Miracle-dz/UniFAR-FTUs huggingface dataset:
Shannon's Secrecy System With Informed Receivers and its Application to Systematic Coding for Wiretapped Channels

Shannon's secrecy system is studied in a setting, where both the legitimate decoder and the wiretapper have access to side information sequences correlated to the source, but the wiretapper receives both the coded information and the side information via channels that are more noisy than the respective channels of the legitmate decoder, which in turn, also shares a secret key with the encoder. A single-letter characterization is provided for the achievable region in the space of five figures of merit: the equivocation at the wiretapper, the key rate, the distortion of the source reconstruction at the legitimate receiver, the bandwidth expansion factor of the coded channels, and the average transmission cost (generalized power). Beyond the fact that this is an extension of earlier studies, it also provides a framework for studying fundamental performance limits of systematic codes in the presence of a wiretap channel. The best achievable performance of systematic codes is then compared to that of a general code in several respects, and a few examples are given.
Automatically generating features for learning program analysis heuristics

We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been proposed, which mine existing codebases and automatically learn heuristics for finding a cost-effective abstraction for a given analysis task. Such approaches reduce the burden of the analysis designers, but they do not remove it completely; they still leave the highly nontrivial task of designing so called features to the hands of the designers. Our technique automates this feature design process. The idea is to use programs as features after reducing and abstracting them. Our technique goes through selected program-query pairs in codebases, and it reduces and abstracts the program in each pair to a few lines of code, while ensuring that the analysis behaves similarly for the original and the new programs with respect to the query. Each reduced program serves as a boolean feature for program-query pairs. This feature evaluates to true for a given program-query pair when (as a program) it is included in the program part of the pair. We have implemented our approach for three real-world program analyses. Our experimental evaluation shows that these analyses with automatically-generated features perform comparably to those with manually crafted features.
Concentration inequalities of the cross-validation estimator for empirical risk minimizer

ABSTRACT We derive concentration inequalities for the cross-validation estimate of the generalization error for empirical risk minimizers. In the general setting, we show that the worst-case error of this estimate is not much worse that of training error estimate see Kearns M, Ron D. [Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput. 1999;11:1427–1453]. General loss functions and class of predictors with finite VC-dimension are considered. Our focus is on proving the consistency of the various cross-validation procedures. We point out the interest of each cross-validation procedure in terms of rates of convergence. An interesting consequence is that the size of the test sample is not required to grow to infinity for the consistency of the cross-validation procedure.
Linear and order statistics combiners for reliable pattern classification

In difficult classification problems, the performance of a single classifier is often inadequate. In such cases, several researchers have combined the outputs of multiple classifiers to obtain better performance. This dissertation provides an analytical framework to quantify the improvements due to combining in classification problems. We show that combining classifiers in output space reduces the variance of the actual decision region boundaries around the optimum boundary. For linear combiners, we show that, in the absence of classifier bias, the added classification error, or the error due to the selection of a particular classifier, is proportional to the boundary variance. In the presence of bias, the error reduction is shown to depend on two factors, and in general is less than the reduction obtained in the absence of bias. Furthermore, we introduce the order statistics combiners, a family of non-linear combiners. We show analytically that the selection of the median, the maximum, and in general the ith order statistic improves classifier performance. Another aspect of pattern classification that is of paramount importance is determining the fundamental limits on classification performance. The Bayesian error is such a limit, but is generally difficult to estimate. We provide a method based on linear combining theory that estimates the Bayesian error rates. Then, we present a more general method that provides decision confidences and error bounds based on different error types arising from the training data. Experimental results on both a difficult underwater acoustic data set and several public domain data sets are provided to confirm that both linear and order statistics combiners lead to improved classification performance, and that, in most cases, the results are near the fundamental limits.
Discrete Memoryless Interference and Broadcast Channels with Confidential Messages: Secrecy Rate Regions

Discrete memoryless interference and broadcast channels in which independent confidential messages are sent to two receivers are considered. Confidential messages are transmitted to each receiver with perfect secrecy, as measured by the equivocation at the other receiver. In this paper, we derive inner and outer bounds for the achievable rate regions for these two communication systems.
The geology of the Mt Stoker area, Eastern Otago

