Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband channel characterization : simulation and measurements analysis

The development of wireless telecommunication requires higher speed data rates. Wideband MIMO systems are a possible answer to that need. Both wideband and MIMO characteristics enhance multipath propagation, and require a precise knowledge of the channel to properly exploit their capabilities. The extraction of the channel parameters can be done from the measurements, but also from simulations. In the frame of this thesis, the possibility to use ray tracing to derive capacity coverage prediction is studied, based on the comparison with measurements. First the radio channel models are discussed, from the MIMO channel model to the ray tracing methods. Then the measurements methods and the algorithms extracting the channel parameters are presented, before focusing on a measurement campaign to compare prediction from ray tracing and measurements-based SAGE algorithm. Key parameters for capacity –delay spread and angular spread– were first compared, then capacity itself is introduced and a capacity comparison is conducted. Polarization influence on capacity is also studied, and different methods of emulating polarization on ray tracing are studied. Last but not least, the desired capacity coverage prediction is achieved on a wide area around the measurement streets. The simulation results with the ray tracing software are promising. The multipath components were predicted well enough by ray tracing to compute capacity. Then the polarization emulating methods gave interesting results: polarization influences capacity, and the derived capacity values were close enough to those computed from measurement to launch a capacity coverage calculation, first step toward a cellular planning based on MIMO capacity. However, some uncertainties are still left, due to computation time and models approximations.
A Sketching Interface for Sitting Pose Design in the Virtual Environment

Character pose design is one of the most fundamental processes in computer graphics authoring. Although there are many research efforts in this field, most existing design tools consider only character body structure, rather than its interaction with the environment. This paper presents an intuitive sketching interface that allows the user to interactively place a 3D human character in a sitting position on a chair. Within our framework, the user sketches the target pose as a 2D stick figure and attaches the selected joints to the environment (e.g., the feet on the ground) with a pin tool. As reconstructing the 3D pose from a 2D stick figure is an ill-posed problem due to many possible solutions, the key idea in our paper is to reduce solution space by considering the interaction between the character and environment and adding physics constraints, such as balance and collision. Further, we formulated this reconstruction into a nonlinear optimization problem and solved it via the genetic algorithm (GA) and the quasi-Newton solver. With the GPU implementation, our system is able to generate the physically correct and visually pleasing pose at an interactive speed. The promising experimental results and user study demonstrates the efficacy of our method.
Towards a proposal to capture usability requirements through guidelines

Usability is a quality attribute related to effectiveness, efficiency and satisfaction of the end-users when they interact with a system. There are sound usability design guidelines that aim to optimize the system usability. However, it is difficult to work with them if there is not a previous background in the Human-Computer Interaction area. Moreover, in the Software Engineering community, there are not specific methods to capture usability requirements. Usually, usability requirements are gathered with other non-functional requirements such as security or reliability, even though the goals of these non-functional requirements are not the same as the goals of usability. This problem arises when we aim to include the usability requirements capture in a Model Driven Development (MDD) process, where an effective capture technique with an unambiguous notation is needed to represent these requirements. In this paper, we propose a new method to capture usability requirements at the early stages of the software development process in such a way that non-experts in usability can use it. In a first step, experts in usability organize several interface design guidelines and usability guidelines in a tree structure. Next, these guidelines are shown to the analyst (non-expert in usability) through questions that she/he must ask the end-users. Answers to these questions mark the path through the tree structure. At the end of the process, if we gather all the end-user's answers, we have the usability requirements. These usability requirements could be transformed into a conceptual model of any existing MDD method by means of model to model transformations.
Is rising non-teacher pay to blame for falling teacher quality? Lessons from the introduction of the birth control pill

