Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning algorithms

This chapter presents many extensions and variations of the motion planning problem considered in Chapters 3 to 6. Each one of these can be considered as a “spin-off” that is fairly straightforward to describe using the mathematical concepts and algorithms introduced so far. Unlike the previous chapters, there is not much continuity in Chapter 7. Each problem is treated independently; therefore, it is safe to jump to whatever sections in the chapter you find interesting without fear of missing important details. In many places throughout the chapter, a state space X will arise. This is consistent with the general planning notation used throughout the book. In Chapter 4, the C-space, C , was introduced, which can be considered as a special state space: It encodes the set of transformations that can be applied to a collection of bodies. Hence, Chapters 5 and 6 addressed planning in X = C . The C-space alone is insufficient for many of the problems in this chapter; therefore, X will be used because it appears to be more general. For most cases in this chapter, however, X is derived from one or more C-spaces. Thus, C-space and state space terminology will be used in combination. Time-varying problems This section brings time into the motion planning formulation. Although the robot has been allowed to move, it has been assumed so far that the obstacle region O and the goal configuration, q g ∈ C free , are stationary for all time.
Reconciling Predictive Coding and Biased Competition Models of Cortical Function

A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model.
On the dynamic pattern analysis, discovery and recognition

1. Background In the real world, most of objects can be treated as one kind of patterns in general. Although patterns are ubiquitous and of miscellaneous forms, they can be generally divided into two categories: static and dynamic patterns. In nature, static patterns tend to unchange dramatically upon generation whilst dynamic patterns always tend to change in a wide range. For instance, printed characters can be regarded as a class of static patterns since upon generation they are not altered considerably despite the slight deformation by the noise corruption. In contrast, handwriting artifacts of a word may vary significantly. On the one hand, different people have different writing styles, which results in different shapes for the same word. On the other hand, one could write the same word in various forms at different times. Thus, handwriting artifacts become a class of dynamic patterns. In terms of pattern analysis, discovery and recognition, dynamic patterns lead to more challenging problems than static patterns. In artificial intelligence, there are many challenging problems, ranging from biometrics to bioinformatics, relevant to various dynamic patterns, and their solutions rely on effective dynamic pattern analysis, discovery and recognition techniques [1]. As a prominent characteristic, the variability of dynamic patterns in the same class becomes gradually larger, which leads to tremendous difficulties in discriminating between dynamic patterns belonging to different classes. In addition, dynamic patterns often convey mixing information, which is often hardly separable, so that the direct use of the mixing information can result in the inadequate performance for a specific task due to the interference of irrelevant information. For example, it is well known that a speech signal, a type of dynamic patterns, generally contains three types of information; i.e., linguistic, speaker‐specific, and environmental information [2]. A speech recognition task demands only linguistic information rather than speaker‐specific and environmental information; an ideal system should recognize speech regardless of speakers. Conversely, a speaker recognition task needs speaker‐specific information; an ideal speaker recognition system should carry out the voice‐ based authentication regardless of linguistic information or whatever a speaker utters. As a consequence, the direct use of all mixing information in either speech or speaker recognition hinders either of two systems from producing higher performance. The characteristics of dynamic patterns give rise to several challenging problems as follows: how to generate a parsimonious yet robust representation of dynamic patterns that tolerates their intra‐ and inter‐class variability, how to …
rDCF: A Relay-Enabled Medium Access Control Protocol for Wireless Ad Hoc Networks

It is well known that IEEE 802.11 provides a physical layer multirate capability and, hence, MAC layer mechanisms are needed to exploit this capability. Several solutions have been proposed to achieve this goal. However, these solutions only consider how to exploit good channel quality for the direct link between the sender and the receiver. Since IEEE 802.11 supports multiple transmission rates in response to different channel conditions, data packets may be delivered faster through a relay node than through the direct link if the direct link has low quality and low rate. In this paper, we propose a novel MAC layer relay-enabled distributed coordination function (DCF) protocol, called rDCF, to further exploit the physical layer multirate capability. We design a protocol to assist the sender, the relay node, and the receiver to reach an agreement on which data rate to use and whether to transmit the data through a relay node. Considering various issues, such as, bandwidth utilization, channel errors, and security, we propose techniques to further improve the performance of rDCF. Simulation results show that rDCF can significantly reduce the packet delay, improve the system throughput, and alleviate the impact of channel errors on fairness
Histogram based normalization in the acoustic feature space

