Miracle-dz/UniFAR-FTUs huggingface dataset:
Control system design guide : a practical guide

Introduction to Controls The Frequency Domain Tuning a Control System Delay in Digital Controllers The z-Domain Six Types of Controllers Disturbance Response Feed-Forward Filters in Control Systems Introduction to Observers in Control Systems Introduction to Modeling Nonlinear Behavior and Time Variation Seven Steps to Developing a Model Encoders and Resolvers Basics of the Electric Servomotor and Drive Compliance and Resonance Position-Control Loops Using the Luenberger Observer in Motion Control. Appendices: Active Analog Implementation of Controller Elements European Symbols for Block Diagrams The Runge-Kutta Method Development of the Bilinear Transformation The Parallel Form of Digital Algorithms Basic Matrix Math.
Languages in international business : a practical guide

Languages in international business - an integrated strategy the language audit effective language training strategies face-to-face tuition self-tuition crash courses computer-assisted language learning selecting language-training materials TV/video - a user-friendly guide business culture training translating and interpreting foreign language "publicity material" languages for the small/medium-sized business in-company language training - three case studies TQM and an integrated language stategy - perfect partners?
Computational issues in fluid construction grammar : a new formalism for the representation of lexicons and grammars

Design Methods for Fluid Construction Grammar. - Tools for Grammar Engineering. - A Reflective Architecture for Robust Language Processing and Learning. - Chunking Constructions. - Expressing Grammatical Meaning with Morphology: A Case Study for Russian Aspect. - Handling Scope in Fluid Construction Grammar: A Case Study for Spanish Modals. - Complex Declension Systems and Morphology in Fluid Construction. - Grammar: A Case Study of Polish. - Field Topology and Information Structure: A Case Study for German Constituent Order. - A Formal Deconstruction of Fluid Construction Grammar. - A Logic Programming Approach to Parsing and Production in Fluid Construction Grammar. - Computational Construction Grammar: Comparing ECG and FCG. - Fluid Construction Grammar and Feature Constraint Logics. - Fluid Construction Grammar in the Brain.
Head direction cells and the neural mechanisms of spatial orientation

Head direction cells -- neurons that fire only when an animal orients its head in a certain direction -- are found in several different brain areas, with different neurons selective for different head orientations; they are influenced by landmarks as well as motor and vestibular information concerning how the head moves through space. These properties suggest that head direction cells play an important role in determining orientation in space and in navigation. Moreover, the prominence, strength, and clarity of head direction signals indicate their importance over the course of evolution and suggest that they can serve as a vital key for understanding brain function. This book presents the latest findings on head direction cells in a comprehensive treatment that will be a valuable reference for students and researchers in the cognitive sciences, neuroscience, computational science, and robotics. The book begins by presenting head direction cell properties and an anatomical framework of the head direction system. It then looks at the types of sensory and motor information that control head direction cell firing, covering topics including the integration of diverse signals; the relationship between head direction cell activity and an animal's spatial behavior; and spatial and directional orientation in nonhuman primates and humans. The book concludes with a tutorial demonstrating the implementation of the continuous attractor network, a computational model of head direction cells, and an application of this approach for a navigational system for mobile robots.
Análisis de algoritmos de detección de objetos para la creación de un prototipo basado en la fusión de dos modelos de reconocimiento

En el presente estudio se muestra una metodología experimental deductiva basada en redes neuronales para el reconocimiento de objetos con el uso de CNN. Nuestro objetivo es generar un prototipo el cual está basado en un mapa de características en combinación con RPN y propuesta de recorte en tronco que usa TNET para la detección 3D, dado por modelos de reconocimiento de objetos de la plataforma KITTI ,enfocados especialmente en AVOD y FPOINTNET, obteniendo una mayor precisión en objetos más pequeños que fácilmente son descartables por la nube de puntos proporcionados por el sensor laser 3d LIDAR HDL-64 pero no por el mapeado de características.
Assessing Opinions in Texts

This chapter focuses on measuring the effect of discourse structure on sentiment analysis. Discourse structure is essential in determining the content conveyed by a text. It affects for example, the temporal structure of a text, the interpretation of anaphoric expressions and pre-suppositions. Discourse structure has been shown to be useful in many NLP applications, such as automatic text summarization and textual entailment. This chapter focuses on measuring the effect of discourse structure on sentiment analysis. It presents the annotation scheme and then describes the corpus. The chapter presents the data, and describes the annotation campaign. It then gives the results of annotation campaign along with the observations. The sentence level is not appropriate for analyzing opinions in discourse since, in addition to objective clauses, a single sentence may contain several opinion clauses that can be connected by rhetorical relations. Keywords: anaphoric expressions; annotation campaign; discourse structure; NLP applications; opinions; presuppositions; sentiment analysis
A Functional Verification Methodology for an Improved Coverage of System-on-Chips

