Miracle-dz/UniFAR-FTUs huggingface dataset:
AN INTEGRATIVE SEMIOTIC METHODOLOGY FOR IS RESEARCH 1

Semiotics studies the production, transmission and interpretation of meaning represented symbolically in signs and messages, primarily but not exclusively in language. For information systems ( IS) the domain of semiosis consists of human and non-human interactions based on technologically-mediat ed communication in the social, material and personal worlds. The paper argues that sem iosis has immense bearing on processes of communication central to the advanced information and communications technologies studied by IS scholars. Its use separately, or in mixed methods approaches, enri areas of central concern to the IS field, and is particularly apt when researching internetbased development and applications, for example virtual worlds and social media. This paper presents a four step structured methodology, informed by a central theoretical semiotic framework to provide practi cal guidelines for operationalizing semiotics in IS research. Thus, using illustrative examples, the paper provides a stepbystep semiotics approach to research based on distinctive semiotic concepts and their r ela ionships – producer, consumer, medium, code , message and content – and how, at an integrating level, the personal, social and material worlds relate through sociation, embodiment and socio-materiality.
Industrial design of axisymmetrical devices using a customized FDTD solver from RF to optical frequency bands [Application Notes]

This article focused on the vector two-dimensional finite-difference time-domain (V2-D FDTD) method for axisymmetrical devices, also known as BOR FDTD. The approach to solving Maxwell equations by the V2-D FDTD method is straightforward. Not only the whole structure simulation is feasible, facilitating a search for globally optimum solutions, but also a valuable insight is provided into the subtleties of the device operation in the 3-D space and time. A graphical interface of the QW-V2D solver has been used to exemplify how the software helps in problem solving and quick search for new solutions.
1 THE MORE THE MERRIER ? THE RELATIONSHIP BETWEEN STRUCTURAL CONFIGURATIONS OF CORPORATE GOVERNANCE AND FIRM PERFORMANCE

This study provides insights concerning the effects of corporate governance structures on firm performance, focusing on an initial sample of the 500 most capitalized European firms. We argue that corporate governance is a highly situational and contextual concept underlying an interdependent nature. Prior studies attempted to empirically verify the theoretical link between specific configurations of governance mechanisms and performance, yet no conclusions could be drawn with regard to the interplay of structural corporate governance arrangements. Against this background we investigate the relationships between the board and audit committee size and meetings in terms of their effect on performance, while taking account of selected firm-specific properties as moderators. Our results are based on two hierarchical regression models that distinguish between the effects on accounting-based and market-based performance. This differentiation finally allows us to derive theoretical contributions by integrating the implications of principal agency and institutional theory, specifically regarding the influence of shareholder expectations on corporate governance design and effectiveness.
AN INVESTIGATION INTO THE EFFECT OF PARTICLE PLATYNESS ON THE STRENGTH OF GRANULAR MATERIALS USING THE DISCRETE ELEMENT METHOD

It has long been recognised that the macroscopic mechanical behaviour of a granular material depends, to differing extents, on micro-mechanical properties such as the particle size distribution, the particle shape, the inter-particle friction angle and the particle strength. However, a systematic investigation of some of these effects is still lacking. In this paper we focus on particle shape, which is one of the fundamental characteristics of a granular material. We build on previous work that used the axes of an equivalent scalene ellipsoid to characterise particle form, one of the three aspects that define particle shape. (The other two being angularity and roughness.) We use DEM simulations to investigate the effect of particle form, and in particular of particle platyness, on the friction angle of a granular material at critical state. It is found that a deviation of particle shape from that of a sphere leads to higher angles of friction; quantities such as fabric, average rates of particle rotation and interparticle sliding are used to provide insights into the underlying micromechanics.
Using TSVs for thermal mitigation in 3D circuits: Wish and truth

