Miracle-dz/UniFAR-FTUs huggingface dataset:
Not All News Sources Are Equally Informative

Across a sample of twenty-seven European nations, we examine variation in the level of factual political knowledge in relation to self-reported exposure to news programs aired by public or commercial channels, and to broadsheet or tabloid newspapers. Unlike previous studies, we estimate the effects of exposure to these news outlets while controlling for self-selection into the audience. Our results show that the positive effects of exposure to broadsheets and public broadcasting on knowledge remain robust. Finally, we show that only exposure to broadsheets (and not to public broadcasting) narrows the knowledge gap within nations; relatively apathetic individuals who read broadsheet newspapers are able to “catch up” with their more attentive counterparts.
Subspace Identification of Individual Systems Operating in a Network (SI $^2$ON)

This note studies the identification of individual systems operating in a large-scale distributed network by considering the interconnection signals between neighboring systems to be unmeasurable. The unmeasurable interconnections act as unknown system inputs to the individual systems in a network, which poses a challenge for the identification problem. A subspace identification framework is proposed in this note for the consistent identification of individual systems using only local input and output information. The key step of this identification framework is the accurate estimation of the unknown system inputs of individual systems using local observations. Sufficient identifiability conditions are provided for the proposed identification framework and a simulation example is given to demonstrate its performance.
Managing Clutter in a High Pulse Rate Echolocation System

The use of echolocation for navigating in dense, cluttered environments is a challenge due to the need for rapid sampling of nearby objects in the face of delayed echoes from distant objects. In the wild, echolocating bats frequently encounter this situation when leaving the roost or while hunting. If long-delay echoes from a distant object are received after the next pulse is sent out, these “aliased” echoes appear as close-range phantom objects. Little is known about how bats cope with these situations. In this work, we demonstrate a novel strategy to manage aliasing in cases where a single target is actively being tracked at close range. This paper presents three reactive strategies for a high pulse-rate sonar system to combat aliased echoes: (1) changing the interpulse interval to move the aliased echoes away in time from the tracked target, (2) changing positions to create a geometry without aliasing, and (3) a phase-based, transmission beam-shaping strategy to illuminate the target and not the aliasing object.
Content-Based Image Retrieval Based on Multi-feature Fusion Optimized by Brain Storm Optimization

With the fast development of information technology and increasing number of image database, how to retrieval a large amount of information from the image quickly and effectively becomes more and more important. Brain Storm Optimization (BSO) is simple, robust and has high searching precision. So it is applied into the image retrieval in this paper. Content-based image retrieval (CBIR) extracts the color, texture, shape and other low-level visual features of images to realize image matching. Compared with image retrieval based on single feature, image retrieval based on multi-feature fusion can fully represent image information. In the multi-feature fusion, the ratio of each feature selection is critical to the search result. Traditional method is manually set or proportional integration, which ignores the priority between the various features. This paper uses BSO for image retrieval. Color histogram, color moment, color structure descriptor, Tamura texture feature, GLCM texture, wavelet transform texture, Gabor transform texture, edge histogram descriptors and Hu invariant moment are extracted in the paper and BSO is used for image retrieval. Experiment results show that the proposed method can retrieval the target image accurately and improve the precision of the system.
Role of Plant-Derived Flavonoids and Their Mechanism in Attenuation of Alzheimer’s and Parkinson’s Diseases: An Update of Recent Data

Neurodegeneration is a progressive loss of neuronal cells in certain regions of the brain. Most of the neurodegenerative disorders (NDDs) share the communal characteristic such as damage or reduction of various cell types typically including astrocytes and microglial activity. Several compounds are being trialed to treat NDDs but they possess solitary symptomatic advantages along with copious side effects. The finding of more enthralling and captivating compounds to suspend and standstill the pathology of NDDs will be considered as a hallmark of present times. Phytochemicals possess the potential to alternate the synthetic line of therapy against NDDs. The present review explores the potential efficacy of plant-derived flavonoids against most common NDDs including Alzheimer’s disease (AD) and Parkinson’s disease (PD). Flavonoids are biologically active phytochemicals which possess potential pharmacological effects, including antiviral, anti-allergic, antiplatelet, anti-inflammatory, anti-tumor, anti-apoptotic and anti-oxidant effects and are able to attenuate the pathology of various NDDs through down-regulating the nitric oxide (NO) production, by reducing the tumor necrosis factor-α (TNF-α), by reducing the excitotoxicity of superoxide as well as acting as tyrosine kinase (TK) and monoamine oxidase (MAO) inhibiting enzyme.
Comparative lesion sequencing provides insights into tumor evolution