The increasing popularity of System-on-Chip (SoC) circuits results in many new design challenges. One major challenge is to ensure the functional correctness of such complicated circuits. Functional verification is a verification technique used to verify the functional correctness of SoCs. Coverage Directed Test Generation (CDTG) is an essential part of functional verification, where the objective is to generate input stimulus that maximize the coverage of a design. Coverage helps to determine how well the input stimulus verified the design under verification. CDTG techniques analyze coverage results and adapt the input stimulus generation process to improve the coverage. One of the important component of CDTG based tools is the constraint solver. The time efficiency of the verification process depends on the efficiency of the solver. But the constraint solvers associated with CDTG tools require large amount of memory and time to generate input stimuli for SoCs. The solvers cannot generate solutions which are evenly distributed in search space, in order to attain the required coverage. The aim of this thesis is to provide a practical framework that enables the generation of evenly distributed input stimuli. A basic feature of the search space (data set) is that it contains k sub populations or clusters. Partitioning the search space into clusters and generating solutions from the partitions can improve the evenness of the solutions generated by the solver. Hence one of our main contribution is a novel domain partitioning algorithm. The domain partitioning algorithm relies on solution generated by a consistency search algorithm developed for our purpose. The number of partitions (required by the domain partitioning algorithm) is determined by using an algorithm which can find the optimal number of clusters present in a data set. To demonstrate the effectiveness of our approach, we apply our methodology on Constraint Satisfaction Problems (CSPs) and some real life applications.
The role of quality management practices in operational performance An empirical study in a transitional economy

Purpose: This research was conducted to recognize quality management (QM) practices which have relationship with operational performance. Design/methodology/approach: Based on the extensive literature review, a set of quality management practices were identified and a conceptual model was built to explore their relationship with operational performance. An empirical testing at Vietnamese garment enterprises was carried out. Findings: The results of this research showed that four main practices including top management support, human resource management, reporting and analysis of quality data, product/service design and process management are directly and indirectly related to operational performance. In addition, 57.1% variance of operational performance was explained by the QM practices above mentioned. Research limitations/implications: Within the scope of this study, there are some aspects that haven’t been yet considered: (i) there are many other factors that might also have impact on operational performance which are not incorporated in this study, such as, external environment, capital, technology, equipment, information flow, oursourcing, etc., (ii) generalized ability of results would be higher if extending the sample scope to other industries and other countries. These imply directions for further research. Practical implications: To increase operational performance, the QM practices should be applied simultaneously and the structural model was used in this study could play as “a guideline” to orient for the implementation of these practices. Originality/value: This study tried to address some issues that have not been fully examined in the literature. Not only fill these voids, the research model presented the relationship among QM practices and operational performance, along with the measurement instrument validated in this study also provided some insights to the theory system of QM and operational performance.
A pr 2 01 8 Interleaved group products

Let G be the special linear group SL(2, q). We show that if (a1, . . . , at) and (b1, . . . , bt) are sampled uniformly from large subsets A and B of G t then their interleaved product a1b1a2b2 · · · atbt is nearly uniform over G. This extends a result of the first author [Gow08], which corresponds to the independent case where A and B are product sets. We obtain a number of other results. For example, we show that if X is a probability distribution on Gm such that any two coordinates are uniform in G2, then a pointwise product of s independent copies of X is nearly uniform in Gm, where s depends on m only. Extensions to other groups are also discussed. We obtain closely related results in communication complexity, which is the setting where some of these questions were first asked by Miles and Viola [MV13]. For example, suppose party Ai of k parties A1, . , Ak receives on its forehead a t-tuple (ai1, . . . , ait) of elements from G. The parties are promised that the interleaved product a11 . ak1a12 . ak2 . a1t . akt is equal either to the identity e or to some other fixed element g ∈ G, and their goal is to determine which of the two the product is equal to. We show that for all fixed k and all sufficiently large t the communication is Ω(t log |G|), which is tight. Even for k = 2 the previous best lower bound was Ω(t). As an application, we establish the security of the leakage-resilient circuits studied by Miles and Viola [MV13] in the “only computation leaks” model. Preliminary versions of this paper have appeared as [GV15, GV16]. Royal Society 2010 Anniversary Research Professor. Supported by NSF grant CCF-1319206. Work done in part while a visiting scholar at Harvard University, with support from Salil Vadhan’s Simons Investigator grant, and in part while visiting the Simons institute for the theory of computing. Email: viola@ccs.neu. edu.
Evolution Methods for Discrete Minimal Weight Design of Space Trusses with Stability Constraints

