Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Tier Precoding for FDD Multi-Cell Massive MIMO Time-Varying Interference Networks

Massive MIMO is a promising technology in future wireless communication networks. However, it raises a lot of implementation challenges, for example, the huge pilot symbols and feedback overhead, requirement of real-time global CSI, large number of RF chains needed, and high computational complexity. We consider a two-tier precoding strategy for multi-cell massive MIMO interference networks, with an outer precoder for inter-cell/inter-cluster interference cancellation, and an inner precoder for intra-cell multiplexing. In particular, to combat with the computational complexity issue for the outer precoding, we propose a low complexity online iterative algorithm to track the outer precoder under time-varying channels. We follow an optimization technique and formulate the problem on the Grassmann manifold. We develop a low complexity iterative algorithm, which converges to the global optimal solution under static channels. In time-varying channels, we propose a compensation technique to offset the variation of the time-varying optimal solution. We show with our theoretical result that, under some mild conditions, perfect tracking of the target outer precoder using the proposed algorithm is possible. Numerical results demonstrate that the two-tier precoding with the proposed iterative compensation algorithm can achieve a good performance with a significant complexity reduction compared with the conventional two-tier precoding techniques in the literature.
Does Personality Influence Workplace Bullying and Lead to Depression Among Nurses

ABSTRACT Workplace bullying is one of the most common work related psychological problems and has been a topic of interest since the 1990s. However, only a few researchers have investigated the influence of personality towards workplace bullying and depression. This study examines the role of personality factors as predictors of workplace bullying and their effects on depression. Structural equation modeling analysis was used to analyze the workplace bullying model. The research involved 340 registered nurses from the public sector. The respondents completed questionnaires on personality factors, workplace bullying, and depression. Results showed that only conscientiousness influenced workplace bullying and neuroticism affected depression directly. Workplace bullying has been found to directly influence depression among nurses. The findings have implications on how workplace bullying plays a key role in the relationship between personality and depression and may serve as a guideline on how to prevent nurses from psychological problems caused by workplace bullying. Keywords: Big Five personality; workplace bullying; depression; structural equation modeling, nurses ABSTRAK Tingkah laku buli di tempat kerja merupakan satu daripada masalah psikologi yang lazim di alami di tempat kerja dan situasi ini telah menjadi topik yang mendapat perhatian sejak tahun 1990. Walaubagaimanapun, hanya terdapat beberapa kajian personaliti di tempat kerja yang mengkaji pengaruhnya terhadap tingkah laku buli dan kemurungan. Kajian ini mengkaji peranan faktor-faktor personaliti sebagai penyebab buli di tempat kerja dan kesannya ke atas kemurungan. Analisis model persamaan berstruktur digunakan untuk menganalisa model tingkah laku buli di tempat kerja. Kajian ini membabitkan 340 jururawat berdaftar daripada sektor awam yang telah melengkapkan soal selidik berkenaan faktor-faktor personaliti, buli di tempat kerja, dan kemurungan. Kajian menunjukkan hanya kehematan mempengaruhi buli di tempat kerja manakala neurotisisme mempengaruhi kemurungan secara langsung. Sebaliknya, buli tempat kerja mempunyai pengaruh langsung ke atas kemurungan antara jururawat. Dapatan ini mempunyai implikasi tentang bagaimana buli memainkan peranan utama dalam hubungan di antara personaliti dan kemurungan dan boleh menjadi panduan untuk menghalang jururawat daripada mengalami masalah psikologikal yang disebabkan oleh buli di tempat kerja. Kata kunci: Personaliti lima faktor; buli di tenpat kerja; kemurungan; model persamaan berstruktur; jururawat
A New Spatial Downsampling Algorithm for DCT-Domain Video Transcoder