We show that the times separating the birth of benign, invasive, and metastatic tumor cells can be determined by analysis of the mutations they have in common. When combined with prior clinical observations, these analyses suggest the following general conclusions about colorectal tumorigenesis: (i) It takes ≈17 years for a large benign tumor to evolve into an advanced cancer but <2 years for cells within that cancer to acquire the ability to metastasize; (ii) it requires few, if any, selective events to transform a highly invasive cancer cell into one with the capacity to metastasize; (iii) the process of cell culture ex vivo does not introduce new clonal mutations into colorectal tumor cell populations; and (iv) the rates at which point mutations develop in advanced cancers are similar to those of normal cells. These results have important implications for understanding human tumor pathogenesis, particularly those associated with metastasis.
Recent Developments in Semiconductor Thermoelectric Physics and Materials

Recent advances in semiconductor thermoelectric physics and materials are reviewed. A key requirement to improve the energy conversion efficiency is to increase the Seebeck coefficient (S) and the electrical conductivity (σ) while reducing the electronic and lattice contributions to thermal conductivity (κe + κL). Some new physical concepts and nanostructures make it possible to modify the trade-offs between the bulk material properties through changes in the density of states, scattering rates, and interface effects on electron and phonon transport. We review recent experimental and theoretical results on nanostructured materials of various dimensions: superlattices, nanowires, nanodots, and solid-state thermionic power generation devices. Most of the recent success has been in the reduction of lattice thermal conductivity with the concurrent maintenance of good electrical conductivity. Several theoretical and experimental results to improve the thermoelectric power factor (S2σ) and to reduce the Lorenz .
Targeting IgG in Arthritis: Disease Pathways and Therapeutic Avenues

Rheumatoid arthritis (RA) is a polygenic and multifactorial syndrome. Many complex immunological and genetic interactions are involved in the final outcome of the clinical disease. Autoantibodies (rheumatoid factors, anti-citrullinated peptide/protein antibodies) are present in RA patients’ sera for a long time before the onset of clinical disease. Prior to arthritis onset, in the autoantibody response, epitope spreading, avidity maturation, and changes towards a pro-inflammatory Fc glycosylation phenotype occurs. Genetic association of epitope specific autoantibody responses and the induction of inflammation dependent and independent changes in the cartilage by pathogenic autoantibodies emphasize the crucial contribution of antibody-initiated inflammation in RA development. Targeting IgG by glyco-engineering, bacterial enzymes to specifically cleave IgG/alter N-linked Fc-glycans at Asn 297 or blocking the downstream effector pathways offers new avenues to develop novel therapeutics for arthritis treatment.
Scaling Byzantine Fault-Tolerant Replication toWide Area Networks

This paper presents the first hierarchical Byzantine fault-tolerant replication architecture suitable to systems that span multiple wide area sites. The architecture confines the effects of any malicious replica to its local site, reduces message complexity of wide area communication, and allows read-only queries to be performed locally within a site for the price of additional hardware. A prototype implementation is evaluated over several network topologies and is compared with a flat Byzantine fault-tolerant approach
Biological network extraction from scientific literature: state of the art and challenges

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research.
IoT based indoor location detection system for smart home environment

Smart home environment is expected to meet the requirements, essentially for the aging population, to support the concept of “aging in place”, to provide reliable care and to ensure safety and proper diagnosis by keeping track of daily living, medical condition of the resident and providing feedback to the caregiver. In order to meet these requirements, smart home of today should support a number of functionalities. One such functionality of a smart home environment is the location detection. In this work, we propose a voice based location detection system which can be integrated in a smart home environment. Our location detection system uses Amazon Echo as the voice interface and HC-SR04 ultrasonic sensor to detect location of specific patients. The proposed location detection will be suitable for large scale application where we may need to keep track of multiple patients. Moreover, the inclusion of voice enabled feature to this system will reduce the burden of learning curve of new technologies on family and caregivers thereby improving the quality of life.
Methodology for improving detection speed of pedestrians in autonomous vehicle by image class classification

