Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate sum-capacity of full- and half-duplex asymmetric interference channels with unilateral source cooperation

This paper focuses on the causal cognitive radio channel. This model consists of a two-user Gaussian interference channel where a cooperative secondary/cognitive source can causally learn the message of a primary source through a noisy link. The generalized Degrees-of-Freedom (gDoF) and the sum-capacity to within a constant gap are characterized for channels where one destination does not experience interference, referred to as interference-asymmetric channels. Both cases where the cognitive source operates in full- and half-duplex mode are considered. The different models are compared among themselves and with the case of interference-symmetric channels with either bilateral or unilateral source cooperation. In particular, the parameter regimes where causal cognition, or unilateral cooperation, attains the same gDoF of classical channel models, such as the noncooperative interference channel and the non-causal cognitive channel, are identified in order to determine when causal cognitive radio offers benefits in practical systems.
Did the Tea Party Movement Fuel the Trump-Train? The Role of Social Media in Activist Persistence and Political Change in the 21st Century

Arguably, the Tea Party movement played a role in Trump’s rise to power. Indeed, it is difficult to ignore the similarities in the populist claims made by Tea Partiers and those made by Trump throughout his campaign. Yet, we know very little about the potential connections between the Tea Party Movement and the “Trump-train” that crashed through the White House doors in 2017. We take a first step at tracing the connection between the two by examining who stayed involved in the Tea Party Movement at the local level and why. Drawing on interview and participant observation data with supporters of the Florida Tea Party Movement (FTPM) over a 2-year time period, we use qualitative comparative analysis (QCA) to assess the factors that determine whether individuals stay with or leave the movement and how the structure of the movement, which relied heavily on social media, contributed to this decision. We find that individuals who identified as libertarian left the FTPM, while those who identified as “fiscal conservatives” stayed. The FTPM’s reliance on social media further explains these results. Individuals who left the movement blamed the “openness” of social media, which, in their view, enabled the Republican Party to “hijack” the FTPM for its own purposes. Individuals who stayed in the movement attributed social media’s “openness” with the movement’s successes. We find that social media helped politically like-minded people locate one another and cultivate political communities that likely sustained activist commitment to changing the Republican Party over time.
Tip-Grasp Experiences with Three-Finger Anthropomorphic LARM Hand

This paper presents tip-grasp experiences with a robotic hand with three 1 dof anthropomorphic fingers of human sizes. The last prototype of LARM Hand built at LARM in Cassino is illustrated as a low-cost easy-operation design. Cylindrical-grasp experiences have been previously carried out in order to compare the force measure results with the force results that are measured during the tip-grasp experiences. Tip-grasp experiences have been developed by using specific grasping strategies and they have been carried out successfully for a suitable easy-operation of the LARM Hand.
Derivation and cardiomyocyte differentiation of induced pluripotent stem cells from heart failure patients.

AIMS Myocardial cell replacement therapies are hampered by a paucity of sources for human cardiomyocytes and by the expected immune rejection of allogeneic cell grafts. The ability to derive patient-specific human-induced pluripotent stem cells (hiPSCs) may provide a solution to these challenges. We aimed to derive hiPSCs from heart failure (HF) patients, to induce their cardiomyocyte differentiation, to characterize the generated hiPSC-derived cardiomyocytes (hiPSC-CMs), and to evaluate their ability to integrate with pre-existing cardiac tissue. METHODS AND RESULTS Dermal fibroblasts from two HF patients were reprogrammed by retroviral delivery of Oct4, Sox2, and Klf4 or by using an excisable polycistronic lentiviral vector. The resulting HF-hiPSCs displayed adequate reprogramming properties and could be induced to differentiate into cardiomyocytes with the same efficiency as control hiPSCs (derived from human foreskin fibroblasts). Gene expression and immunostaining studies confirmed the cardiomyocyte phenotype of the differentiating HF-hiPSC-CMs. Multi-electrode array recordings revealed the development of a functional cardiac syncytium and adequate chronotropic responses to adrenergic and cholinergic stimulation. Next, functional integration and synchronized electrical activities were demonstrated between hiPSC-CMs and neonatal rat cardiomyocytes in co-culture studies. Finally, in vivo transplantation studies in the rat heart revealed the ability of the HF-hiPSC-CMs to engraft, survive, and structurally integrate with host cardiomyocytes. CONCLUSIONS Human-induced pluripotent stem cells can be established from patients with advanced heart failure and coaxed to differentiate into cardiomyocytes, which can integrate with host cardiac tissue. This novel source for patient-specific heart cells may bring a unique value to the emerging field of cardiac regenerative medicine.
Does Material and Service Offshoring Improve Domestic Productivity? Evidence from Japanese Manufacturing Industries