This paper provides a comparative study of evolution methods for minimal weight design of space trusses. Recently used genetic algorithms (GA), simulated annealing (SA) and tabu search (TS) methods are observed for metal structures where the truss member profiles are selected from available catalogue values. In this paper, global and local stability problems are considered using a path-following method for non-linear stability investigation. The results of the comparative study are presented for the commonly known numerical test problems. A twenty-four-member shallow dome structure was presented where structural instability constraints and member buckling are considered as well as using linear elastic material property. The effect of the nonlinear material law is compared in optimal design of the ten-bar truss structure and the twenty-five-bar transmission tower using an inverse Ramberg-Osgood material law.
Security Control and Privacy Preservation in RFID enabled Wine Supply Chain

Modern identification procedures such as radio frequency identification (RFID) are able to provide transparency in applications including supply chain, logistics and equipment management. The benefits of visibility and fast identification provided by RFID technology especially in supply chain management (SCM) reduce the risk of counterfeiting (Gao et al., 2004). There are two mainly ways in which RFID technology supports a visible and fast identification processes: 1) RFID allows for new, automated and secure ways to efficiently authenticate physical items; and 2) As many companies invest in networked RFID technology for varying supply chain applications, the item-level data can be gathered in any case (Lehtonen et al., 2006). Despite these benefits, RFID technology is still not widely implemented. The main reasons are, firstly, the difficult are, firstly, the difficult technical aspects of implementation resulting in high setup costs, secondly, growing security and privacy concerns. Our focus in this paper is to discuss the second reason for the low take-up of RFID technology, that is, security and privacy concerns. We argue that without applying maximum security and privacy, trustworthiness between supply chain partners will be minimal. As a result, the effectiveness and collaboration of traditional supply chain environment with RFID technology cannot be achieved. Given that humans cannot read the RFID tags on items and the tags themselves maintain no history of past readings, the challenge of security and privacy in this technology is related to the nature of RFID tags and their functionality (Juels, 2005). A retailer inventory that is labeled with unprotected tags may be monitored and read by unauthorised readers. The inventory data holds significant financial value for commercial organisations and their competitors. Once data has been accessed by unauthorised users, it can be cloned on empty tags, giving rise to the counterfeiting issue. Counterfeiting in the form of cloned or fraudulent RFID tags is the consequence of a lack of security measures and trustworthiness among the supply chain partners when RFID technology is used to automate their business transactions. Privacy violations stem from the fact that when goods are tagged, the manufacturers, retailers and consumers will be able to track the goods beyond point-of-sale (POS) because they have associated data. Even if the tags only contain product codes rather than unique serial numbers, a consumer’s taste in brands “constellation” can betray their identity.
Les trajectoires concomitantes des toitures végétalisées en Amérique du nord : enjeux, connaissances et actions locales

Technique ancienne mais objet d'un consensus croissant, le toit vert semble etre devenu un modele de l'architecture durable. Sa progression rapide dans les grandes villes nord-americaines s'explique davantage par l'action des municipalites que par ses avantages relatifs pour les proprietaires ou l'adhesion des acteurs de la construction. Pression environnementale du gouvernement federal sur les metropoles, diffusion continentale de savoir-faire, fabrication d'un bien commun local et incitations technico-economiques locales ciblees sont les ingredients du deploiement des toits verts sur le continent.
Manifold Optimization-Assisted Gaussian Variational Approximation

Abstract Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference, especially in high-dimensional and large data settings. To control the computational cost, while being able to capture the correlations among the variables, the low rank plus diagonal structure was introduced in the previous literature for the Gaussian covariance matrix. For a specific Bayesian learning task, the uniqueness of the solution is usually ensured by imposing stringent constraints on the parameterized covariance matrix, which could break down during the optimization process. In this article, we consider two special covariance structures by applying the Stiefel manifold and Grassmann manifold constraints, to address the optimization difficulty in such factorization architectures. To speed up the updating process with minimum hyperparameter-tuning efforts, we design two new schemes of Riemannian stochastic gradient descent methods and compare them with other existing methods of optimizing on manifolds. In addition to fixing the identification issue, results from both simulation and empirical experiments prove the ability of the proposed methods of obtaining competitive accuracy and comparable converge speed in both high-dimensional and large-scale learning tasks. Supplementary materials for this article are available online.
A Simple Gap-producing Reduction for the Parameterized Set Cover Problem