Spatial resolution reduction of compressed video is highly desirable in video transcoding. Many studies have addressed this problem, but they either cause drift error or have high computation complexity. A new spatial downsampling algorithm for DCT-Domain video transcoder is proposed in this paper, in which LxM sub-blocks of NxN are combined into a LxN rows and MxN columns blocks in DCT-Domain firstly, by extracting the left-top NxN low-frequency coefficients from this block, the new NxN downsampled block is the obtained. Experimental results confirm that the proposed algorithm performs better than other algorithms in terms of PNSR of reconstructed video sequences, furthermore, the computation complexity reduced greatly. sheet, as illustrated by the portions given in this document.
Alerting, orienting or executive attention networks: differential patters of pupil dilations

Attention capacities, alerting responses, orienting to sensory stimulation, and executive monitoring of performance are considered independent yet interrelated systems. These operations play integral roles in regulating the behavior of diverse species along the evolutionary ladder. Each of the primary attention constructs—alerting, orienting, and executive monitoring—involves salient autonomic correlates as evidenced by changes in reactive pupil dilation (PD), heart rate, and skin conductance. Recent technological advances that use remote high-resolution recording may allow the discernment of temporo-spatial attributes of autonomic responses that characterize the alerting, orienting, and executive monitoring networks during free viewing, irrespective of voluntary performance. This may deepen the understanding of the roles of autonomic regulation in these mental operations and may deepen our understanding of behavioral changes in verbal as well as in non-verbal species. The aim of this study was to explore differences between psychosensory PD responses in alerting, orienting, and executive conflict monitoring tasks to generate estimates of concurrent locus coeruleus (LC) noradrenergic input trajectories in healthy human adults using the attention networks test (ANT). The analysis revealed a construct-specific pattern of pupil responses: alerting is characterized by an early component (Pa), its acceleration enables covert orienting, and executive control is evidenced by a prominent late component (Pe). PD characteristics seem to be task-sensitive, allowing exploration of mental operations irrespective of conscious voluntary responses. These data may facilitate development of studies designed to assess mental operations in diverse species using autonomic responses.
Estimating L Error of Kernel Estimator: Monitoring Convergence of Markov Samplers

In many Markov chain Monte Carlo problems, the target density function is known up to a normalization constant. In this paper, we take advantage of this knowledge to facilitate the convergence diagnostic of a Markov sampler by estimating the L error of a kernel estimator. Firstly, we propose an estimator of the normalization constant which is shown to be asymptotically normal under mixing and moment conditions. Secondly, the L error of the kernel estimator is estimated using the normalization constant estimator, and the ratio of the estimated L error to the true L error is shown to converge to 1 in probability under similar conditions. Thirdly, we propose a sequential plot of the estimated L error as a tool to monitor the convergence of the Markov sampler. Finally, a 2-dimensional bimodal example is given to illustrate the proposal, and two Markov samplers are compared in the example using the proposed diagnostic plot.
CLASS-E POWER AMPLIFIER

PUNDI, BHARATRAM SATYANARAYANAN. Class-E Power Amplifier Design and Back-Telemetry Communication for Retinal Prosthesis. (Under the direction of Wentai Liu.) This thesis discusses the power link in the retinal prosthesis project that aims to provide vision to the profoundly blind. Clinical experiments have confirmed that electrical stimulation can be used to duplicate the action of photoreceptors in the retina to provide vision to blind patients suffering from RP/AMD. The retinal prosthesis system consists of an intraocular unit implanted inside the eye and a separate extraocular unit. The microstimulator involved in retinal prosthesis consists of multiple electrodes arranged in twodimensional array and is a part of the intraocular unit. The micro-stimulator needs data and power to be transferred wirelessly from the extraocular unit. The power link consists of a highly efficient Class-E driver that transfers power inductively to the intraocular unit. A Class-E power driver is developed to suit the requirements and has been analyzed. The effect of the loaded Q variations on the Class-E circuit has been studied. Back-telemetry from the intraocular unit is necessary and is achieved through load modulation in the intraocular unit. A novel variation of PWM encoding has been conceived and employed to combine data and clock. The data detection unit consisting of an envelope detector and a PWM decoder has been developed. The data received is processed in the extraocular unit to adjust the power transfer and achieve power regulation. The format for data transmission has been proposed to arrange data into continuous packets, which includes error detection capabilities. CLASS-E POWER AMPLIFIER DESIGN AND BACK-TELEMETRY COMMUNICATION FOR RETINAL PROSTHESIS
Identifying representative trees from ensembles