We describe a technique called histogram normalization that aims at normalizing feature space distributions at different stages in the signal analysis front-end, namely the log-compressed filterbank vectors, cepstrum coefficients, and LDA (local density approximation) transformed acoustic vectors. Best results are obtained at the filterbank, and in most cases there is a minor additional gain when normalization is applied sequentially at different stages. We show that histogram normalization performs best if applied both in training and recognition, and that smoothing the target histogram obtained on the training data is also helpful. On the VerbMobil II corpus, a German large-vocabulary conversational speech recognition task, we achieve an overall reduction in word error rate of about 10% relative.
Unsupervised Relation Extraction From Web Documents

The IDEX system is a prototype of an interactive dynamic Information Extraction (IE) system. A user of the system expresses an information request in the form of a topic description, which is used for an initial search in order to retrieve a relevant set of documents. On basis of this set of documents, unsupervised relation extraction and clustering is done by the system. The results of these operations can then be interactively inspected by the user. In this paper we describe the relation extraction and clustering components of the IDEX system. Preliminary evaluation results of these components are presented and an overview is given of possible enhancements to improve the relation extraction and clustering components.
1 Cooperation , Transparency , and the Rise of Support Vector Machines in E-Discovery : Issues Raised by the Need to Classify Documents as Either Responsive or Nonresponsive

Exponential increases in the volume of electronically stored information are necessitating new thinking on the part of the greater legal community, including a movement away from linear or manual review, as well as away from reliance on keyword searching as the sole automated means to handle e-discovery search and document review requirements. Increasingly, lawyers are becoming more familiar with certain advanced forms of search techniques, including those utilizing machine learning. The landmark US opinion in da Silva Moore v. Publicus Groupe SA, issued in February 2012, giving a judicial imprimatur to use of “predictive coding” and other sophisticated iterative sampling techniques in satisfaction of discovery obligations, should assist in paving the way toward greater acceptance of these new methods. Almost all of these machine learning processes are based on support vector machines or related algorithms, which at first glance seem unapproachably complex. The basic intuitions behind their functionality are not nearly as daunting. After providing relevant background on traditional notions of the discovery process and the emergence of a need for more sophisticated forms of artificial intelligence to solve e-discovery challenges, this paper will explain the mathematical intuition behind support vector machines, so that lawyers can more fully grasp the implications of this new technology. In particular, this paper suggests that support vector machine technology necessarily requires lawyers paying heightened attention to notions of cooperation and transparency, in light of the collaborative, iterative interaction with coding software, and the need for sharing sets of non-responsive documents in order that use of the technology is optimized.
A LEARNING ALGORITHM FOR COMMUNICATING MARKOV DECISION PROCESSES WITH UNKNOWN TRANSITION MATRICES

This study is concerned with finite Markov decision processes (MDPs) whose state are exactly observable but its transition matrix is unknown. We develop a learning algorithm of the reward-penalty type for the communicating case of multi-chain MDPs. An adaptively optimal policy and an asymptotic sequence of adaptive policies with nearly optimal properties are constructed under the average expected reward criterion. Also, a numerical experiment is given to show the practical effectiveness of the algorithm.
Reusable Web Sites for Content Delivery

Introduction The rapid advancement of the World Wide Web and hypermedia systems puts educators more and more under pressure to provide some of their course material on the web. But given the time constraints of the faculty and the necessary skills in electronic media production, it cannot be expected that many useful systems will be built and used by them. Most available systems support a great deal of activity that goes into the development of an electronically enhanced course with features which support testing, interfacing with other university-based systems, tracking of students, assignments, chat rooms and some instruction. However, explicit support for the development of a web-based environment for learning is rare at best. One of the exceptions is an adaptive hypermedia system provide navigation support that adapts to the user's action and learning (Brusilowski et al., 1997). However, the provided navigation space tends to constrain the learner’s possibilities for exploration.
Users in cells: A data traffic analysis

