Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-band transmitters using digitally predistorted frequency multipliers for reconfigurable radios

Two methods for reconfigurable transmitters using frequency multipliers in conjunction with digital predistortion linearizers are developed. One method utilizes a circuit topology that can be switched between a fundamental-mode in-phase combined amplifier, and a push-push frequency doubler using input phasing. Investigation to maximize output harmonics out of regular power amplifiers (PAs) was performed, and the implementation of the device was successful for the amplifier- and doubler-mode operation. To satisfy optimal load-line conditions for the operation in both modes, a bi-tuned output-combining technique is introduced as well. Measurement results indicate that the circuit is able to transmit 28 dBm of output power at 900 MHz in the amplifier mode, and 22 dBm at 1800 MHz in the doubler mode. In combination with predistortion linearization, the reconfigurable transmitter was shown to be capable of amplifying IS-95B code-division multiple-access (CDMA) signals with an adjacent-channel power ratio (ACPR) up to -58dBc/30kHz. The second suggested method utilizes a fundamental-frequency PA followed by a varactor multiplier that can be bypassed with an RF switch. A varactor-diode doubler with a saturated conversion loss of 1.3 dB was built and tested. Using predistortion linearization techniques on both the PA and doubler, an ACPR of -53dBc/30kHz at 885-kHz offset was achieved for a CDMA signal transmitted at 1850 MHz.
Booker T. Washington and the Historians: How Changing Views on Race Relations, Economics, and Education Shaped Washington Historiography, 1915-2010

v INTRODUCTION . 1 CHAPTER I: THE HAGIOGRAPHERS, 1910-1950 . 10 CHAPTER II: THE EMERGENCE OF THE CRITICAL PARADIGM, 1950-1970 . 44 CHAPTER III: WASHINGTON ON TRIAL, 1970-2000 . 78 CHAPTER IV: THE RETURN TO SYMPATHETIC SCHOLARSHIP, 2000-2010 . 120 CONCLUSION . 159 BIBLIOGRAPHY . 163 VITA . 167
Smoothed $L_p$-Minimization for Green Cloud-RAN With User Admission Control

The cloud radio access network (Cloud-RAN) has recently been proposed as one of the cost-effective and energy-efficient techniques for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems, which call for adaptive remote radio head (RRH) selection and user admission. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the ℓ0-norm-based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but nonsmooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed ℓ1/ℓ2-norm relaxation, we adopt the nonconvex but smoothed ℓp-minimization (0 <; p ≤ 1) approach to promote sparsity in the multicast setting, thereby enabling efficient algorithm design based on the principle of the majorization-minimization (MM) algorithm and the semidefinite relaxation (SDR) technique. In particular, an iterative reweighted-ℓ2 algorithm is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the relaxed smoothed ℓp-minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast cloud-RAN.
Source Density Evolution of Gamma-Ray Bursts

Recent optical afterglow observations of gamma-ray bursts (GRBs) indicate a setting and distance scale that many relate to star formation regions. In this paper, we use ⟨V/Vmax⟩ and a set of artificial trigger thresholds to probe several potential GRB source density evolutionary scenarios. In particular, we compare a uniform subset of BATSE 4B data to cosmological scenarios where GRBs evolve as the comoving density, the star formation rate, the QSO rate, and the SN Type Ic rate. Standard candle bursts with power-law spectra and a universe without vacuum energy were assumed. Our results significantly favor a comoving density model, which implies that GRB source density evolution is weaker than expected in these evolutionary scenarios. GRB density might still follow star formation rates given proper concurrent GRB luminosity evolution, significant beaming, significant error in standard candle assumptions, or if a significant modification of star formation rate estimates were to occur.
Intrusive versus deliberate rumination in posttraumatic growth across US and Japanese samples

Abstract To examine the role of rumination in the aftermath of traumatic/stressful events, posttraumatic growth (PTG) and the four types of rumination (i.e., intrusive rumination soon after the event, intrusive rumination recently, deliberate rumination soon after the event, and deliberate rumination recently) were assessed retrospectively for participants from the USA (N=224) and Japan (N=431). The results from a hierarchical regression analysis revealed that the hypothesized relationships among the four types of rumination and PTG were largely supported. Intrusive rumination soon after the event was positively related to PTG but recent deliberate rumination most strongly predicted the current levels of PTG for both samples. Some evidence for cultural differences in the role of rumination in PTG was also observed. In the US sample, deliberate rumination recently was more important than the deliberate rumination in the immediate aftermath of the traumatic/stressful event, whereas in the Japanese sample, deliberate rumination both soon after and recently were positively related to PTG. The results illustrate the importance of considering rumination as multidimensional and as varying across time in its impact on PTG. Future directions and clinical implications were discussed.
Pengaruh Dimensi Kepercayaan Terhadap Adopsi Layanan E-Government