3D technology is envisioned to offer advanced integration capabilities, enabling heterogeneous system integration and offering improved performance and reduced power consumption thanks the so-called Through Silicon Vias (TSVs). Nevertheless, 3D integration is facing strong thermal issues due to its higher power density and reduced heat dissipation properties. In previous studies, it has been often reported the use of TSV insertion techniques for thermal mitigation in 3D stacked circuits. However, due to the thin oxide layer isolating TSVs from silicon substrate, the expected thermal mitigation is actually not effective for the current TSV technologies. This paper firstly provides an analytical study to project the potential benefits and drawbacks of using TSVs for thermal mitigation. Detailed FEM simulations and experimental silicon data from a dedicated thermal test chip are then used to confirm the projections and demonstrate that TSVs may even increase the temperature of hotspots. This paper secondly reports the study of the thermal performance of multiple TSV arrays using thermal simulations for various system-level configurations, including a WideIO compatible 3D circuit. Similar results are obtained where, besides not alleviating thermal issues, TSVs may produce exacerbated hotspots. The results presented in this paper indicate that the use of additional area costly TSVs for thermal mitigation is not worthy.
Diffusion of cytotoxic concentrations of nitric oxide generated luminally at the gastro-oesophageal junction of rats

Background: In humans, high concentrations of nitric oxide are generated luminally at the gastro-oesophageal junction through enterosalivary recirculation of dietary nitrate. Aim: To investigate whether luminal nitric oxide can diffuse into the adjacent digestive tissue and alter tissue integrity. Methods: We designed an animal model using Wistar rats in which physiological concentrations of nitrite and acidified ascorbic acid were administered separately so that the two reactants first meet to form nitric oxide at the gastro-oesophageal junction. Luminal and tissue concentrations of nitric oxide were measured with an electrode and an electron paramagnetic resonance spectrometer, respectively. Concentrations of glutathione in the tissue were measured as a marker of nitrosative stress. Results: High concentrations of luminal nitric oxide were generated locally at the gastro-oesophageal junction of nitrite administered rats, reproducing a phenomenon observed in humans. High levels of nitric oxide were also detected largely in the superficial epithelium of the gastro-oesophageal junction. The concentration of tissue glutathione at the gastro-oesophageal junction was significantly lower in nitrite administered rats compared with control rats, whereas that in the distal stomach was similar in the two rat groups. Conclusions: Using an animal model, this study demonstrated that nitric oxide generated in the lumen diffuses into the adjacent gastric tissue to a substantial degree, leading to localised consumption of glutathione in the tissue. Nitrosative stress induced by this mechanism may be involved in the high prevalence of inflammation and metaplasia, and subsequent development of neoplastic disease at this site.
PePPer: Fine-Grained Personal Access Control via Peer Probing

Users share data on multiple platforms where access control is managed according to the platform-specific policy. However, some data is conceptually owned by peers in a manner that is platform-independent. To enable peers to manage access control rights on such data we introduce PePPer, a tool for fine-grained, personal access control. This system, which runs on the client side, enables loading data items from different sources and annotating them with fine-grained access control requirements via provenance-style Boolean expressions. These expressions are evaluated to decide whether the client is allowed to share the data with a given peer, using a taxonomy that compactly captures data ownership and access control policies. If credentials depend on the unknown access policy of other peers, PePPer probes them to obtain relevant access permissions. For that, we employ efficient algorithms that minimizes the expected number of probes. Our algorithm adapt techniques from stochastic Boolean evaluation to our setting by accounting for multiple peers, policies, expressions and the access control taxonomy. Throughout this process, PePPer further presents to the client a continually updated partial view of the data currently known to be safely shareable. We demonstrate PePPer for the sharing of calendar entries among peers, using as example real-life calendars of politicians and public figures.
Developing and validating two new forms of the VLT

