Miracle-dz/UniFAR-FTUs huggingface dataset:
Probability on Groups : Random Walks and Invariant Diffusions

968 NOTICES OF THE AMS VOLUME 48, NUMBER 9 W hat do card shuffling, volume growth, and Harnack inequalities have to do with each other? They all arise in the study of random walks on groups. Probability on groups is concerned with probability measures and random processes whose properties are dictated in part by an underlying group structure. It is a diverse area where one finds both sophisticated theories and the analysis of concrete problems. Although there are many other fascinating examples, we will focus on random walks and invariant diffusions, which both are processes with independent stationary increments. Random walks proceed by jumps, whereas diffusions have continuous paths. The two share important properties but differ in some aspects, including the nature of the typical underlying group: finitely generated for random walks, connected for diffusions. We will focus on very basic properties of these processes and leave out many developments, some of which can be found in [V+], [W]. Our aim is to present the theory of random walks and invariant diffusions on general groups, with an emphasis on the relations with algebra, analysis, and geometry. By studying these processes we hope to learn something about the underlying group and related objects. For instance, certain properties of the solutions of the Laplace and heat diffusion equations on the universal cover of a compact manifold relate to the behavior of random walks on the fundamental group. From this viewpoint, understanding the basic properties of random walks on large classes of groups is more important than the detailed study of specific examples. The accumulated knowledge concerning the example of d-dimensional Brownian motion (including the beautiful recent advances by Lawler, Schramm, and Werner) serves as a remote, perhaps unreachable, ideal. It provides natural questions, ideas, and insights, but one may have to settle for much less on general groups. This article has two parts: one treating random walks, the other diffusions. The two parts are related in many ways—at the level of ideas as well as on firmer mathematical ground—and more so than this article can possibly convey. Behind the difference in settings, ranging from the symmetric group Sn to the Lie group SLn(R) to the infinite dimensional torus T∞, there is unity in the problems that are discussed, and any substantial progress in one particular context sheds light on the entire subject.
License Plate Detection on Autonomous Surveillance Systems

Autonomous surveillance systems are widely used as an important tool for security control in public areas. Among the numerous targets of the autonomous surveillance system, license plate recognition can help to find stolen vehicles or even to identify the terrorist in cars. Uneven lighting condition and incorrect viewing direction happen all the time in real applications for an autonomous surveillance system. In this paper, we propose a novel method for license plate localization. The features of a license plate are first generated in the preprocessing step through the morphology operations. Then, apply the AdaBoost algorithm to select some weak classifiers from the weak classifier space to construct a strong classifier. Furthermore, the detection results are used as the feedback to the autonomous surveillance system to modify the location of the robot. This feedback correction mechanism can solve the problem of out-of-plane rotations and get better detection results. The exact detection results would be very useful for further recognition tasks. Experimental results show that the proposed method can efficiently detect license plates under different illuminations and viewing directions.
Mapping Cluster Mass Distributions via Gravitational Lensing of Background Galaxies

We present a new method for measuring the projected mass distributions of galaxy clusters, based solely on the gravita-tional lens ampliication of background galaxies by the cluster potential eld. The gravitational ampliication is measured by comparing the joint distribution in redshift and magnitude of galaxies behind the cluster with that of the average distribution of eld galaxies. Lensing shifts the magnitude distribution in a characteristic redshift{dependent way, and simultaneously dilutes the surface density of galaxies. These eeects oppose, with the latter dominating at low redshift and the former at high redshift, owing to the curvature of the galaxy luminosity function. Lensing by a foreground cluster thus induces an excess of bright high-redshift galaxies, from which the lens ampliication may be inferred. We show that the total ampliication is directly related to the surface mass density in the weak eld limit, and so it is possible to map the mass distribution of the cluster. The method is shown to be limited by discreteness noise and galaxy clustering behind the lens. Galaxy clustering sets a lower limit to the error along the redshift direction, but a clustering independent lensing signature may be obtained from the magnitude distribution at xed redshift. Provided the luminosity function deviates from a pure power law, the lens{induced brightening can be measured directly by comparison with the eld. In the limit that galaxy luminosities are independent of environment, this method is only shot{noise limited. Statistical techniques are developed for estimating the surface mass density of the cluster. We extend these methods to account for any obscuration by cluster halo dust, which may be mapped independently of the dark matter. We apply the method to a series of numerical simulations and show the the feasibility of the approach. We consider the use of approximate redshift information, and show how the mass estimates are degraded; nally we discuss the data required to map the dark matter in clusters from photometry alone.
Experimental Performance Evaluation of a Cooperative ARQ Scheme using OpenMAC

This paper experimentally validates the efficiency of Cooperative Automatic Retransmission reQuest (C-ARQ) versus traditional ARQ protocols in terms of delay, throughput, and energy consumption. We report on our development of a testbed for obtaining these results, which is based on OpenMAC, a platform we have designed for rapid prototyping of MAC algorithms. With this work we demonstrate that OpenMAC can provide high levels of abstraction and flexibility while effectively operating novel protocol drivers under field testing by sustaining real-time execution of device-independent software prototypes.
1 Are Software Metrics Really Any Use ?