Abstract The area mapped, south-east of Middlemarch, is underlain by schist but also includes several small Tertiary outliers of basanitic volcanic rocks resting on a thin formation of quartz sands and conglomerates. In this paper the lithology, mineralogy, and structure of the schist are described. The following conclusions are drawn: (1) bedding in a wide range of thicknesses is recognisable in the schist, (2) the area lies within a biotite and possibly garnet zone of metamorphism, (3) retrogressive metamorphism occurred and appears to have been accompanied by a deformational phase, and (4) there were at least two phases of deformation during the metamorphic period, the first possibly having produced large-scale recumbent folds.
GAUSSIAN PROCESSES WITH MULTIDIMENSIONAL DISTRIBUTION INPUTS VIA OPTIMAL TRANSPORT AND HILBERTIAN EMBEDDING BY FRANÇOIS BACHOC

In this work, we investigate Gaussian Processes indexed by multidimensional distributions. While directly constructing radial positive definite kernels based on the Wasserstein distance has been proven to be possible in the unidimensional case, such constructions do not extend well to the multidimensional case as we illustrate here. To tackle the problem of defining positive definite kernels between multivariate distributions based on optimal transport, we appeal instead to Hilbert space embeddings relying on optimal transport maps to a reference distribution, that we suggest to take as a Wasserstein barycenter. We characterize in turn radial positive definite kernels on Hilbert spaces, and show that the covariance parameters of virtually all parametric families of covariance functions are microergodic in the case of (infinitedimensional) Hilbert spaces. We also investigate statistical properties of our suggested positive definite kernels on multidimensional distributions, with a focus on consistency when a population Wasserstein barycenter is replaced by an empirical barycenter and additional explicit results in the special case of Gaussian distributions. Finally, we study the Gaussian process methodology based on our suggested positive definite kernels in regression problems with multidimensional distribution inputs, on simulation data stemming both from synthetic examples and from a mechanical engineering test case.
2 4 O ct 2 00 7 Bayesian Treed Gaussian Process Models with an Application to Computer Modeling

Motivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details which make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster simulator, our approach is demonstrated to be effective in other arenas.
Subspace correction methods for total variation and $\ell_1-$minimization

This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a semi-norm for a subspace. The optimization is realized by alternating minimizations of the functional on a sequence of orthogonal subspaces. On each subspace an iterative proximity-map algorithm is implemented via \emph{oblique thresholding}, which is the main new tool introduced in this work. We provide convergence conditions for the algorithm in order to compute minimizers of the target energy. Analogous results are derived for a parallel variant of the algorithm. Applications are presented in domain decomposition methods for singular elliptic PDE's arising in total variation minimization and in accelerated sparse recovery algorithms based on $\ell_1$-minimization. We include numerical examples which show efficient solutions to classical problems in signal and image processing.
On the prey of the Indian Eagle Owl Bubo bengalensis (Franklin, 1831) in and around Pondicherry, southern India

© Zoo Outreach Organisation; www.zoosprint. org Manuscript 1425; Received 26 August 2005; Finally accepted 01 April 2006; Date of publication 21 April 2006 2231 May 2006 | ISSN 0973-2535 (Print edition); 0973-2551 (Online edition) The Genus Bubo contains some of the world’s largest species of owls, most, if not all of which, are tertiary consumers and excellent indicators of ecosystems they inhabit. Authorities differ in opinion about the number of species in the Genus which varies from 16 (http://owlpages.com/species/Default.htm; del Hoyo et al., 1999) to 18 (http://www.omne-vivum.com/b/ 3399.htm). Of all these, the prey spectrum and dynamics of only two (both Northern Hemisphere species) have been studied in detail viz., the Eurasian Eagle Owl Bubo bubo (Bayle et al., 1987; Blondel & Badan, 1976; Choussy, 1971; Donazar, 1987; Herrera & Hiraldo, 1976; Martinez et al., 1992; Martinez, 2003, Mysterud & Dunker, 1982; Orsini, 1985; Penteriani et al., 2002; Simeonov et al., 1998; Uttendorfer, 1939, 1952) and the American Great Horned Owl Bubo virginianus (Adamcik et al., 1978; Austing & Holt, 1966; Houston & Francis, 1995; McInvaille & Keith, 1974; Rohner, 1995, 1996, 1997; Rohner & Hunter, 1996; Rohner & Smith, 1996). Like most others of its genus, the Indian Eagle Owl Bubo bengalensis (also known as the Indian Great Horned Owl, Bengal Eagle Owl, Rock Horned Owl and Rock Eagle Owl, and till recently considered a subspecies of Bubo bubo) has received scant attention. A preliminary report on the prey of the species exists (Ramanujam, 2001), but this left a lot to be desired as, in addition to other shortcomings, it relied fundamentally on pellet analysis of just two unconnected subjects and no effort was made to specifically identify the rodent prey base. All other literature concerning the diet of the species are short notes of casual observations (Ali, 1969, 1996; Ali & Ripley, 1987; Barnes, 1981; Dharmakumarasinhji, 1954; Grewal, 1995; Jerdon, 1862; Whistler, 1986). This report aims to identify the prey spectrum of Bubo bengalensis, categorise components of prey consumed (and in the process identify the main food resources), review fluctuations in prey occurence and corelate the primary prey base to climatic factors and plant phenology of the region.
0 71 0 . 37 81 v 1 [ cs . IT ] 19 O ct 2 00 7 Wireless Network Information Flow

