Miracle-dz/UniFAR-FTUs huggingface dataset:
The Data Base for Advances in Information Systems Common Misunderstandings of Deterrence Theory in Information Systems Research and Future Research Directions

In the 1980s, information systems (IS) borrowed deterrence theory (DT) from the field of criminology to explain information security behaviors (or intention). Today, DT is among the most commonly used theories in IS security research. Our review of IS research applying DT highlights that many fundamental assumptions of DT are unrecognized and therefore unexamined. This may have resulted in misunderstandings and conceptual confusions regarding some of the basic concepts of DT. For example, some IS studies confuse general deterrence with specific deterrence or do not recognize the difference between the two. Moreover, these fundamental assumptions, when directly examined, may provide important information about the applicability of DT in certain IS security contexts. This research commentary aims to identify and discuss some of the fundamental assumptions of DT and their implications for IS research. By examining these assumptions, IS researchers can study the previously unexplored aspects of DT in different IS contexts. Further, by recognizing these assumptions, IS scholars can revise them and build new variants of DT to better account for specific characteristics of IS behaviors and contexts.
The moderating influence of school performance on intelligence in young adulthood

Education has been suggested to be possibly the most consistent, robust, and durable method available for raising intelligence, but little is known about the genetic and environmental interplay underlying this association. Therefore, we investigated how school achievement, as measured by grade point average in lower secondary school at 15 years of age, moderated intelligence variation in young adulthood. The sample consisted of all Danish male twin pairs who had left lower secondary school since 2002 and appeared, typically at age 18, before a draft board through 2015 ( N = 2,660). Shared environmental variance unique to intelligence (10% of total variance) was found to be greater among individuals with poor school achievement. However, school achievement did not moderate the genetic influences or the non-shared environmental influences on intelligence. We discuss the implications of this in light of the constraints imposed by the statistical models we used.
University of Groningen No difference in gait between posterior cruciate retention and the posterior stabilized design after total knee arthroplasty

Citation for published version (APA): van den Boom, L. G. H. , Halbertsma, J. P. K. , van Raaij, J. J. A. M. , Brouwer, R. W. , Bulstra, S. K. , & van den Akker-Scheek, I. (2014). No difference in gait between posterior cruciate retention and the posterior stabilized design after total knee arthroplasty. Knee Surgery, Sports Traumatology, Arthroscopy, 22(12), 3135-3141. https://doi.org/10.1007/s00167-014-3215-y
Discrete analysis for the America west coast rainfall predictability using El Niño/Southern Oscillation relationships

poster -- Universidad de Costa Rica. Centro de Investigaciones Geofisicas, 2013. Sugerencia para referencias: - Cid, L. , S. Ramirez, E. Alfaro & D. Enfield, 2013. Discrete analysis for the America west coast rainfall predictability using El Nino/Southern Oscillation relationships. Memorias del 3er Congreso de Oceanografia Fisica, Meteorologia y Clima del Pacifico Suroriental. Sesion de Variabilidad Climatica (poster VC.P3). Santiago, Chile. 16-18 de octubre de 2013.
Single-cell profiling reveals inflammatory polarization of human carotid versus femoral plaque leukocytes

Femoral atherosclerotic plaques are less inflammatory than carotid plaques histologically, but limited cell-level data exist regarding comparative immune landscapes and polarization at these sites. We investigated intraplaque leukocyte phenotypes and transcriptional polarization in 49 patients undergoing femoral (n = 23) or carotid (n = 26) endarterectomy using single-cell RNA-Seq (scRNA-Seq; n = 13), flow cytometry (n = 24), and IHC (n = 12). Comparative scRNA-Seq of CD45+-selected leukocytes from femoral (n = 9; 35,265 cells) and carotid (n = 4; 30,655 cells) plaque revealed distinct transcriptional profiles. Inflammatory foam cell–like macrophages and monocytes comprised higher proportions of myeloid cells in carotid plaques, whereas noninflammatory foam cell–like macrophages and LYVE1-overexpressing macrophages comprised higher proportions of myeloid cells in femoral plaque (P < 0.001 for all). A significant comparative excess of CCR2+ macrophages in carotid versus plaque was observed by flow cytometry in a separate validation cohort. B cells were more prevalent and exhibited a comparatively antiinflammatory profile in femoral plaque, whereas cytotoxic CD8+ T cells were more prevalent in carotid plaque. In conclusion, human femoral plaques exhibit distinct macrophage phenotypic and transcriptional profiles as well as diminished CD8+ T cell populations compared with human carotid plaques
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition

