on-demand over the Internet. It is a new consumption, supplement and delivery model wherein resources are provided in a cost effective manner. It typically involves the use of software and hardware that are delivered as a service. The technology of cloud computing deals with leaving the provision of resources to a remote server and this server has performs services as per the user’s need and data. This Research Paper discusses the concepts of the ‘Cloud’, the issues arisen by the cloud as well as discusses a method to select the best “Cloud” for an organization.
A P ] 2 0 Se p 20 06 Error bounds for semi-Galerkin approximations of nonhomogeneous incompressible fluids

We consider spectral semi-Galerkin approximations for the strong solutions of the nonhomogeneous Navier-Stokes equations. We derive an optimal uniform in time error bound in the H 1 norm for approximations of the velocity. We also derive an error estimate for approximations of the density in some spaces L r .
C C ] 2 2 Fe b 20 07 A Sequential Algorithm for Generating Random Graphs ∗

The goal of this article is to develop a fast algorithm for generating simple random graphs with a given degree sequence. For degree sequence (di) with maximum degree dmax = O(m ), our algorithm generates an asymptotically uniform random graph with that degree sequence in almost linear time, where m = 12 ∑ i di is the number of edges in the graph and ǫ is any positive constant. Our method also gives an independent proof of McKay’s estimate [35] for number of such graphs. Additionally, for d-regular graphs on n vertices we show the above result holds in the larger region dmax = O(m ), or equivalently d = O(n).
Heritability of body mass index in pre-adolescence, young adulthood and late adulthood

Increased body mass index (BMI) is a world- wide health issue. Individual differences in the suscepti- bility to increased BMI could be related to genes or environment. We performed a systematic review of genetic studies on BMI in pre-adolescence, young adulthood and late adulthood. We searched PubMed and EMBASE with heritability, body mass index, BMI, weight, height, anthropometry and twins as search terms. Studies reporting intra-pair correlations of healthy twin pairs that were raised together were included. This resulted in the inclusion of 8,179 monozygotic (MZ) and 9,977 dizygotic (DZ) twin pairs from twelve published studies in addition to indi- vidual participant data for 629 MZ and 594 DZ pairs from four twin registries. Structural equation modelling with intra-pair twin correlations showed that the heritability of BMI remained high over all age categories ranging from 61 % (95 % CI 54-64 %) to 80 % (95 % CI 76-81 %) for male and female subjects combined, while unique
Ju n 20 05 Aperiodic Tilings : Breaking Translational Symmetry

Classical results on aperiodic tilings are rather complicated and not widely understood. Below an alternative approach to these results is offered in hope to provide additional intuition not apparent in classical works. 1 Palettes and Tilings Physical computing media are asymmetric. Their symmetry is broken by irregularities, physical boundaries, external connections, etc. Such peculiarities, however, are highly variable and extraneous to the fundamental nature of the media. Thus, they are pruned from theoretical models such as cellular automata, and reliance on them is frowned upon in programming practice. Yet, computation, like many other highly organized activities, is incompatible with perfect symmetry. Some standard mechanisms must assure breaking the symmetry inherent in idealized computing models. A famous example of such mechanisms is aperiodic tiling: hierarchical self-similar constructions, first used for computational purposes in a classical – although rather complicated – work [Berger 66]. They were further developed in [Robinson 71, Myers 74, Gurevich Koriakov 72]; [Allauzen Durand 96] give a helpful exposition. Definition 1 Let G be the grid of unit length edges between integer points on an infinite plane. A tiling T is its mapping into a finite set of colors. Its crosses and tiles are ordered color combinations of four edges sharing a corner or forming a square respectively. A Palette P of T is a set including all its tiles (+palette for crosses). We say P with a mapping f of its colors into a smaller color alphabet enforces a set S of tilings if replacing colors according to f turns each P -tiling into a tiling in S. Turning each edge orthogonally around its center turns G into its dual graph and palettes into +palettes and vice versa. Thus, one can use either type as convenient. Supported by NSF grant CCR-0311411. Computer Science department, 111 Cummington St., Boston, MA 02215; (email: Lnd at bu.edu)
Explainable Image Classification with Evidence Counterfactual

The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. Existing explanation methods generally create importance rankings in terms of pixels or pixel groups. However, the resulting explanations lack an optimal size, do not consider feature dependence and are only related to one class. Counterfactual explanation methods are considered promising to explain complex model decisions, since they are associated with a high degree of human interpretability. In this paper, SEDC is introduced as a model-agnostic instance-level explanation method for image classification to obtain visual counterfactual explanations. For a given image, SEDC searches a small set of segments that, in case of removal, alters the classification. As image classification tasks are typically multiclass problems, SEDC-T is proposed as an alternative method that allows specifying a target counterfactual class. We compare SEDC(-T) with popular feature importance methods such as LRP, LIME and SHAP, and we describe how the mentioned importance ranking issues are addressed. Moreover, concrete examples and experiments illustrate the potential of our approach (1) to obtain trust and insight, and (2) to obtain input for model improvement by explaining misclassifications.
Description of seasonal beach changes using empirical eigenfunctions

A 2-yr set of profile data from Torrey Pines Beach, California, measured at monthly intervals has been statistically analyzed by using empirical eigenfunctions. The analysis separates the temporal and spatial dependence of the data, this separation permitting beach changes to be described objectively by a linear combination of corresponding time and space functions. Most of the variation in profile configuration can be accounted for by three eigenfunctions corresponding to the three largest eigenvalues. The largest eigenvalue corresponds to an eigenfunction called the ‘mean beach function,’ which represents an average profile. A second eigenfunction, the ‘bar-berm function,’ has a large maximum at the location of the summer berm and a minimum at the location of the winter bar, indicating its relation to the seasonal onshore-offshore movement of sand. The third eigenfunction, the ‘terrace function,’ has a maximum at the location of the low-tide terrace. Results of this study indicate that the eigenfunctions are useful in the analysis of beach profile data and provide objective insight into the nature of the variations of the profile configuration.
PRISONERS’ DILEMMA IN ONE-DIMENSIONAL CELLULAR AUTOMATA: VISUALIZATION OF EVOLUTIONARY PATTERNS

The spatial Prisoners’ Dilemma is a prototype model to show the emergence of cooperation in very competitive environments. It considers players at site of lattices that can either cooperate or defect when playing the Prisoners’ Dilemma with other z players. The value of z is determined by the lattice coordination number and order of considered neighborhood of each player. This model presents a rich phase diagram. In this work we consider players in cells of an one-dimensional cellular automata where interacting with z players. This geometry allows us to vary, in a simple manner, the number of neighbors ranging from one up to the lattice size, including self-interaction. This approach has multiple advantages. It is simple to implement numerically and we are able to retrieve all the previous results found in the previous considered lattices, with a faster convergence to stationary values. More importantly, it permits us to keep track of the spatio-temporal evolution of each player of the automaton, which gives rise to interesting patterns. These patterns allows the interpretation of cooperation/defection clusters as particles which can be absorbed and collide among themselves. The presented approach represents a new paradigm to study the emergence and maintenance of cooperation in the spatial Prisoners’ Dilemma.