The average quality of teachers declined precipitously between 1960 and 2000, coinciding with a decline in the ratio of pay in teaching as compared to pay in alternative professions: relative pay. The effect of relative pay on the quality of teachers is difficult to measure because teacher pay may be correlated with working conditions, such as the school's neighborhood quality, student behaviors, and other challenges associated with the work environment. To address this concern I exploit state-by-cohort variation in whether women had legal access to the birth control pill in young adulthood. Previous work has shown that young adult pill access improved early career investments by enabling better control of childbirth timing, producing landmark improvements in the alternative professional pay available to high-ability women. This lowered the effective relative teacher pay for high-ability women. I therefore use a measure of young adult pill access as an instrument for relative pay. This instrumental variables approach assumes that the pill rollout is random with respect to state-by-cohort variation in working conditions, thus producing unbiased estimates of the effect of relative pay on the propensity to teach among women who entered the labor market between 1960 and 1975. The primary results indicate that a 10 percent increase in relative pay increases the likelihood of choosing to teach by 5 percentage points. In addition, my results show no significant differences in the labor supply elasticity to teaching by ability suggesting that high-ability women are about equally responsive to relative pay as lowability women. In culmination, the results reveal that the opportunity cost to teaching produces a significant effect on the average quality of U.S. teachers. 1 I would like to thank the UC-Berkeley faculty, students, and staff for their support and guidance with this project. In particular, Professors Rucker Johnson, Jesse Rothstein, Steven Raphael and Eugene Smolensky have provided invaluable mentorship. Also, I would like to thank the Ford Foundation for their generous support of my scholarship over the last three years. Any errors herein are my responsibility alone. Correspondence can be addressed by email to hamilton.candace@gmail.com. Is rising non-teacher pay to blame for falling teacher quality? Candace Hamilton Hester, Goldman School of Public Policy, U.C. , Berkeley—Job Market Paper 2013-14
Perspective Change and Personality State Variability: An Argument for the Role of Self-Awareness and an Outlook on Bidirectionality (Commentary on Wundrack et al., 2018)

In a recent article, Wundrack et al. (2018) put forward an elaborate and intriguing hypothesis on enhanced perspective-taking (Theory of Mind) ability as a consequence of higher personality state variability. While there is evidence in favor of this hypothesis, the clinical examples of bipolar disorder and borderline personality disorder, as highlighted by the authors, demonstrate that a high state variability can also be accompanied by a lower perspective-taking ability (as commonly observed in these disorders). We suggest that only those state changes which are initiated on a voluntary basis and are accompanied by self-awareness go along with a higher perspective-taking ability. Introducing self-awareness as a moderating factor might help explain seemingly conflicting findings related to the hypothesis proposed in the target article. Moreover, we argue that the direction of causality proposed in the target article is not the only conceivable one, and perspective-taking ability could also be a cause, not just a consequence, of personality state variability. Finally, we provide suggestions on how these hypotheses could be tested in future studies.
DenXFPN: Pulmonary Pathologies Detection Based on Dense Feature Pyramid Networks

Computer-aided detection and diagnosis (CAD) have been applied to many departments of medical institutions, and early detection of diseases can prevent serious health loss. Pulmonary diseases generate negative effects on human health, even leading to death. The chest X-ray is a common examination for diagnosis of pulmonary diseases. The experienced radiologist can quickly infer patients’ symptoms by screening the chest X-ray image. While in some developing countries or remote rural areas, due to the lack of experienced radiologists or doctors, patients may be misdiagnosed. Many efforts have been spent on developing an effective auxiliary detection system to provide medical workers with evidence on diseases. In particular, detecting pulmonary complication via chest X-ray images is one of the most challenging tasks. In this paper, we transform the pulmonary complications detection task into a multi-binary classification task for each pulmonary pathology, and propose a new classification model, DenXFPN (for X-ray). DenXFPN combines multiple feature maps at different scales extracted through a densely convolutional neural network. Our model achieves 0.827 on the area under the receiver operating characteristic curve (AUC) metric on average, which outperforms the state-of-the-art results on most of all pathologies in the Chest X-ray14 dataset.
A comparison of two new exact algorithms for the robust shortest path problem

Real road networks can be modelled in mathematical terms as interval digraphs, where an interval of travel times (costs) is associated with each arc. Intervals represent uncertainty, typical of real situations, about exact travel times. A robust shortest path is a path which is not too far from the shortest one, whatever the exact values of arc costs are. This concept, expressed in mathematical terms, is used to drive optimization. In this paper we compare the performance of two exact methods recently presented on some real road networks.
Disambiguating Prepositional Phrase Attachments by Using On-Line Dictionary Definitions

Standard on-line dictionaries offer a wealth of knowledge expressed in natural language form. We claim that such knowledge can and should be accessed by natural language processing systems to solve difficult ambiguity problems. This paper sustains that claim by describing a set of computational tools and techniques used to disambiguate prepositional phrase attachments in English sentences, by accessing on-line dictionary definitions. Such techniques offer hope for eliminating the time-consuming, and often incomplete, hand coding of semantic information that has been conventional in natural language understanding systems.
A levelwise search algorithm for interesting subspace clusters

We present a levelwise search algorithm for finding subspace clusters in high dimensional data satisfying various properties besides the commonly used minimum density property. A set of such properties are summarized and a user can choose any of these properties. A lattice is built with all the discovered clusters which enables further analysis and discovery of useful knowledge about the clusters and their inter-relationships.
Construction of a Russian WordNet Grid