Teknologi e-Government berkembang secara pesat, namun pada kenyataannya penggunaan e-Government masih rendah, salah satu faktor yang mempengaruhi rendahnya penggunaan e-Goverment adalah kurangnya kepercayaan, telah ditunjukkan bahwa kepercayaan adalah salah satu faktor yang mempengaruhi kesuksesan dari adopsi e-Government . Penelitian ini bertujuan mengetahui dimensi kepercayaan pada e-Goverment dan hubungannya dengan niat menggunakan e-Government, pada penelitian ini juga memvalidasi 8 dimensi kepercayaan pada e-Government berdasarkan data empiris dan menyajikan hubungan 8 dimensi kepercayaan pada e-Government itu dengan niat menggunakan e-Government. Hasil penelitian ini memberikan kontribusi berupa model berdasarkan pada 8 dimensi trust in e-Goverment dan hubungannya dengan keinginan untuk menggunakan e-Goverment malalui mediasi trust in e-Goverment. Kata kunci— kepercayaan, dimensi kepercayaan, 8 dimensi kepercayan pada e-government, keinginan menggunaakan e-government.
An Improvement of the K-SVD Algorithm with Applications on Face Recognition

Image representation is an essential issue regarding the problems related to image processing and understanding. In the last years, the sparse representation modelling for signals has been receiving a lot of attention due to its state-of-the art performance in different computer vision tasks. One of the important factors to its success is the ability to promote representations well adapted to the data which rised with the dictionary learning algorithm. The most well known of theses algorithms is the K-SVD. In this work we proposed the &alpha;K-SVD algorithm, which tries to explore the search space of possible dictionaries better than the K-SVD. Our approach is evaluated on two public face recognition databases. The results showed that our approach achieved better results than the K-SVD and LC-KSVD when the sparsity level is low.
IRobot self-localization using EKF

Self-Localization plays an important role in the mobile robot autonomous navigation. The Wheel Mobile robot usually contains a large number of different sensors, such as odometry, gyro, laser, camera and so on. All these sensors provide the information of robot localization and all these information should be considered for the optimal location. However, for the cost of the iRobot, we could not be equipped with a lot of sensors. We have only encoder sensor and gyro sensor. So this paper researches mobile robot localization only using odometer and gyro sensor based on Extended Kalman Filter (EKF). The method is that the iRobot fuses the messages from encoder sensor and gyro sensor by EKF theory, which can collect the errors that obtained the robot's orientation and position. The experiment results appear that the proposed self-localization method is effective and feasible.
The Effects of HCl and CaCl2 Injections on Intracellular Calcium and pH in Voltage-clamped Snail (Helix aspersa) Neurons

To investigate the mechanisms by which low intracellular pH influences calcium signaling, I have injected HCl, and in some experiments CaCl2, into snail neurons while recording intracellular pH (pHi) and calcium concentration ([Ca2+]i) with ion-sensitive microelectrodes. Unlike fluorescent indicators, these do not increase buffering. Slow injections of HCl (changing pHi by 0.1–0.2 pH units min−1) first decreased [Ca2+]i while pHi was still close to normal, but then increased [Ca2+]i when pHi fell below 6.8–7. As pHi recovered after such an injection, [Ca2+]i started to fall but then increased transiently before returning to its preinjection level. Both the acid-induced decrease and the recovery-induced increase in [Ca2+]i were abolished by cyclopiazonic acid, which empties calcium stores. Caffeine with or without ryanodine lowered [Ca2+]i and converted the acid-induced fall in [Ca2+]i to an increase. Injection of ortho-vanadate increased steady-state [Ca2+]i and its response to acidification, which was again blocked by CPA. The normal initial response to 10 mM caffeine, a transient increase in [Ca2+]i, did not occur with pHi below 7.1. When HCl was injected during a series of short CaCl2 injections, the [Ca2+]i transients (recorded as changes in the potential (VCa) of the Ca2+-sensitive microelectrode), were reduced by only 20% for a 1 pH unit acidification, as was the rate of recovery after each injection. Calcium transients induced by brief depolarizations, however, were reduced by 60% by a similar acidification. These results suggest that low pHi has little effect on the plasma membrane calcium pump (PMCA) but important effects on the calcium stores, including blocking their response to caffeine. Acidosis inhibits spontaneous calcium release via the RYR, and leads to increased store content which is unloaded when pHi returns to normal. Spontaneous release is enhanced by the rise in [Ca2+]i caused by inhibiting the PMCA.
Writing and the Management of Power: Producing Public Policy in New Zealand