We propose a pedestrian detection method to minimize the amount of computation for classifying and candidate region detection in autonomous vehicles. The minimization of the computational complexity is a crucial factor for commercial products with a limited computational power. In conventional pedestrian detection methods, the number of candidate regions is 300 to 2,000 even if there is no pedestrian in an image. Therefore, the unnecessary computation is significant to classify each falsely decided candidate region. Moreover, it leads to false detection. In this paper, we propose a new methodology for solving this problem, and show through experiments that the processing speed can be improved by the proposed methodology.
Design and analysis of low noise amplifier for satellite transponder

A typical Geostationary Satellite which is used to receive and retransmit signals from the earth. A communication satellite is capable of receiving and retransmitting one or more independent signals. The electronic equipment used to amplify and shift the frequency of each signal is called a transponder. In this paper a Low Noise Amplifier for satellite transponder application at 10GHz is designed. This proposed LNA is used to amplify the weak signal at the front end of the receiver. The Low Noise Amplifiers are implemented using PHEMT and GaAsFET. The parameters are such as input return loss (Su), output return loss (S22), gain (S2i) and noise figure are simulated by using ‘ADVANCED DESIGN SYSTEM(ADS)’ and results are shown.
A strategy for broken bars diagnosis in induction motors drives

This paper presents a strategy for detecting broken rotor bars in induction motors (IM), using a zero sequence high-frequency signal injection. The derivatives of the motor currents are analyzed, the subsequent signal processing allows us to study how these signals change its value as an effect of a broken rotor bar in the IM. The main feature of the strategy is that the zero sequence high-frequency signals are generated by a standard space vector modulation of the IM drive. Analytical and experimental results are presented, validating the broken bars detection strategy.
Efficient Walking Gait Generation via Principal Component Representation of Optimal Trajectories: Application to a Planar Biped Robot With Elastic Joints

Recently, the method of choice to exploit robot dynamics for efficient walking is numerical optimization (NO). The main drawback in NO is the computational complexity, which strongly affects the time demand of the solution. Several strategies can be used to make the optimization more treatable and to efficiently describe the solution set. In this letter, we present an algorithm to encode effective walking references, generated offline via numerical optimization, extracting a limited number of principal components and using them as a basis of optimal motions. By combining these components, a good approximation of the optimal gaits can be generated at run time. The advantages of the presented approach are discussed, and an extensive experimental validation is carried out on a planar legged robot with elastic joints. The biped thus controlled is able to start and stop walking on a treadmill, and to control its speed dynamically as the treadmill speed changes.
PM3: Power Modeling and Power Management for Processing-in-Memory

Processing-in-Memory (PIM) has been proposed as a solution to accelerate data-intensive applications, such as real-time Big Data processing and neural networks. The acceleration of data processing using a PIM relies on its high internal memory bandwidth, which always comes with the cost of high power consumption. Consequently, it is important to have a comprehensive quantitative study of the power modeling and power management for such PIM architectures. In this work, we first model the relationship between the power consumption and the internal bandwidth of PIM. This model not only provides a guidance for PIM designs but also demonstrates the potential of power management via bandwidth throttling. Based on bandwidth throttling, we propose three techniques, Power-Aware Subtask Throttling (PAST), Processing Unit Boost (PUB), and Power Sprinting (PS), to improve the energy efficiency and performance. In order to demonstrate the universality of the proposed methods, we applied them to two kinds of popular PIM designs. Evaluations show that the performance of PIM can be further improved if the power consumption is carefully controlled. Targeting at the same performance, the peak power consumption of HMC-based PIM can be reduced from 20W to 15W. The proposed power management schemes improve the speedup of prior RRAM-based PIM from 69 × to 273 ×, after pushing the power usage from about 1W to 10W safely. The model also shows that emerging RRAM is more suitable for large processing-in-memory designs, due to its low power cost to store the data.
Nickel silicide growth modeling using level set methods