Given an $n$-vertex bipartite graph $I=(S,U,E)$, the goal of set cover problem is to find a minimum sized subset of $S$ such that every vertex in $U$ is adjacent to some vertex of this subset. It is NP-hard to approximate set cover to within a $(1-o(1))\ln n$ factor. If we use the size of the optimum solution $k$ as the parameter, then it can be solved in $n^{k+o(1)}$ time. A natural question is: can we approximate set cover to within an $o(\ln n)$ factor in $n^{k-\epsilon}$ time? In a recent breakthrough result, Karthik, Laekhanukit and Manurangsi showed that assuming the Strong Exponential Time Hypothesis (SETH), for any computable function $f$, no $f(k)\cdot n^{k-\epsilon}$-time algorithm can approximate set cover to a factor below $(\log n)^{\frac{1}{poly(k,e(\epsilon))}}$ for some function $e$. This paper presents a simple gap-producing reduction which, given a set cover instance $I=(S,U,E)$ and two integers $k<h\le (1-o(1))\sqrt[k]{\log |S|/\log\log |S|}$, outputs a new set cover instance $I'=(S,U',E')$ with $|U'|=|U|^{h^k}|S|^{O(1)}$ in $|U|^{h^k}\cdot |S|^{O(1)}$ time such that: (1) if $I$ has a $k$-sized solution, then so does $I'$; (2) if $I$ has no $k$-sized solution, then every solution of $I'$ must contain at least $h$ vertices. Setting $h=(1-o(1))\sqrt[k]{\log |S|/\log\log |S|}$, we show that assuming SETH, for any computable function $f$, no $f(k)\cdot n^{k-\epsilon}$-time algorithm can distinguish between a set cover instance with $k$-sized solution and one whose minimum solution size is at least $(1-o(1))\cdot \sqrt[k]{\frac{\log n}{\log\log n}}$. This improves the result of Karthik, Laekhanukit and Manurangsi.
Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning

Unsupervised clustering has broad applications in data stratification, pattern investigation and new discovery beyond existing knowledge. In particular, clustering of bioactive molecules facilitates chemical space mapping, structure-activity studies, and drug discovery. These tasks, conventionally conducted by similarity-based methods, are complicated by data complexity and diversity. We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1.39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space. These band-clusters, displayed by a space-navigation simulation software, band molecules of selected bioactivity classes into individual band-clusters possessing unique sets of common sub-structural features beyond structural similarity. These sub-structural features form the frameworks of the literature-reported pharmacophores and privileged fragments. Within each band-cluster, molecules are further banded into selected sub-regions with respect to their bioactivity target, sub-structural features and molecular scaffolds. Our method is potentially applicable for big data clustering tasks of different fields.
NeurAll: Towards a Unified Model for Visual Perception in Automated Driving

Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and modeled. In this paper, we propose a joint multi-task network design for learning several tasks simultaneously. Our main motivation is the computational efficiency achieved by sharing the expensive initial convolutional layers between all tasks. Indeed, the main bottleneck in automated driving systems is the limited processing power available on deployment hardware. There is also some evidence for other benefits in improving accuracy for some tasks and easing development effort. It also offers scalability to add more tasks leveraging existing features and achieving better generalization. We survey various CNN based solutions for visual perception tasks in automated driving. Then we propose a unified CNN model for the important tasks and discuss several advanced optimization and architecture design techniques to improve the baseline model. The paper is partly review and partly positional with demonstration of several preliminary results promising for future research. We first demonstrate results of multi-stream learning and auxiliary learning which are important ingredients to scale to a large multi-task model. Finally, we implement a two-stream three-task network which performs better in many cases compared to their corresponding single-task models, while maintaining network size.
A new simple and effective measure for bag-of-word inter-document similarity measurement

To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarity---the most widely used inter-document similarity measure in text mining. In this paper, we identify the shortcomings of the underlying assumptions of term weighting in the inter-document similarity measurement task; and provide a more fit-to-the-purpose alternative. Based on this new assumption, we introduce a new simple but effective similarity measure which does not require explicit term weighting. The proposed measure employs a more nuanced probabilistic approach than those used in term weighting to measure the similarity of two documents w.r. t each term occurring in the two documents. Our empirical comparison with the existing similarity measures using different term weighting schemes shows that the new measure produces (i) better results in the binary BoW representation; and (ii) competitive and more consistent results in the term-frequency-based BoW representation.