An increasing number of manufacturers are shifting some stages of the production process offshore. This study investigates the effects of offshoring on productivity in Japanese manufacturing industries for the period 1988-2004. Material offshoring, as measured by an import share of intermediate material inputs, has steadily increased during the period, with a pronounced increase in offshoring to Asian countries. In a wide range of specifications, we find significantly positive correlations between material offshoring and productivity at the industry level. The estimates are particularly robust for offshoring to Asia. These results suggest that Japanese manufacturing firms have developed an extensive international division of labor in East Asia, which in turn may have enhanced domestic productivity. In contrast, service offshoring, as measured by an import ratio of service inputs, is not associated with industry-level productivity. We find a positive correlation between offshoring and productivity only for information services, suggesting that in this segment, offshoring offers potential benefits.
Homeostasis and the Importance for a Balance Between AKT/mTOR Activity and Intracellular Signaling

The AKT family of serine threonine kinases is of critical importance with regard to growth factor signaling, cell proliferation, survival and oncogenesis. Engagement of signaling receptors induces the lipid kinase, phosphatidylinositol 3-kinase (PI3K), which enables the activation of AKT. Responsive to the PI3K/AKT pathway is the mammalian target of rapamycin (mTOR), a major effector that is specifically implicated in the regulation of cell growth as a result of nutrient availability and cellular bioenergetics. These kinases mediate the activity of a multitude of intracellular signaling molecules and intersect with multiple pathways that regulate cellular processes. Elucidating the role of AKT/mTOR in metabolism and in hallmark signaling pathways that are aberrantly affected in cancer has provided a solid foundation of discoveries. From this, new research directions are emerging with regard to the role of AKT/mTOR in diabetes and T cell-mediated immunity. As a result, a new perspective is developing in how AKT/mTOR functions within intracellular signaling pathways to maintain cellular homeostasis. An appreciation is emerging that altered equilibrium of AKT/mTOR pathways contributes to disease and malignancy. Such new insights may lead to novel intervention strategies that may be useful to reprogram or reset the balance of intracellular signaling.
Design of 9-transistor content addressable memory cells using Schottky-barrier carbon nanotube field effect transistors

The work in this paper proposes a novel design and performance analysis of high performance content addressable memory (CAM) cell using Double gate (DG) Schottky-barrier carbon nanotube field effect transistor (SBCNTFET). The standard 9-Transistor model of CAM cell is implemented using circuit compatible model of DG SBCNTFET and simulated extensively using Cadence Spectre simulator. It has been found that DG SBCNTFET-based CAM cell can achieve 148.57× less power-delay-product (PDP) for read operation and 925× less PDP for write operation compared to CMOS based design. For search operation the PDP is 324× less in CNTFET-based CAM as compared to CMOS CAM cell. It has been also compared with the MOSFET-like CNTFET based 9T-CAM cell and it is found that it can achieve 36.84× less power-delay-product (PDP) for read operation and 44.8× less PDP for write operation and 52.46× less PDP for search operation.
Quantum state reconstruction by high-frequency , time-domain , homodyne tomography

We show a time-domain optical homodyne tomography achieved at very high frequencies for the reconstruction of quantum field states. A mode-locked Ti:Sa laser is used as the source for the local oscillator. The pulse trains are detected by two oppositely-biased silicon pin photodiodes and amplified by an high-bandwidth operational amplifier. This scheme is checked by means of standard techniques, including the Allan-variance analysis, and it is applied to the tomographic reconstruction of weak coherent states.
What Is eXtreme Programming ?

0 7 4 0 7 4 5 9 / 0 0 / $ 1 0 . 0 0 © 2 0 0 0 I E E E J u l y / A u g u s t 2 0 0 0 I E E E S O F T W A R E 19 design, algorithm, code, or test—does indeed improve software quality and reduce time to market. Additionally, student and professional programmers consistently find pair programming more enjoyable than working alone. Yet most who have not tried and tested pair programming reject the idea as a redundant, wasteful use of programming resources: “Why would I put two people on a job that just one can do? I can’t afford to do that! ” But we have found, as Larry Constantine wrote, that “Two programmers in tandem is not redundancy; it’s a direct route to greater efficiency and better quality. ”1 Our supportive evidence comes from professional programmers and from advanced undergraduate students who participated in a structured experiment. The experimental results show that programming pairs develop better code faster with only a minimal increase in prerelease programmer hours. These results apply to all levels of programming skill from novice to expert.
A comparison of the agreement, internal consistency, and 2-day test stability of the InBody 720, GE iDXA, and BOD POD® gold standard for assessing body composition