The Vocabulary Levels Test (Nation, 1983; Schmitt, Schmitt, & Clapham, 2001) indicates the word frequency level that should be used to select words for learning. The present study involves the development and validation of two new forms of the test. The new forms consist of five levels measuring knowledge of vocabulary at the 1000, 2000, 3000, 4000, and 5000 levels. Items for the tests were sourced from Nation’s (2012) BNC/COCA word lists. The research involved first identifying quality items using the data from 1,463 test takers to create two equivalent forms, and then evaluating the forms with the data from a further 250 test takers. This study also makes an initial attempt to validate the new forms using Messick’s (1989, 1995) validity framework.
Do Women Perceive Hate Differently: Examining the Relationship Between Hate Speech, Gender, and Agreement Judgments

Understanding hate speech remains a significant challenge for both creating reliable datasets and automated hate speech detection. We hypothesize that being part of the targeted group or personally agreeing with an assertion substantially effects hate speech perception. To test these hypotheses, we create FEMHATE – a dataset containing 400 assertions that target women. These assertion are judged by female and male subjects for (i) how hateful these assertions are and (ii) for whether they agree with the assertions. We find that women and men consistently evaluate extreme cases of hate speech. We also find a strong relationship between hate speech and agreement judgments, showing that a low agreement score is a prerequisite for hate speech. We show how this relationship can be used for automatic hate speech detection. Our best system based on agreement judgments outperforms a baseline SVM classifier (equipped with ngrams) by a wide margin.
eological , tomographic , kinematic and geodynamic constraints on he dynamics of sinking slabs

We use geodynamic models with imposed plate velocities to test the forward-modeled history of subduction based on a particular plate motion model against alternative seismic tomography models. We utilize three alternative published reference frames: a hybrid moving hotspot-palaeomagnetic, a hybrid moving hotspot-true polar wander corrected-palaeomagnetic, and a Subduction Reference Frame, a plate model including longitudinal shifts of subduction zones by matching subduction volumes imaged by Pwave tomography, to assess which model best predicts present day mantle structure compared with seismic tomography and volumetrically derived subduction history. Geodynamic modeling suggests paleo-longitudinal corrections applied to the Subduction Reference Frame result in lower mantle slab material beneath North America and East Asia accumulating up to 10–15◦ westward of that imaged by tomography, whereas the hybrid models develop material offset by 2–9◦. However, the Subduction Reference Frame geodynamic model produces slab material beneath the Tethyan Domain coinciding with slab volumes imaged by tomography, whereas the hybrid reference frame models do not, suggesting regional paleo-longitudinal corrections are required to constrain slab locations. We use our models to test inferred slab sinking rates in the mantle focusing on well-constrained regions. We derive a globally averaged slab-sinking rate of 13 ± 3 mm/yr by combining the ages of onset and cessation of subduction from geological data and kinematic reconstructions with images of subducted slabs in the mantle. Our global average slab-sinking rate overlaps with the 15–20 mm/yr rate implied by mantle convection models using a lower mantle viscosity 100 times higher than the upper mantle.
Chapter 2 Surface Plasmon Enhanced Solid-State Light-Emitting Devices

A novel method to enhance light emission efficiencies from solid-state materials was developed by the use of surface plasmon (SP). A 17-fold increase in the photoluminescence (PL) intensity along with a 7-fold increase in the internal quantum efficiency (IQE) of light emission from InGaN/GaN quantum wells (QWs) was obtained when nanostructured silver layers were deposited 10 nm above the QWs. A 32-fold increase in the spontaneous emission rate of InGaN/GaN at 440 nm probed by the time-resolved PL measurements was also observed. Likewise, both light emission intensities and rates were enhanced for organic materials, CdSebased nanocrystals, and also Si/SiO2 nanostructures. These enhancements should be attributed to the SP coupling. Electron–hole pairs in the materials couple to electron vibrations at the metal surface and produce SPs instead of photons or phonons. This new path increases the spontaneous emission rate and the IQEs. The SP-emitter coupling technique would lead to super bright and high-speed solid-state light-emitting devices that offer realistic alternatives to conventional fluorescent light sources.
A 240-mW 2.1-GS/s 52-dB SNDR Pipeline ADC Using MDAC Equalization