This statement, when applied to software engineering, reflects harshly upon the software engineer that believes themselves to really be a computer scientist. The fundamentals of any science lie in its ability to prove or refute theory through observation. Software engineering is no exception to this yet, to date, we have failed to provide satisfactory empirical evaluations of many of the theories we hold as truths.
A Neural-Network Based DDoS Detection System Using Hadoop and HBase

This paper presents a detection system for the Distributed Denial of Service (DDoS) attack based on neural network, which is implemented in the Apache Hadoop cluster and the HBase system. While there are already many approaches for the DDoS detection, there are two main challenges: the learning capability of a DDoS detection system and the ability to process a huge unstructured dataset. The main contribution of this paper is to develop a DDoS detection system with learning capability to adapt to new types of DDoS attacks and ability to store and analyze a huge unstructured dataset collected from network logs. Particularly, a neural network architecture is designed for the DDoS detection system, and a list of training samples is developed to train the neural network. This approach is validated with a series of generated datasets of different scenarios. It was shown that the system with the well-trained neural network is able to detect DDoS attacks efficiently and successfully.
Hubble Space Telescope photometry of globular clusters in the Sombrero galaxy

We explore the rich globular cluster (GC) system of the nearby Sa galaxy M104, the ‘Sombrero’ (NGC 4594), using archive Wide Field Planetary Camera 2 data. The GC colour distribution is found to be bimodal at the >99 per cent confidence level, with peaks at and . The inferred metallicities are very similar to those of GCs in our Galaxy and M31. However, the Sombrero reveals a much enhanced number of red (metal-rich) GCs compared to other well-studied spirals. Because the Sombrero is dominated by a huge bulge and only has a modest disc, we associate the two subpopulations with the halo and bulge components, respectively. Thus our analysis supports the view that the metal-rich GCs in spirals are associated with the bulge rather than with the disc. The Sombrero GCs have typical effective (half-light) radii of ∼2 pc with the red ones being ∼30 per cent smaller than the blue ones. We identify many similarities between the GC system of the Sombrero and those of both late-type spirals and early-type galaxies. Thus both the GC system and the Hubble type of the Sombrero galaxy appear to be intermediate in their nature.
Amplification of Compression Waves in Clean and Bubbly Liquid

The theoretical investigation is carried out to describe the effect of increase of pressure waves amplitude in clean and bubbly liquid. The goal of the work is to capture the regime of multiple magnification of acoustic and shock waves in the liquid, which enables to get appropriate conditions to enlarge collapses of micro-bubbles. The influence of boundary conditions and frequency of the governing acoustic field is studied for the case of the cylindrical acoustic resonator. It has been observed the formation of standing waves with large amplitude at resonant frequencies. The interaction of the compression wave with gas and vapor bubbles is investigated for the convergent channel. It is shown theoretically that the chemical reactions, which occur inside gas bubbles, provide additional impulse to the wave, that affect strongly on the collapses of the vapor bubbles. Keywords— acoustics, cavitation, detonation, shock waves
Sparse Principal Component Analysis via Rotation and Truncation

Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, while still covering the data subspace as much as possible. In contrast to most existing work that addresses the problem by adding sparsity penalties on various objectives of PCA, we propose a new method, sparse PCA via rotation and truncation (SPCArt), which finds a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation. The algorithm of SPCArt consists of three alternating steps: 1) rotating the PCA basis; 2) truncating small entries; and 3) updating the rotation matrix. Its performance bounds are also given. The SPCArt is efficient, with each iteration scaling linearly with the data dimension. Parameter choice is simple, due to explicit physical explanations. We give a unified view to several existing sparse PCA methods and discuss the connections with SPCArt. Some ideas from SPCArt are extended to GPower, a popular sparse PCA algorithm, to address its limitations. Experimental results demonstrate that SPCArt achieves the state-of-the-art performance, along with a good tradeoff among various criteria, including sparsity, explained variance, orthogonality, balance of sparsity among loadings, and computational speed.
Aligning Sentences in Bilingual Corpora Using Lexical Information