Computational lexicons (CL) provide machine understandable word knowledge. Semi-automatic integration and enrichment of large-scale multilingual lexicons like WordNet [1] is used in many computer applications. The three core concepts in WordNet are the synset, the word sense and the word. Words are the basic lexical units, while a sense is a specific sense in which a specific word is used. Synsets group word senses with a synonymous meaning, such as {car, auto, automobile, machine, motorcar} or {car, railcar, railway car, railroad car}. There are four disjoint types of synset, containing exclusively nouns, verbs, adjectives or adverbs. There is one specific type of adjective, namely an adjective satellite. • " gloss " (between a synset and a sentence); • " frame " (between a synset and a verb construction pattern). Linking concepts across many lexicons belonging to the WordNet-family started by using the Interlingual Index (ILI) [2]. Unfortunately, no version of the ILI can be considered a standard and often the various lexicons exploit different version of WordNet as ILI. At the 3rd GWA Conference in Korea there was launched the idea to start building a WordNet grid around a Common Base Concepts expressed in terms of WordNet synsets and SUMO definitions planned to be build around the set of 4689 Common Base Concepts. Since then only three languages with essentially various number of synsets and different WordNet versions were placed in the Grid mappings (English – 4689 synsets with WN 2.0 mapping, Spanish – 15556 synsets with WN1.6 mapping and Catalan-12942 synsets with WN1.6 mapping). But there is yet no official format for the Global WordNet Grid. So far there are just only 3 files in the specified format. As alternative another possible solution can use the DTD from the Arabic WordNet: This paper reports about the result of the development of the Russian WordNet Grid [3]. It describes usage of Russian and English-Russian lexical language resources and software to process WordNet Grid for Russian language (4600 synsets with WN 2.0 mapping) and design of a XML/RDF/OWL-markup of the grid resources. Relevant aspects of the DTD/XML/RDF/OWL formats and related technologies are surveyed. Several Russian lexical resources were used for the Russian WordNet Grid [4]. We've done porting of the original English and Russian WordNet [3] into XML using the DTD for the XML structure from The standard DTD for the Russian grid XML structure and the English/Russian XML format for …
High-speed hardware architectures for authenticated encryption mode GCM

We propose various high-speed hardware architectures for GCM (Galois counter mode) in conjunction with various AES (advanced encryption standard) hardware macros, and clarify the trade-offs between speed and hardware resources. The designs were evaluated by using a 0.13-mum CMOS standard cell library. The highest throughput of 42.7 Gbps with 297 Kgates was obtained from a sequential GCM architecture with a full-pipelined AES circuit where a 128-bit data block is processed on every clock, and the smallest size of 73 Kgates with 6.4 Gbps was achieved with pipelined-loop architecture. All of our architectures support key sizes of 128, 192, and 256 bits, while only one previous approach does. Even with variable-length key support, all of our designs showed higher performance than conventional designs
Non-negative matrix factorization-based subband decomposition for acoustic source localization

A novel non-negative matrix factorisation (NMF)-based subband decomposition in frequency–spatial domain for acoustic source localisation using a microphone array is introduced. The proposed method decomposes source and noise subband and emphasises source dominant frequency bins for more accurate source representation. By employing NMF, delay basis vectors and their subband information in frequency–spatial domain for each frame is extracted. The proposed algorithm is evaluated in both simulated noise and real noise with a speech corpus database. Experimental results clearly indicate that the algorithm performs more accurately than other conventional algorithms under both reverberant and noisy acoustic environments.
The titration of trachoma and inclusion blennorrhoea viruses in cell cultures.

SUMMARY: A technique is described for titrating trachoma and inclusion blennorrhoea viruses by counting the inclusions formed in HeLa cell monolayers. The method compares favourably in accuracy with other techniques used for the assay of viruses and is more reliable than titration in the chick embryo yolk sac.
Accuracy Analysis of the Multicycle Synchrophasor Estimator Provided by the Interpolated DFT Algorithm

This paper investigates the accuracy of synchrophasor estimators provided by the interpolated discrete Fourier transform (IpDFT) algorithm under both steady-state and dynamic conditions when two- or three-cycle length observation intervals are considered. According to the IEEE Standard C37.118. 1-2011 about synchrophasor measurements for power systems, the estimation accuracy is expressed by the total vector error (TVE). The effect on the estimation accuracy of different window functions, observation interval lengths, and processed DFT samples is analyzed through computer simulations. It is shown that most of the performance requirements specified in the Standard can be satisfied with a proper selection of the algorithm characteristics. Also, the performances of the proposed synchrophasor estimators and state-of-the-art estimators recently proposed in the scientific literature are compared and discussed. Some experimental results are presented in order to confirm the performed analysis.
Fast String Kernels using Inexact Matching for Protein Sequences