We present a large-scale cell based measurement analysis of the user behavior in a live operational HSDPA network. The motivations are: first, to understand the statistical properties of users in cells for refining network planning procedures; and second, to provide realistic traffic models for simulations of cellular packet-oriented networks. We provide an analysis of mean cell load over daytime, as well as models for short-term cell load in terms of user activity and throughput. Furthermore, an evaluation of user-sessions with respect to duration and mean throughput is given. Our findings lead to four different models reflecting realistic user-traffic load of cellular networks. To verify the concept we present respective simulations which investigate multiuser-scheduling in an LTE-network. They show that conventional simulation settings can lead to an overestimation of performance.
Design and Implementation of a Hardware Module for MIMO Decoding in a 4G Wireless Receiver

Future 4th Generation (4G) wireless multiuser communication systems will have to provide advanced multimedia services to an increasing number of users, making good use of the scarce spectrum resources. Thus, 4G systemdesign should pursue both highertransmission bit rates and higher spectral efficiencies. To achieve this goal,multiple antenna systems are called to play a crucial role. In this contribution we address the implementation in FPGAs of a multiple-input multiple-output (MIMO) decoder embedded in a prototype of a 4G mobile receiver. This MIMO decoder is part of a multicarrier code-division multiple-access (MC-CDMA) radio system, equipped with multiple antennas at both ends of the link, that is able to handle up to 32 users and provides raw transmission bit-rates up to 125 Mbps. The task of the MIMO decoder is to appropriately combine the signals simultaneously received on all antennas to construct an improved signal, free of interference, from which to estimate the transmitted symbols. A comprehensive explanation of the complete design process is provided, including architectural decisions, floating-point to fixedpoint translation, and description of the validation procedure. We also report implementation results using FPGA devices of the Xilinx Virtex-4 family.
A Survey on Algorithms for Euclidean Matching

Let G = (V,E) be a weighted undirected graph with vertex set V , edge set E and a weight function d. Thus, d(u, v) denotes the weight of any edge (u, v) ∈ E. A matching M ⊆ E is a collection of edges such that every node in V is incident to at most one edge in M . The matching is perfect if every node in V is incident to exactly one edge in M . The cost of the matching is given by ∑ (u,v)∈M d(u, v). The Euclidean Bipartite Matching Problem (EBM) is defined as follows: Given a set R of n red points and another set B of n blue points in the plane, let G = (R ⋃ B,E) be the complete weighted bipartite graph with the property that there exists an edge between two points if and only if they are of different colors. Moreover, for each u ∈ R and v ∈ B, the weight d(u, v) of the edge (u, v) is given by the euclidean distance between the points u and v. The objective is to find a perfect matching in this graph whose cost is minimum. The Euclidean Non-bipartite Matching Problem (ENM) can be defined similarly. Let V be a set of 2n points in the plane. Consider the complete weighted graph G = (V,E) where the weight d(u, v) of an edge is given by the euclidean distance between the points u and v. Compute a perfect matching in this graph with minimum cost.
Evolution of curvature perturbation in generalized gravity theories

Using the cosmological perturbation theory in terms of the δN formalism, we find the simple formulation of the evolution of the curvature perturbation in generalized gravity theories. Compared with the standard gravity theory, a crucial difference appears in the end-boundary of the inflationary stage, which is due to the non-ideal form of the energy–momentum tensor that depends explicitly on the curvature scalar. Recent study shows that ultraviolet-complete quantum theory of gravity (Hořava–Lifshitz gravity) can be approximated by using a generalized gravity action. Our paper may give an important step in understanding the evolution of the curvature perturbation during inflation, where the energy–momentum tensor may not be given by the ideal form due to the corrections from the fundamental theory.
Lifetime Information for Model-Based Diagnosis

In this paper, we propose an extension to model-based diagnosis that includes lifetime information. A probabilistic temporal knowledge representation consisting of a probabilistic ATMS with temporally changing probabilities represents lifetime information. Adding statistical survival models to such a knowledge representation gives a more powerful reasoning tool that can evaluate life expectancy under speciic operating conditions. Not only does this extension speed up diagnosis but it also detects faults traditional diagnostic engines cannot.
The Intersection Conics of Six Straight Lines

We investigate and visualize the manifold M of planes that intersect six straight lines of real projective three space in points of a conic section. It is dual to the apex-locus of the cones of second order that have six given tangents. In general M is algebraic of dimension two and class eight. It has 30 single and six double lines. We consider special cases, derive an algebraic equation of the manifold and give an ecient algorithm for the computation of solution planes.
Cloning of the GABAB Receptor Subunits B1 and B2 and their Expression in the Central Nervous System of the Adult Sea Lamprey