In this paper, we describe a fast algorithm for aligning sentences with their translations in a bilingual corpus. Existing efficient algorithms ignore word identities and only consider sentence length (Brown et al., 1991b; Gale and Church, 1991). Our algorithm constructs a simple statistical word-to-word translation model on the fly during alignment. We find the alignment that maximizes the probability of generating the corpus with this translation model. We have achieved an error rate of approximately 0.4% on Canadian Hansard data, which is a significant improvement over previous results. The algorithm is language independent.
Text Correction Using Domain Dependent Bigram Models from Web Crawls

The quality of text correction systems can be improved when using complex language models and by taking peculiarities of the garbled input text into account. We report on a series of experiments where we crawl domain dependent web corpora for a given garbled input text. From crawled corpora we derive dictionaries and language models, which are used to correct the input text. We show that correction accuracy is improved when integrating word bigram frequency values from the crawls as a new score into a baseline correction strategy based on word similarity and word (unigram) frequencies. In a second series of experiments we compare the quality of distinct language models, measuring how closely these models reect the frequencies observed in a given input text. It is shown that crawled language models are superior to language models obtained from standard corpora.
Discovering Problem Solutions with Low Kolmogorov Complexity and High Generalization Capability

Many machine learning algorithms aim at nding \simple" rules to explain training data. The expectation is: the \simpler" the rules, the better the generalization on test data (! Occam's razor). Most practical implementations, however, use measures for \simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoo's algorithmic probability. Likewise, most previous approaches (especially those of the \Bayesian" kind) suuer from the problem of choosing appropriate priors. This paper addresses both issues. It rst reviews some basic concepts of algorithmic complexity theory relevant to machine learning, and how the Solomonoo-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for nding \algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. With a given problem, solution candidates are computed by eecient \self-sizing" programs that innuence their own runtime and storage size. The probabilistic search algorithm nds the \good" programs (the ones quickly computing algorithmically probable solutions tting the training data). Simulations focus on the task of discovering \algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural net algorithms. Much remains do be done, however, to make large scale applications and \incremental learning" feasible.
Coupling User Models and System Models : A Modeling Framework for Fault Diagnosis in Complex Systems

Design of effective support tools for human interaction with a complex system requires models of both the human and the system. Models of the human are necessary in order to understand and evaluate the behavioral demands imposed by the system. Models of the system are necessary in order to comprehend the complexities of the work domain. The potential problem with two separate and independent models i s that there is no guarantee that these models will “interface”. One modeling framework’s language and structure may be virtually untranslatable in the terms of the other model, and moreover, in union, not very illuminating in understanding mutual implications for design. In the research depicted in this paper, we propose to wed two models; one, a process model of human cognition, and the other, a model based on a particular descriptive framework of system structure, the Abstraction Hierarchy (Rasmussen 1986).
The Bayesian Ontology Reasoner is BORN!

Bayesian ontology languages are a family of probabilistic ontology languages that allow to encode probabilistic information over the axioms of an ontology with the help of a Bayesian network. The Bayesian ontology language BEL is an extension of the lightweight Description Logic (DL) EL within the above-mentioned framework. We present the system BORN that implements the probabilistic subsumption problem for BEL.
Lateral diffusion and retrograde movements of individual cell surface components on single motile cells observed with Nanovid microscopy

A recently introduced extension of video-enhanced light microscopy, called Nanovid microscopy, documents the dynamic reorganization of individual cell surface components on living cells. 40-microns colloidal gold probes coupled to different types of poly-L-lysine label negative cell surface components of PTK2 cells. Evidence is provided that they bind to negative sialic acid residues of glycoproteins, probably through nonspecific electrostatic interactions. The gold probes, coupled to short poly-L-lysine molecules (4 kD) displayed Brownian motion, with a diffusion coefficient in the range 0.1-0.2 micron2/s. A diffusion coefficient in the 0.1 micron2/s range was also observed with 40-nm gold probes coupled to an antibody against the lipid-linked Thy-1 antigen on 3T3 fibroblasts. Diffusion of these probes is largely confined to apparent microdomains of 1-2 microns in size. On the other hand, the gold probes, coupled to long poly-L-lysine molecules (240 kD) molecules and bound to the leading lamella, were driven rearward, toward the boundary between lamelloplasm and perinuclear cytoplasm at a velocity of 0.5-1 micron/min by a directed ATP-dependent mechanism. This uniform motion was inhibited by cytochalasin, suggesting actin microfilament involvement. A similar behavior on MO cells was observed when the antibody-labeled gold served as a marker for the PGP-1 (GP-80) antigen. These results show that Nanovid microscopy, offering the possibility to observe the motion of individual specific cell surface components, provides a new and powerful tool to study the dynamic reorganization of the cell membrane during locomotion and in other biological contexts as well.
An Information-Theoretic Approach to Automatic Evaluation of Summaries