ABSTRACT The study compared the agreement, internal consistency, and measurement stability of the GE iDXA, BOD POD, and InBody 720. Body composition of 43 men and 37 women (31.4 ± 10.7 years; 90% Caucasian and 10% other) was assessed in triplicate using each method over two different days. Mean percent body fat (% BF) of the participants was different for all three machines (27.6 ± 10.0% [GE iDXA)] 25.6 ± 10.4% [BOD POD], and 21.3 ± 10.6% [InBody 720]; p < . 05). The coefficient of variation for same day repeated measures was 1.06% (GE iDXA), 3.29% (BOD POD), and 2.97% (InBody 720). The coefficient of variation for 2-day repeated measures was 1.81% (GE iDXA), 4.61% (BOD POD), and 4.24% (InBody 720). The difference between the GE iDXA and BOD POD was within acceptable variability, while the InBody 720 significantly underestimated % BF. The internal consistency was highest for the GE iDXA, followed by the InBody 720, and then the BOD POD.
Automatic recognition of traffic signs

With the development of information technologies, intelligent vehicle and driver support systems are started to take place more in our daily life. Such systems can warn the driver or even control movements of the vehicle by watching situation of roads and traffic signs. This paper introduces an approach which can be a part of such systems to detect and recognize traffic signs automatically. The video images taken from a vehicle in Turkish Highways are processed to find probable regions of traffic signs. After finding probable regions of signs in each video frame, the type of sign is determined by using features of traffic signs in the database. With the proposed approach, signs were recognized in various distances, scales, and viewing angles. Speed performance of the approach is sufficient to implement a real-time system in the future.
Automatic metadata enrichment in news production

News production is characterized by complex and dynamic workflows in which it is important to produce and distribute news items as fast as possible. In this paper, we show how personalized distribution and consumption of news items can be enabled by automatically enriching news metadata with open linked datasets available on theWeb of data, thus providing a more pleasant experience to fastidious consumers where news content is presented within a broader historical context. Further we present a faceted browser that provides a convenient way for exploring news items based on an ontology of NewsML-G2 and rich semantic metadata.
Estimation of leaf area index and the fraction of absorbed photosynthetically active radiation in a boreal forest

The aim of this dissertation is to assess the accuracy of different ground reference methods used to validate satellite based leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) products. LAI and fPAR are strongly linked, although they principally and practically measure different properties: LAI quantifies the areal interphase between soil and atmosphere, whereas fPAR quantifies the energy available for photosynthesis. Until now, the development of remote sensing based methods to estimate LAI and fPAR in a boreal forest has been hindered by the scarcity of ground data, which is required to validate and develop existing algorithms. The aim of the first part of this dissertation was to assess the impacts of different methodological approaches to estimate LAI in boreal forests, and to validate satellite based LAI products. Results showed that the accuracy of ground based LAI estimates is sensitive to both the retrieval methods and sampling scheme used to collect the optical LAI data. The satellite based measurements of LAI demonstrated a large temporal variability in LAI. The second part of the dissertation focused on measuring and modeling fPAR in a boreal forest. A new scheme for measuring and modeling ground reference fPAR based on photon recollision probability was presented in this dissertation. Ground reference fPAR is usually estimated only for the forest canopy layer. This study is among the first ones to validate the new global satellite based fPAR product called GEOV1 using data of both the forest canopy and understory layers from boreal forests. Results showed that satellite based fPAR products may correspond better with the total fPAR, instead of only the forest canopy fPAR as has often been presumed.
FFT-based auto-correlation estimation (FACE) for extended radar pulse integration subject to large doppler change

In such applications as detection and tracking of stealthy targets, it is often necessary to integrate radar pulses over an extended period so as to accumulate the strength of target returns while averaging out noise. The autocorrelation estimate (ACE) method was shown to be less sensitive to changes in Doppler frequency than the fast Fourier transform (FFT)-based coherent integration method [1]. However, when the Doppler frequency is subject to large changes, the ACE method still suffers from performance degradation. In this paper, we set forth a new method for extended radar pulse integration that combines the FFT and ACE methods, which we call FACE. Among various existing techniques analyzed in this paper, the connections of the new method to the Wigner-Ville transform and the cyclic autocorrelation function are emphasized. For the case of a quadratic phase function or a linear frequency modulation (LFM) where the change rate in frequency is constant, simulations are presented to demonstrate the functionality of the new method for estimation of chirp signal parameters as compared to the ACE method.
Detecting Spammers on Twitter Based on Content and Social Interaction