Tree‐based methods have become popular for analyzing complex data structures where the primary goal is risk stratification of patients. Ensemble techniques improve the accuracy in prediction and address the instability in a single tree by growing an ensemble of trees and aggregating. However, in the process, individual trees get lost. In this paper, we propose a methodology for identifying the most representative trees in an ensemble on the basis of several tree distance metrics. Although our focus is on binary outcomes, the methods are applicable to censored data as well. For any two trees, the distance metrics are chosen to (1) measure similarity of the covariates used to split the trees; (2) reflect similar clustering of patients in the terminal nodes of the trees; and (3) measure similarity in predictions from the two trees. Whereas the latter focuses on prediction, the first two metrics focus on the architectural similarity between two trees. The most representative trees in the ensemble are chosen on the basis of the average distance between a tree and all other trees in the ensemble. Out‐of‐bag estimate of error rate is obtained using neighborhoods of representative trees. Simulations and data examples show gains in predictive accuracy when averaging over such neighborhoods. We illustrate our methods using a dataset of kidney cancer treatment receipt (binary outcome) and a second dataset of breast cancer survival (censored outcome). Copyright © 2012 John Wiley & Sons, Ltd.
Application of Supervisory Control Synthesis to a Patient Support Table of a Magnetic Resonance Imaging Scanner

In this paper, we present a case-study on application of Ramadge-Wonham supervisory control theory (abbreviated by SCT in the sequel) to a patient support system of a magnetic resonance imaging (MRI) scanner. We discuss the whole developmental cycle, starting from the mathematical models of the uncontrolled system and of the control requirements, and ending with the implementation of the obtained controller on the actual hardware. The obtained controller was tested on the physical system. In this case study, we attempted to build the models in a modular way, in order to decrease the computational complexity of the controller synthesis and to improve the adaptability of the models. An important advantage of SCT is that it allows automatic generation of the controller, and that it can thus improve adaptability of the control software. We also briefly discuss our experience on the adaptability of the control software, obtained in the course of this case study.
Reconstruction of sequential data with density models

We introduce the problem of reconstructing a sequence of multidimensional real vectors where some of the data are missing. This problem contains regression and mapping inversion as particular cases where the pattern of missing data is independent of the sequence index. The problem is hard because it involves possibly multivalued mappings at each vector in the sequence, where the missing variables can take more than one value given the present variables; and the set of missing variables can vary from one vector to the next. To solve this problem, we propose an algorithm based on two redundancy assumptions: vector redundancy (the data live in a low-dimensional manifold), so that the present variables constrain the missing ones; and sequence redundancy (e.g. continuity), so that consecutive vectors constrain each other. We capture the low-dimensional nature of the data in a probabilistic way with a joint density model, here the generative topographic mapping, which results in a Gaussian mixture. Candidate reconstructions at each vector are obtained as all the modes of the conditional distribution of missing variables given present variables. The reconstructed sequence is obtained by minimising a global constraint, here the sequence length, by dynamic programming. We present experimental results for a toy problem and for inverse kinematics of a robot arm.
Modeling Terrorist Networks, Complex Systems at the Mid-range

In this paper, we develop the themes presented at the 2003 Joint Complexity Conference at the London School of Economics and subsequently published in The Intelligencer (2004) and O Tempo Das Redes (2008). Following the data analysis of the 9/11 high-jacker network developed by Valdis Krebs from open sources, we apply social network theory to examine salient arguments regarding terrorism as seen from the standpoint of complex adaptive systems theory. In particular, we explore the concepts of group cohesion, adhesion and alternative network mappings derived from node removal.
Dynamic paired comparison models with stochastic variances