In contrast to the traditional view of policymaking, which assumes a rational process of problem identification and solution evaluation, much actual policy turns out to be solutionled. In other words, predetermined policy measures are imposed as “solutions” to retrospectively presented “problems”. This has been particularly true, ironically, of the supposedly rational neo-liberal or “New Right” resurgence of the last quarter-century, whose influence is perhaps diminishing in some quarters but continues to be felt. In these conditions, the processes and mechanisms of policy development, particularly the various genres of the formal written stages, can be viewed as a system of production, where the favored policy is “managed” through the traditional democratic framework of agenda setting, consultation, and enactment. This chapter charts the passage of a particular instance of policy development in New Zealand – the privatization of electricity supply – through this textual regime. The chapter shows both how traditional textual genres can survive unchanged into new circumstances, thereby misleading their readers, and how genres can change or rupture under pressure of new conditions and expectations. It will be demonstrated that the staged production of policy creates a differentiation of audiences that limits participation; and that viewing the texts in interaction allows analysts to refine their perceptions of the rhetorical purposes of each.
Digital Storytelling: A Method for Engaging Students and Increasing Cultural Competency.

Digital storytelling is explored as a method of engaging students in the development of media literacy and cultural competency. This paper describes the perceptions and experiences of 96 undergraduate students at a large Midwestern university, after completing a digital storytelling project in a semester-long diversity course. Digital storytelling was introduced in the course as a pedagogy for engaging students with technology to develop cultural competency. Data were collected from an end of semester survey. Results found that the use of digital storytelling enhanced the learning environment through greater student engagement around technology and diversity issues. By bridging course content and pedagogy around diversity and cultural competency, students were able to gain competency, and experience the use of technology in academic/workplace settings, and create awareness/discourse around social issues.
Syntactic Category Formation with Vector Space Grammars

A method for deriving phrase structure categories from structured samples of a context-free language is presented. The learning algorithm is based on adaptation and competition, as well as error backpropagation in a continuous vector space. These connectionist-style techniques become applicable to grammars as the traditional grammar formalism is generalized to use vectors instead of symbols as category labels. More generally, it is argued that the conversion of symbolic formalisms to continuous representations is a promising way of combining the connectionist learning techniques with the structures and theoretical insights embodied in classical models.
MMSE Based Greedy Antenna Selection Scheme for AF MIMO Relay Systems

We propose a greedy minimum mean squared error (MMSE)-based antenna selection algorithm for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems. Assuming equal-power allocation across the multi-stream data, we derive a closed form expression for the mean squared error (MSE) resulted from adding each additional antenna pair. Based on this result, we iteratively select the antenna-pairs at the relay nodes to minimize the MSE. Simulation results show that our algorithm greatly outperforms the existing schemes.
GID: Graph-based Intrusion Detection on Massive Process Traces for Enterprise Security Systems

Intrusion detection system (IDS) is an important part of enterprise security system architecture. In particular, anomaly-based IDS has been widely applied to detect abnormal process behaviors that deviate from the majority. However, such abnormal behavior usually consists of a series of low-level heterogeneous events. The gap between the low-level events and the high-level abnormal behaviors makes it hard to infer which single events are related to the real abnormal activities, especially considering that there are massive "noisy" low-level events happening in between. Hence, the existing work that focus on detecting single entities/events can hardly achieve high detection accuracy. Different from previous work, we design and implement GID, an efficient graph-based intrusion detection technique that can identify abnormal event sequences from a massive heterogeneous process traces with high accuracy. GID first builds a compact graph structure to capture the interactions between different system entities. The suspiciousness or anomaly score of process paths is then measured by leveraging random walk technique to the constructed acyclic directed graph. To eliminate the score bias from the path length, the Box-Cox power transformation based approach is introduced to normalize the anomaly scores so that the scores of paths of different lengths have the same distribution. The efficiency of suspicious path discovery is further improved by the proposed optimization scheme. We fully implement our GID algorithm and deploy it into a real enterprise security system, and it greatly helps detect the advanced threats, and optimize the incident response. Executing GID on system monitoring datasets showing that GID is efficient (about 2 million records per minute) and accurate (higher than 80% in terms of detection rate).
Learning-Based Argument Structure Analysis of Event-Nouns in Japanese

Previous work on predicate-argument structure analysis has focused largely on verbs and adjectives. However, to extract richer information from such a sentence as “His decision was right,” not only the predicate “was right” but also the noun “decision” need to be identified as an event denoting expression. Here, we call event denoting nouns event-nouns, which refer to events like “he decided something. ” We define a task of argument structure analysis of event-nouns in Japanese. Some event-nouns, such as ryouri “cook/cuisine”, do not always refer to an event, and word sense disambiguation of event-nouns has to be done depending on their context. In this paper, we present an unsupervised machine learning method to extract patterns of event-nouns for event-hood determination. We also investigate verb-noun co-occurrences and support verb constructions for argument identification of event-nouns.
On Predicting Deletions of Microblog Posts