Twitter has become a target platform on which spammers spread large amounts of harmful information. These malicious spamming activities have seriously threatened normal users' personal privacy and information security. An effective method for detecting spammers is to learn a classifier based on user features and social network information. However, social spammers often change their spamming strategies for evading the detection system. To tackle this challenge, latent user features factorized by text matrix are adopted to capture the consistency of users' behavior. Also, a new social regularization based on users' interaction is introduced to distinguish different types of users. Finally, Supervised Spammer Detection method with Social Interaction is proposed, which jointly learn a classifier by using combine text content, social network information and labeled data. Experimental results on a real-world Twitter dataset confirm the effectiveness of the proposed method.
A functional view on the expression of emotion – the case of Spanish emigration and the media: Politics, manipulation and stance

The expression of emotion works as an argumentative tool in the case of Spanish emigration and its depiction in the media. I have made a comparative analysis of two different political contexts to observe different social parameters at work: the current democratic system and the post-Spanish Civil War dictatorial regime. In both cases, they are exploited by third parties to validate a particular political stance. The analysis made in this work draws on Edwards’ ‘emotion in use’ and the lexical framework within Martin and White’s ‘appraisal model’. The way emigration is emotionally verbalized determines issues such as responsibility. Negative emotion remarks require some agent to be blamed, normally the government for its economic policies, whereas the positive ones have a reinforcement effect and work as a bonding mechanism to the system. Judgment and Affect are found to be interrelated. More specifically, and following Martin and White’s terminology, Affect may work as a graduation instrument in the expression of Judgment.
Coding theorems for individual sequences

A quantity called the {\em finite-state} complexity is assigned to every infinite sequence of elements drawn from a finite sot. This quantity characterizes the largest compression ratio that can be achieved in accurate transmission of the sequence by any finite-state encoder (and decoder). Coding theorems and converses are derived for an individual sequence without any probabilistic characterization, and universal data compression algorithms are introduced that are asymptotically optimal for all sequences over a given alphabet. The finite-state complexity of a sequence plays a role similar to that of entropy in classical information theory (which deals with probabilistic ensembles of sequences rather than an individual sequence). For a probabilistic source, the expectation of the finite state complexity of its sequences is equal to the source's entropy. The finite state complexity is of particular interest when the source statistics are unspecified.
A Dynamic Approach to Probabilistic Inference using Bayesian Networks

In this paper we present a framework for dynamically constructing Bayesian networks. We introduce the notion of a background knowledge base of schemata, which is a collection of parameterized conditional probability statements. These schemata explicitly separate the general knowledge of properties an individual may have from the specific knowledge of particular individuals that may have these properties. Knowledge of individuals can be combined with this background knowledge to create Bayesian networks, which can then be used in any propagation scheme. We discuss the theory and assumptions necessary for the implementation of dynamic Bayesian networks, and indicate where our approach may be useful. 1 A dynamic approach to using Bayesian networks Bayesian networks are often used in expert systems [1], and decision analysis [8, 3], where a network is engineered to perform a highly specialized analysis task. In these applications, the network often implicitly combines general knowledge with specific knowledge. For example, a Bayesian network with an arc as in Figure 1 refers to a specific individual (a house or a tree or dinner or whatever), exhibiting a somewhat generalized property (fire causes smoke). The dynamic approach presented in this paper is motivated by the observation that a knowledge engineer who is modelling a domain has expertise in that domain (or is able to consult with an expert), but ∗This research is supported in part by NSERC grant #OGPOO44121. may not be able to anticipate the individuals which are to be included in the model. This paper presents a framework in which probabilistic information is written in a knowledge base of parameterized schemata, explicitly separating the general knowledge of the properties an individual may have, from the specific knowledge of the particular individual which may have these properties. When the system is supplied with knowledge of the individuals that are being modelled, an automatic process combines the parameterized information in the knowledge base with the individuals, creating a Bayesian network. By separating properties from individuals the knowledge engineer can write a knowledge base which is independent of the individuals; the system user can tell the system which individuals to consider, because she can make this observation at run time. Thus the system user doesn’t have to be an expert in the domain to create an appropriate network. In an advanced application, it is conceivable that an automated process would be able to make the observations as part of its operation, constructing and adjusting a probability model as it interacts with its domain.