Automatic Speech Recognition systems have been shown to be vulnerable to adversarial attacks that manipulate the command executed on the device. Recent research has focused on exploring methods to create such attacks, however, some issues relating to Over-The-Air (OTA) attacks have not been properly addressed. In our work, we examine the needed properties of robust attacks compatible with the OTA model, and we design a method of generating attacks with arbitrary such desired properties, namely the invariance to synchronization, and the robustness to filtering: this allows a Denial-of-Service (DoS) attack against ASR systems. We achieve these characteristics by constructing attacks in a modified frequency domain through an inverse Fourier transform. We evaluate our method on standard keyword classification tasks and analyze it in OTA, and we analyze the properties of the cross-domain attacks to explain the efficiency of the approach.
Analysing race and sex bias in brain age prediction

Brain age prediction from MRI has become a popular imaging biomarker associated with a wide range of neuropathologies. The datasets used for training, however, are often skewed and imbalanced regarding demographics, potentially making brain age prediction models susceptible to bias. We analyse the commonly used ResNet-34 model by conducting a comprehensive subgroup performance analysis and feature inspection. The model is trained on 1,215 T1-weighted MRI scans from Cam-CAN and IXI, and tested on UK Biobank (n=42,786), split into six racial and biological sex subgroups. With the objective of comparing the performance between subgroups, measured by the absolute prediction error, we use a Kruskal-Wallis test followed by two post-hoc Conover-Iman tests to inspect bias across race and biological sex. To examine biases in the generated features, we use PCA for dimensionality reduction and employ two-sample Kolmogorov-Smirnov tests to identify distribution shifts among subgroups. Our results reveal statistically significant differences in predictive performance between Black and White, Black and Asian, and male and female subjects. Seven out of twelve pairwise comparisons show statistically significant differences in the feature distributions. Our findings call for further analysis of brain age prediction models.
Advanced circuit interface for systems with multiple voltage domains

Multi-level voltage scaling is a highly effective technique for reducing power and matching required speed in an integrated circuit. However, additional circuitry is required at the interfaces of the circuit blocks which operate at different voltage levels. These circuits impose a significant delay overhead and can prohibit the use of multi-voltage scaling at specific critical paths. These paths often cross boundaries of blocks that can otherwise operate at a different supply voltage, eliminating the benefits of multiple voltage domains. A by-pass circuit is proposed to alleviate these timing issues and simultaneously support multi-voltage scaling under specific operating conditions. The new circuit results in performance improvements of up to 89% and power reduction up to 52% compared to traditional level-up and level-down shifters in a 32 nm technology node. Furthermore, greater performance and power savings are demonstrated where more cells are being by-passed, such as the isolation cells.
Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models

This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. This model enables high-resolution reconstruction while simultaneously compressing spatial and intensity point cloud data. The strategy employed in this work utilizes Gaussian mixture models (GMMs) to represent the environment. While prior GMM-based mapping works have developed methodologies to determine the number of mixture components using information-theoretic techniques, these approaches either operate on individual sensor observations, making them unsuitable for incremental mapping, or are not real-time viable, especially for applications where high-fidelity modeling is required. To bridge this gap, this letter introduces a spatial hash map for rapid GMM submap extraction combined with an approach to determine relevant and redundant data in a point cloud. These contributions increase computational speed by an order of magnitude compared to state-of-the-art incremental GMM-based mapping. In addition, the proposed approach yields a superior tradeoff in map accuracy and size when compared to state-of-the-art mapping methodologies (both GMM- and not GMM-based). Evaluations are conducted using both simulated and real-world data. The software is released open-source to benefit the robotics community.
Sampling a Near Neighbor in High Dimensions-Who is the Fairest of Them All?

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points 𝑆 and a radius parameter 𝑟 > 0, the 𝑟 -near neighbor ( 𝑟 -NN) problem asks for a data structure that, given any query point 𝑞 , returns a point 𝑝 within distance at most 𝑟 from 𝑞 . In this paper, we study the 𝑟 -NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance 𝑟 from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH), the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee. In this work, we show that LSH based algorithms can be made fair, without a significant loss in efficiency. We propose several efficient data structures for the exact and approximate variants of the fair NN problem. Our approach works more generally for sampling uniformly from a sub-collection of sets of a given collection and can be used in a few other applications. We also develop a data structure for fair similarity search under inner product that requires nearly-linear space and exploits locality sensitive filters. The paper concludes with an experimental evaluation that highlights the inherent unfairness of NN data structures and shows the performance of our algorithms on real-world datasets. Preliminary versions of the results of this paper were published in [14, 36].
Genetic Spectrum and Distinct Evolution Patterns of SARS-CoV-2