Among the many classification tasks on Twitter content, predicting whether a tweet will be deleted has to date received relatively little attention. Deletions occur for a variety of reasons, which can make the classification task challenging. Moreover, deletion prediction might serve different goals, the characteristics of which should be reflected in the evaluation design. This paper addresses the problem of deletion prediction by analyzing the distribution of deleted tweets, presenting a new evaluation framework, exploring tweet-based and user-based features, and reporting prediction scores.
Super M-brane actions in AdS 4 × S 7 and AdS 7 × S 4

The world-volume action of the M2-brane and the M5-brane in an ${\mathrm{AdS}}_{4}\ifmmode\times\else\texttimes\fi{}{\mathrm{S}}^{7}$ and an ${\mathrm{AdS}}_{7}\ifmmode\times\else\texttimes\fi{}{\mathrm{S}}^{4}$ background is derived to all orders in anticommuting superspace coordinates $\ensuremath{\theta}. $ Contrary to recent constructions of super p-brane actions relying on supercoset methods, we only use 11-dimensional supergravity torsion and curvature constraints. Complete agreement between the two methods is found. The possible simplification of the action by choosing a suitable $\ensuremath{\kappa}$ gauge is discussed.
Efficient Similarity Search in Dynamic Data Streams

The Jaccard index is an important similarity measure for item sets and Boolean data. On large datasets, an exact similarity computation is often infeasible for all item pairs both due to time and space constraints, giving rise to faster approximate methods. The algorithm of choice used to quickly compute the Jaccard index $\frac{\vert A \cap B \vert}{\vert A\cup B\vert}$ of two item sets $A$ and $B$ is usually a form of min-hashing. Most min-hashing schemes are maintainable in data streams processing only additions, but none are known to work when facing item-wise deletions. In this paper, we investigate scalable approximation algorithms for rational set similarities, a broad class of similarity measures including Jaccard. Motivated by a result of Chierichetti and Kumar [J. ACM 2015] who showed any rational set similarity $S$ admits a locality sensitive hashing (LSH) scheme if and only if the corresponding distance $1-S$ is a metric, we can show that there exists a space efficient summary maintaining a $(1\pm \varepsilon)$ multiplicative approximation to $1-S$ in dynamic data streams. This in turn also yields a $\varepsilon$ additive approximation of the similarity. The existence of these approximations hints at, but does not directly imply a LSH scheme in dynamic data streams. Our second and main contribution now lies in the design of such a LSH scheme maintainable in dynamic data streams. The scheme is space efficient, easy to implement and to the best of our knowledge the first of its kind able to process deletions.
On the basis for ELF: an extensible language facility

There are two basic premises which underlie the development of ELF. The first of these is that there exists a need for a wide variety of programming languages; indeed, our progress in the understanding and application of computers will demand an ever widening variety of languages. There are, in fact, "scientific" problems, "data processing" problems, "information retrieval" problems, "symbol manipulation" problems, "text handling" problems, and so on. From the point of view of a computer user who is working in one or more of these areas there are certain units of data with which he would like to transact and there are certain unit operations which he would like to perform on these data. The user will be able to make effective use of a computer only when the language facilities provided allow him to work toward a desired result in terms of data and operations which he chooses as being a natural representation of his conception of the problem solution. That is, it is not enough to have a language facility which is formally sufficient to allow the user to solve his problem; indeed, most available programming languages are, to within certain size limitations, universal languages. Rather, the facility must be natural for him to use in the solution of his particular problem.
Practical linear-space Approximate Near Neighbors in high dimension

The $c$-approximate Near Neighbor problem in high dimensional spaces has been mainly addressed by Locality Sensitive Hashing (LSH), which offers polynomial dependence on the dimension, query time sublinear in the size of the dataset, and subquadratic space requirement. For practical applications, linear space is typically imperative. Most previous work in the linear space regime focuses on the case that $c$ exceeds $1$ by a constant term. In a recently accepted paper, optimal bounds have been achieved for any $c>1$ \cite{ALRW17}. Towards practicality, we present a new and simple data structure using linear space and sublinear query time for any $c>1$ including $c\to 1^+$. Given an LSH family of functions for some metric space, we randomly project points to the Hamming cube of dimension $\log n$, where $n$ is the number of input points. The projected space contains strings which serve as keys for buckets containing the input points. The query algorithm simply projects the query point, then examines points which are assigned to the same or nearby vertices on the Hamming cube. We analyze in detail the query time for some standard LSH families. To illustrate our claim of practicality, we offer an open-source implementation in {\tt C++}, and report on several experiments in dimension up to 1000 and $n$ up to $10^6$. Our algorithm is one to two orders of magnitude faster than brute force search. Experiments confirm the sublinear dependence on $n$ and the linear dependence on the dimension. We have compared against state-of-the-art LSH-based library {\tt FALCONN}: our search is somewhat slower, but memory usage and preprocessing time are significantly smaller.