We present an achievable rate for general deterministic relay networks, with broadcasting at the transmitters and interference at the receivers. In particular we show that if the optimizing distribution for the information-theoretic cut-set bound is a product distribution, then we have a complete characterization of the achievable rates for such networks. For linear deterministic finite-field models discussed in a companion paper [3], this is indeed the case, and we have a generalization of the celebrated max-flow min-cut theorem for such a network.
Impact of Channel Variation on One-Class Learning for Spoof Detection

Margin-based losses, especially one-class classification loss, have improved the generalization capabilities of countermeasure systems (CMs), but their reliability is not tested with spoofing attacks degraded with channel variation. Our experiments aim to tackle this in two ways: first, by investigating the impact of various codec simulations and their corresponding parameters, namely bit-rate, discontinuous transmission (DTX), and loss, on the performance of the one-class classification-based CM system; second, by testing the efficacy of the various settings of margin-based losses for training and evaluating our CM system on codec simulated data. Multi-conditional training (MCT) along with various data-feeding and custom mini-batching strategies were also explored to handle the added variability in the new data setting and to find an optimal setting to carry out the above experiments. Our experimental results reveal that a strict restrain over the embedding space degrades the performance of the one-class classification model. MCT relatively improves performance by 35.55\%, and custom mini-batching captures more generalized features for the new data setting. Whereas varying the codec parameters made a significant impact on the performance of the countermeasure system.
Parallel Graph Partitioning for Complex Networks

Processing large complex networks like social networks or web graphs has attracted considerable interest. To do this in parallel, we need to partition them into pieces of roughly equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for complex networks. Here we address this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering. By introducing size constraints, label propagation becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning. This way we exploit the hierarchical cluster structure present in many complex networks. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsest graph. The resulting system is both more scalable and achieves higher quality than state-of-the-art systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. As an example, our algorithm partitions a web graph with 3.3 G edges in 16 seconds using 512 cores of a high-performance cluster while producing a high quality partition—none of the competing systems can handle this graph on our system.
Imitation learning for vision-based lane keeping assistance Master ’ s thesis in Systems , Control and Mechatronics

Autonomous driving is currently an active area within the automotive industry where many different solutions are being proposed, for example data driven methods such as deep learning. The aim of this work is to investigate whether neural networks trained by means of imitation learning are capable of acting as an end-toend solution for lane keeping assistance, using a single grayscale front view camera. The capability and robustness of such models, when exposed to both regular and unexpected driving conditions, have therefore been evaluated. Furthermore, the concept of domain changes, whether knowledge learned from real world driving scenarios is transferable to simulation environments, is investigated. The model proposed in this work is based on a state–of–the–art neural network architecture. The data from which it has learned stems from human driving collected on highway-like roads under various conditions, such as time of day and weather. The model has been evaluated in simulation environments with realistic road geometries. The results from this work clearly shows that a neural network trained on real driving data generalizes the concept of driving across different domains, performing well in simulated environments. Additionally, at least for simulated environments, empirical analysis on the model robustness seems to contradict earlier results, which suggests that training a model from a single front view camera is insufficient in order to achieve lane keeping functionality. Based on the findings, the conclusion is drawn that neural networks indeed exhibits some capability to work as an end–to–end solution to specific driving scenarios. However, more work, most notably real vehicle tests, are required in order to assert this completely.
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