We describe several families of k-mer based string kernels related to the recently presented mismatch kernel and designed for use with support vector machines (SVMs) for classification of protein sequence data. These new kernels -- restricted gappy kernels, substitution kernels, and wildcard kernels -- are based on feature spaces indexed by k-length subsequences ("k-mers") from the string alphabet Σ. However, for all kernels we define here, the kernel value K(x,y) can be computed in O(cK(|x|+|y|)) time, where the constant cK depends on the parameters of the kernel but is independent of the size |Σ| of the alphabet. Thus the computation of these kernels is linear in the length of the sequences, like the mismatch kernel, but we improve upon the parameter-dependent constant cK = km+1|Σ|m of the (k,m)-mismatch kernel. We compute the kernels efficiently using a trie data structure and relate our new kernels to the recently described transducer formalism. In protein classification experiments on two benchmark SCOP data sets, we show that our new faster kernels achieve SVM classification performance comparable to the mismatch kernel and the Fisher kernel derived from profile hidden Markov models, and we investigate the dependence of the kernels on parameter choice.
PurTreeClust: A purchase tree clustering algorithm for large-scale customer transaction data

Clustering of customer transaction data is usually an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we present to use a “personalized product tree”, called purchase tree, to represent a customer's transaction data. The customer transaction data set can be represented as a set of purchase trees. We propose a PurTreeClust algorithm for clustering of large-scale customers from purchase trees. We define a new distance metric to effectively compute the distance between two purchase trees from the entire levels in the tree. A cover tree is then built for indexing the purchase tree data and we propose a leveled density estimation method for selecting initial cluster centers from a cover tree. PurTreeClust, a fast clustering method for clustering of large-scale purchase trees, is then presented. Last, we propose a gap statistic based method for estimating the number of clusters from the purchase tree clustering results. A series of experiments were conducted on ten large-scale transaction data sets which contain up to four million transaction records, and experimental results have verified the effectiveness and efficiency of the proposed method. We also compared our method with three clustering algorithms, e.g., spectral clustering, hierarchical agglomerative clustering and DBSCAN. The experimental results have demonstrated the superior performance of the proposed method.
FFT size optimization for LTE RoF in nonlinear fibre propagation

This papers investigates the performance of the fast Fourier transform (FFT) sizes- 64, 128, 256, 512 and 1024 for the orthogonal frequency division multiplexing (OFDM) scheme in 3rd generation partnership program (3GPP)-long term evolution (LTE) and LTE-Advanced (LTE-A). This paper aims to optimize the FFT sizes with respect to quadrature phase shift keying (QPSK), 16, 64 and 256-quadrature amplitude modulation (QAM). This optimization is for the transmission of LTE signals between eNodeB (eNB) and relay node (RN) to extend the mobile coverage employing radio-over-fibre (RoF). This paper will take into account the positive frequency chirp (PFC) induced by distributed feedback laser (DFB) through direct modulation with chromatic dispersion (CD) and self phase modulation (SPM) impairments into consideration. We present the optimum optical launch power (OLP) region termed as the intermixing region between linear and nonlinear optical fibre propagation. The optimum OLP in this investigation takes place at -4 dBm which falls within the intermixing region. At the transmission rate of 200, 400, 600 and 800 Mb/s of QPSK, 16, 64 and 256-QAM, the FFT size-128 provides the optimum power penalty with average system efficiency with respect to FFT size-64 is 54% and FFT size-256 is 65%.
A Bayesian Belief Network approach for predicting kernicterus

A lot of research have been conducted using expert systems in the diagnosis of neonatal jaundice but none has been conducted on kernicterus. Kernicterus is a complication of neonatal jaundice in which bilirubin accumulates in the grey matter of the brain, causing an irreversible neurological damage. In this paper, a Bayesian belief network was designed for predicting neonatal jaundice. The BBN model has 15 nodes and had 97% and 94% accuracy in classifying neonatal jaundice and kernicterus respectively. Keywords: Jaundice, Kernicterus, Bayesian Belief Network
Distributed algorithms for transmission power control in wireless sensor networks

In a wireless, multi-hop sensor network, choosing transmission power levels has an important impact on energy efficiency and network lifetime. Two algorithms for dynamically adjusting transmission power level on a per-node basis are proposed here. Network lifetime, convergence speed as well as resulting network connectivity are used as figures of merit for these two algorithms. They have been evaluated in an indoor sensor environment. The network lifetime metrics of these two local algorithms are also benchmarked against power control algorithms using global information. We show that these local algorithms outperform fixed power level assignment and that the lifetime achieved by them is usually within a factor of two of globally computed solution while being scalable.