This paper introduces multiplying digital-to-analog converter (MDAC) equalization, a digital correction technique for pipeline ADCs that corrects MDAC gain, settling, and other dynamic errors using successive digital FIR filters operating on sub-ADC output samples. This technique reduces the required MDAC residue amplifier (RA) bandwidth relative to the sampling frequency, thereby reducing ADC power. MDAC equalization is demonstrated in a 240-mW 2.1-GS/s ping-pong pipeline ADC in 40-nm CMOS where MDAC RA power is reduced from 175 to 53 mW by 70%. The ADC achieves 58 dB SNR and 52 dB SNDR.
Evidence for a Genetically Stable Strain ofCampylobacter jejuni

ABSTRACT The genetic stability of selected epidemiologically linked strains of Campylobacter jejuni during outbreak situations was investigated by using subtyping techniques. Strains isolated from geographically related chicken flock outbreaks in 1998 and from a human outbreak in 1981 were investigated. There was little similarity in the strains obtained from the different chicken flock outbreaks; however, the strains from each of three chicken outbreaks, including strains isolated from various environments, were identical as determined byfla typing, amplified fragment length polymorphism (AFLP) analysis, and pulsed-field gel electrophoresis, which confirmed the genetic stability of these strains during the short time courses of chicken flock outbreaks. The human outbreak samples were compared with strain 81116, which originated from the same outbreak but has since undergone innumerable laboratory passages. Two main AFLP profiles were recognized from this outbreak, which confirmed the serotyping results obtained at the time of the outbreak. The major type isolated from this outbreak (serotype P6:L6) was exemplified by strain 81116. Despite the long existence of strain 81116 as a laboratory strain, the AFLP profile of this strain was identical to the profiles of all the other historical P6:L6 strains from the outbreak, indicating that the genotype has remained stable for almost 20 years. Interestingly, the AFLP profiles of the P6:L6 group of strains from the human outbreak and the strains from one of the recent chicken outbreaks were also identical. This similarity suggests that some clones ofC. jejuni remain genetically stable in completely different environments over long periods of time and considerable geographical distances.
3-D trajectory tracking control of an underactuated AUV

Most of the AUVs are belongs to underactuated mechanical systems, the tracking control problem of underactuated AUVs attracts many research concerns. In this paper, based on the error dynamics model in reference coordinate system a 3-D trajectory tracking controller for an underactuated AUV is developed by utilizing Lyapunov stability and Backstepping technique, as well as providing a guidance-based approach. The numerical simulations show the effectiveness of the proposed control algorithm.
Wireless microsystems ( MSTs ) for biomedical applications : a review with the state-ofthe-art

This paper presents the state-of-the-art related to wireless microsystems for biomedical application. Aspects including the radio-frequency systems, data acquisition, application specificities (especially in the context of implantable devices), power consumption and their integration are presented. A review of new concepts concerning the technologies and implementations are also presented.
Big Data Analytics for Program Popularity Prediction in Broadcast TV Industries

The precise and timely prediction of program popularity is of great value for content providers, advertisers, and broadcast TV operators. This information can be beneficial for operators in TV program purchasing decisions and can help advertisers formulate reasonable advertisement investment plans. Moreover, in terms of technical matters, a precise program popularity prediction method can optimize the whole broadcasting system, such as the content delivery network strategy and cache strategy. Several prediction models have been proposed based on video-on-demand (VOD) data from YouKu, YouTube, and Twitter. However, existing prediction methods usually require a large quantity of samples and long training time, and the prediction accuracy is poor for programs that experience a high peak or sharp decrease in popularity. This paper presents our improved prediction approach based on trend detection. First, a dynamic time warping-distance-based $K$ -medoids algorithm is applied to group programs’ popularity evolution into four trends. Then, four trend-specific prediction models are built separately using random forests regression. According to the features extracted from an electronic program guide and early viewing records, newly published programs are classified into the four trends by a gradient boosting decision tree. Finally, by combining forecasting values from the trend-specific models and the classification probability, our proposed approach achieves better prediction results. The experimental results on a massive set of real VOD data from the Jiangsu Broadcasting Corporation show that, compared with the existing prediction models, the prediction accuracy is increased by more than 20%, and the forecasting period is effectively shortened.
Evaluation of Long-Term Cloud-Resolving Model Simulations Using 2 Satellite Radiance Observations and Multi-Frequency Satellite Simulators