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NICKEL SILICIDE GROWTH MODELING USING LEVEL SET METHODS By Ashish Kumar December 2013 Chair: Mark E. Law Major: Electrical and Computer Engineering Nickel silicide is being used as a Local-Interconnect (LI) and source-drain contact material in current CMOS technologies. For scaling it has become extremely important to reduce the parasitic resistances from Schottky contacts and LI. Modeling and predicting the silicide shapes has become necessary in order to estimate the transport through device accurately. In this dissertation, Nickel Silicide (NiSi) growth models using numerical technique know as Level set methods coupled with deal groves numerical solution using diffusion solver was implemented in FLOOPS (Florida Object Oriented Process Simulator). Main focus in this work was to implement the above techniques and obtain accurate numerical solutions of the physics governing the shape of NiSi. Dopant segregation during the silicide growth is being used to reduce the Schottky Barrier Height at the source/drain contact. Our segregation model using chemical potential approach is also presented in this work. These models were also coupled with the growth model in order to capture the physics of the phenomenon accurately. It means that at every delta time step of simulations multiple equations (growth, diffusion and segregation) are solved and updated concurrently. Finally stress generated in silicon due to silicide growth is also being simulated using the Finite Element Method capabilities of FLOOPS. Stress contour simulation results are presented in comparison to the real data. Models in this work can be easily extended for 3-D structures. These
Modeling with cubic A-patches

We present a sufficient criterion for the Bernstein-Bezier (BB) form of a trivariate polynomial within a tetrahedron, such that the real zero contour of the polynomial defines a smooth and single sheeted algebraic surface patch. We call this an A-patch. We present algorithms to build a mesh of cubic A-patches to interpolate a given set of scattered point data in three dimensions, respecting the topology of any surface triangulation T of the given point set. In these algorithms we first specify "normals" on the data points, then build a simplicial hull consisting of tetrahedra surrounding the surface triangulation T and finally construct cubic A-patches within each tetrahedron. The resulting surface constructed is at (tangent plane) continuous and single sheeted in each of the tetrahedra. We also show how to adjust the free parameters of the A-patches to achieve both local and global shape control.
The Unexpected Relationship Between Retrieval Demands and Memory Performance When Older Adults are Faced with Age-Related Stereotypes.

Objectives In two studies, we examined the effects of age-related stereotype threat on eyewitness memory using the misinformation paradigm to (1) examine stereotype threat in the context of a more ecologically valid memory task and (2) determine the relationship between task difficulty and susceptibility to stereotype threat. Methods After watching a video that depicted a crime, older and younger adult participants were presented with a written synopsis in which information consistent or inconsistent with the original event was presented. Half of the participants were then presented with information designed to activate negative stereotypes about aging. Finally, participants completed a memory test. Results In Study 1, when participants were instructed to report information from either the video or the synopsis to complete the final memory test, older adults under high stereotype threat were less accurate than those under low threat. In Study 2, when participants were required to engage in more controlled processes at retrieval and respond with only video information, older adults under stereotype threat performed as well or better than those under low threat. Discussion The results are consistent with the Regulatory Focus Model of Stereotype Threat.
Large-Scale Cox Process Inference using Variational Fourier Features

Gaussian process modulated Poisson processes provide a flexible framework for modelling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to a pre-determined grid, or small data sets of up to a few thousand data points. Here we introduce Cox process inference based on Fourier features. This sparse representation induces global rather than local constraints on the function space and is computationally efficient. This allows us to formulate a grid-free approximation that scales well with the number of data points and the size of the domain. We demonstrate that this allows MCMC approximations to the non-Gaussian posterior. We also find that, in practice, Fourier features have more consistent optimization behavior than previous approaches. Our approximate Bayesian method can fit over 100,000 events with complex spatiotemporal patterns in three dimensions on a single GPU.
Design and analysis of high gain CMOS transconductance amplifier for low frequency application

An operational transconductance-amplifier (OTA) is mainly designed for low voltage and low frequency applications such as for the medical applications. The Operational Transconductance Amplifier (OTA) is the part in an analog integrated circuits for many wireless applications such as binural hearing aids. High gain is becoming more important in miniature devices because this parametric form provide the significant outcomes, so it becomes a challenge to design a low power OTA. The proposed design helps to generates the design parameters such as the Width/Length ratio are given to design specifications, such as, gain, unity gain-bandwidth, phase margin. This paper is mainly proposed for the low frequency application using transconductance amplifier.
Driver intent data analysis & classification

Driver intent prediction is required in order to prevent or alert other drivers maneuvers to avoid accidents. However, real-time information makes this task difficult to perform an early detection/classification of any driving changing behavior. In this work, a GPS and IMU data logging from a T-intersection has been analyzed and treated with the aim of obtaining the most discriminant qualitative behavior from the data available.