Four signature groups of frequently occurred single-nucleotide variants (SNVs) were identified in over twenty-eight thousand high-quality and high-coverage SARS-CoV-2 complete genome sequences, representing different viral strains. Some SNVs predominated but were mutually exclusively presented in patients from different countries and areas. These major SNV signatures exhibited distinguishable evolution patterns over time. A few hundred patients were detected with multiple viral strain-representing mutations simultaneously, which may stand for possible co-infection or potential homogenous recombination of SARS-CoV-2 in environment or within the viral host. Interestingly nucleotide substitutions among SARS-CoV-2 genomes tended to switch between bat RaTG13 coronavirus sequence and Wuhan-Hu-1 genome, indicating the higher genetic instability or tolerance of mutations on those sites or suggesting that major viral strains might exist between Wuhan-Hu-1 and RaTG13 coronavirus.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog

In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy . The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clariﬁcation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the ﬂy while completing a real-world task.
Democracy, political risks and stock market performance

This study examines the impacts of democracy and political risk on stock market. Using annualized panel data for 49 emerging markets for 2000-2012 we find evidence that democracy and political risk do have impact on stock market returns and the relationship between democracy and political risk is parabolic i.e., there is a threshold level of democracy after which political risk begins to decline. Our also results suggest that decreases in political risk lead to higher returns. JEL classification: G15, G12, F52
Reviewing Multimedia Meeting Records: Current Approaches

There has been increasing interest in recording and annotating meetings for post-hoc review. Typically, a rich meeting record is formed from multichannel audio and video recordings, along with whiteboard markings, personal notes and presented slides. These records are then supplemented with automatically derived data such as a speech transcript, emotions, and gestures. This media rich meeting representation is then presented to user via a meeting browser. This paper examines the current state of meeting browsers and organises browsers according to the types of data that they typically focus on. Following this current meeting browsers are critiqued and areas for future research are discussed.
Modelling under ambiguity with two correlated Choquet-Brownian motions

Modelling under ambiguity in financial and economic models implies a sound characterisation of ambiguity sources. We expand the seminal work of Kast et al. (2014) who first defined Choquet Random Walks (CRW) and Choquet-Brownian Motions (CBM). Their work allows modelling in the presence of a single source of ambiguity and is used in various contexts, such as investment decisions and portfolio choices. As it is often useful (or even imperative) to introduce multiple sources of ambiguity, we expand the Choquet Brownian model for two correlated sources of ambiguity. Using properties of correlation, we first establish key results for correlated dynamically coherent Choquet Random Walks. We extend it to continuous-time for two correlated sources of ambiguity, each represented by a Choquet-Brownian Motion. Thus, we demonstrate that CBM are sufficiently tractable to adapt to more complex model settings, in the presence of uncertainty represented through two correlated sources of ambiguity. We apply our theoretical model to the optimal portfolio choice of traded assets.
Nanotherapeutics: From Laboratory to Clinic

9 781498 707510 90000 K24894 The emergence of nanotherapeutics is attributable to the integration of nanotechnology, recombinant DNA technology, and synthetic organic chemistry with medicine for treating critical human diseases in a more efficient and specific molecular approach than therapy with conventionally designed and formulated drugs. Nanotherapeutics: From Laboratory to Clinic comprehensively discusses the current shortcomings for delivery of classical (small) drugs, macromolecular therapeutics, and recombinant vaccine via the common intravascular and extravascular routes.
Rajeev Motwani (1962-2009)

Rajeev Motwani was a pre-eminent theoretical computer scientist of his generation, a technology thought leader, an insightful venture capitalist, and a mentor to some of the most influential entrepreneurs in Silicon Valley in the first decade of the 21st century. This article presents an overview of Rajeev’s research, and provides a window to his early life and the various influences that shaped his research and professional career—it is a small celebration of his wonderful life and many achievements. ACM Classification: K.2/People, F.2. 2 AMS Classification: 01A70, 68-00
Environmental health in Nicaragua