In vertebrates, γ-aminobutyric acid (GABA) is the main inhibitory transmitter in the central nervous system (CNS) acting through ionotropic (GABAA) and metabotropic (GABAB) receptors. The GABAB receptor produces a slow inhibition since it activates second messenger systems through the binding and activation of guanine nucleotide-binding proteins [G-protein-coupled receptors (GPCRs)]. Lampreys are a key reference to understand molecular evolution in vertebrates. The importance of the GABAB receptor for the modulation of the circuits controlling locomotion and other behaviors has been shown in pharmacological/physiological studies in lampreys. However, there is no data about the sequence of the GABAB subunits or their expression in the CNS of lampreys. Our aim was to identify the sea lamprey GABAB1 and GABAB2 transcripts and study their expression in the CNS of adults. We cloned two partial sequences corresponding to the GABAB1 and GABAB2 cDNAs of the sea lamprey as confirmed by sequence analysis and comparison with known GABAB sequences of other vertebrates. In phylogenetic analyses, the sea lamprey GABAB sequences clustered together with GABABs sequences of vertebrates and emerged as an outgroup to all gnathostome sequences. We observed a broad and overlapping expression of both transcripts in the entire CNS. Expression was mainly observed in neuronal somas of the periventricular regions including the identified reticulospinal cells. No expression was observed in identifiable fibers. Comparison of our results with those reported in other vertebrates indicates that a broad and overlapping expression of the GABAB subunits in the CNS is a conserved character shared by agnathans and gnathostomes.
Reducing the computational footprint for real-time BCPNN learning

The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware.
An Improved Method for Multi-class Support Vector Machines

Based on analyzing the advantages and disadvantages of existing multi-class support vector machines, we construct an improved multi-class support vector machines based on binary tree structure, adopting a new metrics to determine the classification order which determines each sub-classifier and its location, the new metrics synthesizes mixed degree and distance between classes. Then we do a measuring experiment using the improved multi-class support vector machines, which identifies five major P2P IPTV applications, the results show that our method is better than one-against-all and one-against-one method.
Joint equalization and interference suppression for high data rate wireless systems

Enhanced Data Rates for Global Evolution (EDGE) is currently being standardized as an evolution of GSM in Europe and of IS-136 in the United States as an air interface for high speed data services for third generation mobile systems. In this paper, we study space-time processing for EDGE to provide interference suppression. We consider the use of two receive antennas and propose a joint equalization and diversity receiver. This receiver uses feedforward filters on each diversity branch to perform minimum mean-square error cochannel interference suppression, while leaving the intersymbol interference to be mitigated by the subsequent equalizer. The equalizer is a delayed decision feedback sequence estimator, consisting of a reduced-state Viterbi processor and a feedback filter. The equalizer provides soft output to the channel decoder after deinterleaving. We describe a novel weight generation algorithm and present simulation results on the link performance of EDGE with interference suppression. These results show a significant improvement in the signal-to-interference ratio (SIR) performance due to both diversity (against fading) and interference suppression. At a 10% block error rate, the proposed receiver provides a 20 dB improvement in SIR for both the typical urban and hilly terrain profiles.
Bootstrapping pronunciation dictionaries: practical issues

Bootstrapping techniques are an efﬁcient way to develop electronic pronunciation dictionaries [1, 2], but require fast system response to be practical for medium-to-large lexicons. In ad-dition, user errors are inevitable during this process, and it is useful if automatic means can be used to assist in the search for such errors. We describe how the Default&Reﬁne grapheme-to-phoneme rule extraction algorithm [3] can be adapted to meet both of these goals. Experimental results demonstrate the utility of these methods.
Caveolin-1: an ambiguous partner in cell signalling and cancer

• Introduction • The caveolins • Caveolin‐1 in cell physiology ‐ Caveolin‐1 distribution ‐ Caveolin‐1 and internalization ‐ Caveolin‐1 and cholesterol ‐ Regulation of caveolin‐1 expression ‐ Caveolin‐1 in signal transduction ‐ Alternative mechanisms of caveolin‐1‐mediated control in signalling ‐ Control of transcription ‐ Other modes of control ‐ Cell proliferation ‐ Cell death and apoptosis • Caveolin‐1 in cancer ‐ The tumour suppressor hypothesis ‐ Caveolin‐1 in multi‐drug resistance and metastasis • Concluding remarks