This paper proposes a methodology known as the Tropical Rainfall Measuring 2 Mission (TRMM) Triple-Sensor Three-step Evaluation Framework (T3EF) for the 3 systematic evaluation of precipitating cloud types and microphysics in a cloud-resolving 4 model (CRM). T3EF utilizes multi-frequency satellite simulators and novel statistics of 5 multi-frequency radiance and backscattering signals observed from the TRMM satellite. 6 Specifically, T3EF compares CRM and satellite observations in the form of combined 7 probability distributions of precipitation radar (PR) reflectivity, polarization-corrected 8 microwave brightness temperature ( Tb ), and infrared Tb to evaluate the candidate CRM. 9 T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases 10 involving the South China Sea Monsoon Experiment (SCSMEX) and Kwajalein 11 Experiment (KWAJEX). This evaluation reveals that the GCE properly captures the 12 satellite-measured frequencies of different precipitating cloud types in the SCSMEX case 13 but underestimates the frequencies of deep convective and deep stratiform types in the 14 KWAJEX case. Moreover, the GCE tends to simulate excessively large and abundant 15 frozen condensates in deep convective clouds as inferred from the overestimated GCE- 16 simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed 17 errors in the GCE’s performance provides the best direction for model improvements.
A project portfolio selection decision support system

The selection and scheduling of a portfolio of projects is a task frequently found among the strategical activities performed by management staff in several industries. When choosing a project to be selected and scheduled into a portfolio, managers have to deal with conflicting criteria, resource constraints, distinct scenarios, and changes during the planning and execution phases. In this work, we describe the functionalities of a decision support system (DSS) prototype designed to help managers make better decisions, when constructing a portfolio of projects. The DSS software was developed to solve portfolio selection problems originating at a large corporation in the power generation industry. It implements a heuristic algorithm, which finds solutions at least 10% better than a manually produced solution for a real-world input instance.
Linear Feynman-Kac Linear PDEs & Non-linear SDEs Information Theoretic Control Free Energy & Relative Entropy Stochastic Optimal Control Stochastic Dynamic Programming

Stochastic partial differential equations have been used to model diverse applications ranging from turbulence and plasma physics to partially observable stochastic controlled systems. This paper aims to bridge the gap between abstract theoretical results and implementable algorithms, by developing a variational optimization framework for controlling infinite-dimensional stochastic models. A measuretheoretic approach is employed for systems evolving on Hilbert spaces, which relies on connections between relative entropy and free energy. The derived scheme is applicable to a wide class of problems, and can be used for optimizing parameterized control policies. Our work creates new perspectives and directions for solving stochastic control problems constrained by partial differential equations.
Using Machine Learning to Address Customer Privacy Concerns : An Application with Clickstream Data

The ever-increasing volume of consumer data provide unprecedented opportunities for firms to predict consumer behavior, target customers, and provide customized service. Recent trends of more restrictive privacy regulations worldwide, however, present great challenges for firms whose business activities rely on consumer data. We address these challenges by applying the recently developed federated learning approach a privacy-preserving machine learning approach that uses a parallelized learning algorithm to train a model locally on each individual user’s device. We apply this approach to data from an online retailer and train a Gated Recurrent Unit recurrent neural network to predict each consumer’s clickstream. We show the firm can predict each consumer’s activities with a high level of accuracy without the need to store, access, or analyze consumer data in a centralized location, thereby protecting their sensitive information.