Globally, an estimated 24 percent of the disease burden (healthy life years lost) and an estimated 23 percent of all deaths (premature mortality) are attributable to environmental risks (World Health Organization, or WHO 2006). The burden of disease is unequally shared, with the children and the poor being particularly affected. Among children between the ages 0 and 14, the proportion of deaths attributable to environmental risks, such as poor water and sanitation, indoor air pollution and vector-borne diseases, is estimated to be as high as 36 percent (WHO 2006). Several key messages have emerged from the process of putting together this study: (i) environmental health risks impose a significant burden on Nicaragua's economy, amounting to 2.6 billion Nicaraguan Cordoba (NIO) or 2.4 percent of the country's Gross Domestic Product (GDP), and result in premature deaths and infections, especially in children under five; (ii) cost-effective interventions to address these environmental health risks exist and should be prioritized in Nicaragua; (iii) country-specific health and environmental data are somewhat limited, especially in the case of air quality, and data collection and monitoring need to be further strengthened; and (iv) the capacity of Ministry of Environment and Natural Resources (MARENA) and Ministry of Health (MINSA) staff to conduct environmental health costing analysis needs to be strengthened through proper training.
Co-Production of a 10-m Cropland Extent Map for Continental Africa using Sentinel-2, Cloud Computing, and the Open-Data-Cube

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current continental cropland extent maps of Africa are either inaccurate, have too coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland extent map for the African continent is therefore recognized as a gap in the current crop monitoring services. Using Digital Earth Africa’s Open Data Cube platform, and working in conjunction with multiple regional African geospatial institutions, we co-develop a 10 metre resolution cropland extent map over the African continent using a Random Forest machine learning classiﬁer and an annual time-series of Sentinel-2 satellite images. Members of the regional African geospatial institutions
Multiresolution Surface Representation Based on Displacement Volumes

We propose a new representation for multiresolution models which uses volume elements enclosed between thedifferent resolution levels to encode the detail information. Keeping these displacement volumes locally constantduring a deformation of the base surface leads to a natural behaviour of the detail features. The correspondingreconstruction operator can be implemented efficiently by a hierarchical iterative relaxation scheme, providingclose to interactive response times for moderately complex models.
A hierarchical representation for human activity recognition with noisy labels

Human activity recognition is an essential task for robots to effectively and efficiently interact with the end users. Many machine learning approaches for activity recognition systems have been proposed recently. Most of these methods are built upon a strong assumption that the labels in the training data are noise-free, which is often not realistic. In this paper, we incorporate the uncertainty of labels into a max-margin learning algorithm, and the algorithm allows the labels to deviate over iterations in order to find a better solution. This is incorporated with a hierarchical approach where we jointly estimate activities at two different levels of granularity. The model is tested on two datasets, i.e., the CAD-120 dataset and the Accompany dataset, and the proposed model shows outperforming results over the state-of-the-art methods.
Understanding the Impact of Ridesharing Services on Traffic Congestion

The advance of GPS technologies and the swift adoption of smartphones has enabled the rapid growth of on-demand ride services. People, being connected to the network anywhere anytime, started to see the benefits of having on-demand access to a ride from multiple competing providers. It was a convenient system for all involved parties. Moreover, not adapting to the new network-based system meant missing out on benefits for the riders and losing the market share for the ride-hailing companies. As a result, as Figure 8.1 illustrates, even traditional street hailing services adopted e-hailing systems quite quickly. On-demand ride-hailing platforms, such as Uber and Lyft, have grown rapidly in the last decade in almost all countries; in some markets, they doubled the number of riders each year. It is fascinating to see a service that did not exist just ten years ago become so significant. It is now the fastest-growing transportation mode, has gained a larger market share than taxis in many cities, and has provided another transportation option and much more flexibility to passengers. It has altered the urban mobility landscape in a significant way. However, just like any other disruptive technology in its early years, it has brought some problems too. Municipal officials are concerned about congestion and pollution, drivers complain about their working terms (as evident in the forum [1]), taxi companies are enraged about unfair competition, and passengers are often agitated with sudden spikes in fares, all suggesting that this new experience is far from being stabilized. One of the primary questions about ridesharing services is whether they decrease or increase traffic congestion (and relatedly carbon dioxide [CO2] emissions). Traffic congestion is a serious and growing concern in modern life in metropolitan areas. The 2019 Urban Mobility Report of the Texas Transportation Institute [3] shows that the national congestion cost in the United States has risen from $75 billion in 2000 to $179 billion in 2017 (both in 2017 dollars), a whopping 139 percent increase in less than two decades that signifies the urgency of addressing this issue. Due to the rapid expansion of ridesharing services in urban areas in the last decade, the question of their impact on traffic congestion and carbon emissions has gained