In paired comparison experiments, the worth or merit of a unit is measured through comparisons against other units. When paired comparison outcomes are collected over time and the merits of the units may be changing, it is often convenient to assume the data follow a non-linear state-space model. Typical paired comparison state-space models that assume a fixed (unknown) autoregressive variance do not account for the possibility of sudden changes in the merits. This is a particular concern, for example, in modeling cognitive ability in human development; cognitive ability not only changes over time, but also can change abruptly. We explore a particular extension of conventional state-space models for paired comparison data that allows the state variance to vary stochastically. Models of this type have recently been developed and applied to modeling financial data, but can be seen to have applicability in modeling paired comparison data. A filtering algorithm is also derived that can be used in place of likelihood-based computations when the number of objects being compared is large. Applications to National Football League game outcomes and chess game outcomes are presented.
Disdaining the Media: The American Public's Changing Attitudes Toward the News

After spending two decades studying the news media as an institution, Tim Cook turned his attention to public attitudes about the press, a topic that lurked behind much of his work, most prominently Governing with the News, but one that he had never addressed directly in print. As was typically the case with Tim's voracious intellectual appetite, the project grew into a larger study of public trust and confidence in institutions. This piece represents the first fruits of this collaboration, addressing what began our inquiry: what was the cause of the long known, but seldom explained, decline in pubic confidence in the press? Was it because they had become, in Cook's words, just another “governing” institution? Or was there something distinct about the press as an institution in the array of public attitudes about the social and political world? In this piece, we demonstrate how confidence in the press is distinct from generalized confidence in other social and political institutions. In particular, we find that the same political indicators that lead to higher confidence in institutions in general drive down confidence in the press. We close by speculating on likely future trends given the adversarial tenor of press coverage.
Synthesis of three-arm block copolymer poly(lactic-co-glycolic acid)–poly(ethylene glycol) with oxalyl chloride and its application in hydrophobic drug delivery

Purpose Synthesis of star-shaped block copolymer with oxalyl chloride and preparation of micelles to assess the prospect for drug-carrier applications. Materials and methods Three-arm star block copolymers of poly(lactic-co-glycolic acid) (3S-PLGA)–polyethylene glycol (PEG) were synthesized by ring-opening polymerization, then PEG as the hydrophilic block was linked to the terminal hydroxyl of 3S-PLGA with oxalyl chloride. Fourier-transform infrared (FT-IR) spectroscopy, gel-permeation chromatography (GPC), hydrogen nuclear magnetic resonance (1H-NMR) spectra, and differential scanning calorimetry were employed to identify the structure and properties of 3S-PLGA-PEG. Rapamycin (RPM)-loaded micelles were prepared by solvent evaporation, and pyrene was used as the fluorescence probe to detect the critical micelle concentration of the copolymer. The particle size, distribution, and ζ-potential of the micelles were determined by dynamic light scattering, and the morphology of the RPM-loaded micelles was analyzed by transmission electron microscopy. High-performance liquid chromatography was conducted to analyze encapsulation efficiency and drug-loading capacity, as well as the release behavior of RPM-loaded micelles. The biocompatibility of material and the cytostatic effect of RPM-loaded micelles were investigated by Cell Counting Kit 8 assay. Results FT-IR, GPC, and 1H-NMR suggested that 3S-PLGA-PEG was successfully synthesized. The RPM-loaded micelles prepared with the 3S-PLGA-PEG possessed good properties. The micelles had good average diameter and encapsulation efficiency. For in vitro release, RPM was released slowly from 3S-PLGA-PEG micelles, showing that 3S-PLGA-PEG-RPM exhibited a better and longer antiproliferative effect than free RPM. Conclusion In this study, we first used oxalyl chloride as the linker to synthesize 3S-PLGA-PEG successfully, and compared with reported literature, this method shortened the reaction procedure and improved the reaction yield. The micelles prepared with this material proved suitable for drug-carrier application.
Dynamic Model of Object Types