Until recently there are no common, convenient, and repeatable evaluation methods that could be easily applied to support fast turn-around development of automatic text summarization systems. In this paper, we introduce an information-theoretic approach to automatic evaluation of summaries based on the Jensen-Shannon divergence of distributions between an automatic summary and a set of reference summaries. Several variants of the approach are also considered and compared. The results indicate that JS divergence-based evaluation method achieves comparable performance with the common automatic evaluation method ROUGE in single documents summarization task; while achieves better performance than ROUGE in multiple document summarization task.
Multimedia Explanations in IDEA Decision Support Systems

In this paper, we present a new approach to support the decision of selecting one object out of a set of alternatives. As compared to previous approaches, the distinctive feature of our approach is that neither the user, nor the system need to build a model of user’s preferences. Our proposal is to integrate a system for interactive data exploration and analysis with a multimedia explanation facility. The explanation facility supports the user in understanding unexpected aspects of the data. The explanation generation process is guided by a causal model of the domain that is automatically acquired by the system.
Integrated Analysis of Thyroid Cancer Public Datasets Reveals Role of Post-Transcriptional Regulation on Tumor Progression by Targeting of Immune System Mediators

Papillary thyroid carcinoma (PTC) is a well-differentiated thyroid tumor that accounts for approximately 80% of thyroid cancer cases. On other hand, anaplastic thyroid carcinoma (ATC) is a less frequent, but aggressive subtype, with poor prognosis. MicroRNAs (miRNAs), small non-coding RNAs, have emerged as potent post-transcriptional regulators of gene expression, which modulate the expression of cancer-related genes. Computational analyses estimate that a single miRNA may modulate hundreds of mRNA targets and, at the same time, cooperate with others to regulate one single mRNA transcript. Due to the large number of predicted targets and possible interactions, only a small number of miRNAs have characterized biological roles, and the panorama of miRNA-mediated regulation in thyroid cancer remains to be understood. Taking into consideration the large amount of gene expression data deposited in public databases we aligned miRNA target prediction and gene expression data from public PTC and ATC datasets to construct a network of post-transcriptional regulation in thyroid cancer. After a gene set enrichment analysis we identified signaling pathways and biological processes potentially modulated by the miRNAs deregulated in PTC and ATC. Our results show miRNA-mRNA interaction that could contribute with the de-regulation of key tumor-host mediators, such as extra-cellular matrix molecules, interleukins and interleukin receptors, which could drive a more aggressive behavior and tumor progression. Moreover, our analysis through The Cancer Genome Atlas (TCGA) database revealed that aberrant expression of ECM and cytokines genes is frequent in PTC and is associated with aggressive behavior and decreased overall survival rate. In conclusion, we shed light on the post-transcriptional regulation of gene expression in differentiated and undifferentiated thyroid cancers, revealing a potential role of miRNAs in modulation of tumor-host interaction molecules, particularly ECM molecules and immune system mediators, which could stimulate crosstalk between tumors and the immune system to generate a more aggressive behavior. We propose a novel putative miRNA:mRNA network that could lead to a new path toward functional studies.
Multi-Accent Speech Recognition of Afrikaans, Black and White Varieties of South African English

In this paper we investigate speech recognition performance of systems employing several accent-specific recognisers in parallel for the simultaneous recognition of multiple accents. We compare these systems with oracle systems, in which test utterances are presented to matching accent-specific recognisers, and with accent-independent systems, in which acoustic and language model training data are pooled. Our investigation is based on Afrikaans (AE), Black (BE) and White (EE) accents of South African English. We find that, when accent is classified on a per-utterance basis, parallel systems outperform oracle systems for the AE+EE accent pair while the opposite is observed for BE+EE. When accent identification is carried out on a per-speaker basis, oracle or better performance is obtained for both accent pairs. Furthermore, parallel systems based on multi-accent acoustic modelling, which allows selective crossaccent sharing of acoustic training data, outperform parallel systems using accent-specific acoustic models. The former also yields better performance than accent-independent recognition, which uses pooled acoustic and language models.
Fault tolerance in VLSI circuits

The defects that can occur when manufacturing VLSI ICs and the faults that can result are described. Some commonly used restructuring techniques for avoiding defective components are discussed. Several defect-tolerant designs of memory ICs, logic ICs, and wafer-scale circuits are presented. Yield models for predicting the yield of chips with redundancy are introduced, and the optimal amount of redundancy is determined. <<ETX>>