— In real-world applications, images may be not only sub-sampled but also heavily compressed thus often containing various artifacts. Simple methods for enhancing the resolution of such images will exacerbate the artifacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artifact removal and resolution enhancement. Based on maximum a posterior inference for estimating a clean low-resolution (LR) input image and a clean high resolution (HR) output image from down-sampled and compressed observations, we have designed a CISR architecture consisting of two deep neural network modules: the artifact reduction module (ARM) and resolution enhancement module (REM). ARM and REM work in parallel with both taking the compressed LR image as their inputs, while they also work in series with REM taking the output of ARM as one of its inputs and ARM taking the output of REM as its other input. A unique property of our CSIR system is that a single trained model is able to super-resolve LR images compressed by different methods to various qualities. This is achieved by exploiting deep neural networks capacity for handling image degradations, and the parallel and series connections between ARM and REM to reduce the dependency on specific degradations. ARM and REM are trained simultaneously by the deep unfolding technique. Experiments are conducted on a mixture of JPEG and WebP compressed images without a priori knowledge of the compression type and compression factor. Visual and q uantitative comparisons demonstrate the superiority of our method over state-of-the-art super resolution methods, especially for heavily compressed images. Ablation studies are also conducted to show the impacts of different parts in the proposed model.
M ay 2 01 5 Network cloning unfolds the e ff ect of clustering on dynamical processes

We introduce networkL-cloning, a technique for creating ensembles of random netw orks from any given realworld or artificial network. Each member of the ensemble is an L-cloned network constructed from L copies of the original network. The degree distribution of an L-cloned network and, more importantly, the degree-degree correlation between and beyond nearest neighbors are ident ical to those of the original network. The density of triangles in anL-cloned network, and hence its clustering coe fficient, is reduced by a factor of L compared to those of the original network. Furthermore, the density o f lo ps of any fixed length approaches zero for sufficiently large values of L. Other variants ofL-cloning allow us to keep intact the short loops of certain lengths. As an application, we employ these network cloning methods to investigate the e ff ct of short loops on dynamical processes running on networks and to inspect th accuracy of corresponding tree-based theories. We demonstrate that dynamics on L-cloned networks (with su fficiently largeL) are accurately described by the so-called adjacency tree-based theories, examples of whic h include the message passing technique, some pair approximation methods, and the belief propagation algorit hm used respectively to study bond percolation, SI epidemics, and the Ising model.
Reliable and Secure Multishot Network Coding using Linearized Reed-Solomon Codes

Multishot network coding is considered in a worst-case adversarial setting in which an omniscient adversary with unbounded computational resources may inject erroneous packets in up to t links, erase up to $\rho$ packets, and wire-tap up to $\mu$ links, all throughout $\ell$ shots of a (random) linearly-coded network. Assuming no knowledge of the underlying linear network code (in particular, the network topology and underlying linear code may change with time), a coding scheme achieving zero-error communication and perfect secrecy is obtained based on linearized Reed-Solomon codes. The scheme achieves the maximum possible secret message size of $\ell n'-2t-\rho-\mu$ packets, where $n'$ is the number of outgoing links at the source, for any packet length $m \geq n'$ (largest possible range), with only the restriction that $\ell \lt q$ (size of the base field). By lifting this construction, coding schemes for non-coherent communication are obtained with information rates close to optimal for practical instances. A Welch-Berlekamp sum-rank decoding algorithm for linearized Reed-Solomon codes is provided, having quadratic complexity in the total length $n = \ell n'$, and which can be adapted to handle not only errors, but also erasures, wire-tap observations and non-coherent communication.
RESTUDY ON THE SILURIAN CONODONT BIOSTRATIGRAPHY OF THE BAIZITIAN SECTION IN YANBIAN COUNTY,SICHUAN

Two conodont zones,Caudicriodus woschmidti Zone and P.eopennatus Zone are first described from the Baizitian Section by the present authors.Previous reports that Ozarkodina crispa and Ozarkodina eosteinhornensis were present at this section can not be verified through four times of sample collection and analysis.Pridoli deposits of the Baizitian section could be incomplete,and this section is not suitable for the establishment of a new stage.The contact between the Silurian and Devonian strata at this section could be a pseudoconformity.Conodont zones and Series boundary within the Silurian are briefly discussed in this paper.The present authors conclude that the six new Silurian Stages named by Jin et al.(2005)can not be used in China.