This paper deals with a state model of objects that are able to dynamically change their types. The concept of a state space of an object is given, the state space is reduced to a nite set of macrostates, and nally a nite state automata is used to express object's behavior. Finally, a general dynamic model of multi-type objects is discussed and some propositions are given how to solve some related problems.
Relationship Conflict, Family Name Congruence, and Socioemotional Wealth in Family Firms

We investigate how family relationship conflict and family and firm name congruence influence subjective firm valuations by family firm owner-managers. Drawing on the socioemotional wealth perspective, behavioral agency theory and mixed gamble reasonings, we hypothesize and find a U-shaped association between relationship conflict inside the family firm and subjective firm valuation. While we do not find a direct effect between name congruence and subjective firm valuation, we show that name congruence interacts with relationship conflict to affect valuations in a complex fashion. Implications and contributions of our findings are discussed.
Data Fusion of Machine-Learning Methods for the TREC5 Routing Task (and other work)

The goal of the document routing task is to extrapolate from documents judged relevant or irrelevant for each of a set of topics accurate procedures for assessing the relevance of future documents for each topic. Rather than viewing different approaches to this problem as winner-takes-all competitors, we view them as potentially complementar methods, each exploiting different sources of information. This paper describes two quite different machine-learning approaches to the document routing task, and two approaches to combining their results to perform relevance assessments on new documents. We also describe an approach to the confusion task based on n-grams that allow approximate matches
Investigating touch interactions for an augmented world

Touch screen interaction usually requires the user to view the input surface in order to make their selections. When the interaction platform is purposefully occluded to allow for natural interaction with an augmented reality (AR) system new issues are raised in regard to the usability of the touch sensitive interface. This paper details a user evaluation scenario that we have conducted looking at pen-based selection techniques for a personal, light-weight AR system and introduces a trial for manipulation testing that we are currently conducting. By testing various techniques we are identifying a combination of operations that will enable effective and usable communication with an unobtrusive, mobile AR system.
Gradience, Phonotactics and the Lexicon in English Phonology

Experimental work has established that when subjects judge the phonological wellformedness of nonsense forms, they are strongly affected by the frequency of the phonological elements of the form and by the number of actual words that such a form is similar to. These results challenge phonological theory by suggesting a central role for frequency and the lexicon. In this paper, I review these results and show how they can be easily modelled with Probabilistic Optimality Theory. The payoff is that from very few phonological assumptions we can derive virtually the whole panoply of experimental effects. We can also derive various Local Conjunction effects as well.
Bias-variance tradeoff of soft decision trees

This paper focuses on the study of the error composition of a fuzzy decision tree induction method recently proposed by the authors, called soft decision trees. This error may be expressed as a sum of three types of error: residual error, bias and variance. The paper studies empirically the tradeoff between bias and variance in a soft decision tree method and compares it with the tradeoff of classical crisp regression and classification trees. The main conclusion is that the reduced prediction variance of fuzzy trees is the main reason for their improved performance with respect to crisp ones.
Analyzing a real world scene sequence using fuzziness

A technique to separate images of non-stationary from stationary components in a TV-frame sequence of a real world scene had previously been reported [1]. Using an approach related to fuzzy set theory, this technique has been significantly improved. Analyzing TV-frames representing a downtown street intersection for one second, bitmasks corresponding to images of a car and a pedestrian as well as their trajectories have been extracted from this sequence - without using scene specific knowledge about the moving objects or the stationary scene components. Similar results have been obtained for other TV-frame sequences. This approach may contribute to an algorithmic solution for the "peripheral vision" problem, i.e. to detect a moving object in the field of view, and for the "attention focussing" problem, i.e. to decide which part of the images in a sequence to investigate in detail.