Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Reporting to the Board of Directors: Comparison of Norwegian Power Companies and Banks

There is a never-ending debate about practice relevance within management accounting and control research. This dissertation contributes to an understanding of the gap claimed between theory and practice. The concepts of theory and practice are discussed, and the key finding is that there is no unanimous definition of the concepts, something which may contribute to confusion about the content of the gap. Moreover, in addition to having multiple meanings, the concepts are often applied implicitly. In this dissertation, the concepts are considered according to the following: (1) theory as ideas and hypotheses which explain something, and (2) practice as a translation of these ideas into action. The gap is a discrepancy between the ideas and how these ideas are executed. There is an underlying assumption that this gap matters only if theories in some way do not have an impact on managerial decisions. The papers describe certain situations where this gap is present and relevant. Based on a multiple case study, Paper 1 investigates the ideals of risk reporting in light of the concept of enterprise risk management, a concept that is introduced by consultants and promoted by governing bodies. The main finding is that the ideals of enterprise risk management are not implemented in practice. The topic of Paper 2 is the use of investment analysis techniques and choice of cost of capital. The survey shows that net present value and weighted average cost of capital have the most widespread use. The paper discusses possible causes and consequences of the gap between theory and practice among these well-established methods. The purpose of Paper 3 is to study whether the introductory budgeting textbooks have changed over the past 20 years, as expected based on criticism from both academics and practitioners. This bibliographic study of an important bridge between theory and practice finds only minor traces of the critique being incorporated in new editions of textbooks. Paper 4 addresses the question of how cost-based pricing can be performed in the presence of uncertainty and risk. The paper exemplifies one possible strategy for determining internal and external prices in order to reduce the risk of losses that arise from under-allocation of costs. In contrast to the other three papers, this one is normative. In addition, the introduction presents a normative framework which can be applied by researchers to precis in their communication with businesses the purposes for which certain objects are studied. Practitioners can apply the framework when configuring their control package. The introduction closes with specific implications of the dissertation for researchers, lecturers and practitioners, three target groups with different perspectives on management accounting and control. All over the present work also contribute to management accounting and control research by applying mixed-methods and thus crossing paradigms something which is rarely observed in the literature.
Molecular Tug of War Reveals Adaptive Potential of an Immune Cell Repertoire

The adaptive immune system constantly remodels its lymphocyte repertoire for better protection against future pathogens. Its ability to improve antigen recognition on the fly relies on somatic mutation and selective expansion of B lymphocytes expressing high-affinity antigen receptors. However, this Darwinian process inside an individual appears ineffective, hitting a modest ceiling of antigen-binding affinity. Experiment began to reveal that evolving B cells physically extract antigens from presenting cells and that the extraction level dictates clonal expansion; this challenges the prevailing assumption that the equilibrium constant of receptor-antigen binding determines selective advantage. We present a theoretical framework to explore whether, and how, such tug-of-war antigen extraction impacts the quality and diversity of an evolved B cell repertoire. We find that the apparent ineffectiveness of clonal selection can be a direct consequence of the non-equilibrium nature of antigen recognition. Our theory predicts that the physical strength of antigen tethering under tugging forces sets the affinity ceiling. Meanwhile, the model showed that, intriguingly, cells can use force variability to diversify binding phenotype without compromising fitness, thus remaining plastic under resource constraint. These results suggest that active probing of receptor quality via a molecular tug of war during antigen recognition limit the potency of response to the current antigen, but confer adaptive benefit for protection against future variants. Importantly, a saddle point in the fitness landscape of B cell phenotype evolution emerges from the tug-of-war setting, which rationalizes multiple key phenomenology and puts forward a role of active physical dynamics in immune adaptation.
Development of AI-cloud based high-sensitivity wireless smart sensor for port structure monitoring

Regular structural monitoring of port structure is crucial to cope with rapid degeneration owing to its exposure to saline and collisional environment. However, most of the inspections are being done visually by human in irregular-basis. To overcome the complication, lots of research related to vibration-based monitoring system with sensor has been devised. Nonetheless, it was difficult to measure ambient vibration due to port's diminutive amplitude and specify the exact timing of berthing, which is the major excitation source. This study developed a novel cloud-AI based wireless sensor system with high-sensitivity accelerometer M-A352, which has 0.2uG/sqrt(Hz) noise density, 0.003mg of ultra-low noise feature, and 1000Hz of sampling frequency. The sensor is triggered based on either predefined schedule or long rangefinder. After that, the detection of ship is done by AI object detection technique called Faster R-CNN with backbone network of ResNet for the convolution part. Coordinate and size of the detected anchor box is further processed to certify the berthing ship. Collected data are automatically sent to the cloud server through LTE CAT 1 modem within 10Mbps. The system was installed in the actual port field in Korea for few days as a preliminary investigation of proposed system. Additionally, acceleration, slope, and temperature data are analyzed to suggest the possibility of vibration-based port condition assessment.
Spacetime foam: a review

More than 65 years ago, John Wheeler suggested that quantum uncertainties of the metric would be of order one at the Planck scale, leading to large fluctuations in spacetime geometry and topology, which he termed ‘spacetime foam. ’ In this review I discuss various attempts to implement this idea and to test it, both theoretically and, to a lesser extent, observationally.
SmartMocap: Joint Estimation of Human and Camera Motion Using Uncalibrated RGB Cameras

Markerless human motion capture (mocap) from multiple RGB cameras is a widely studied problem. Existing methods either need calibrated cameras or calibrate them relative to a static camera, which acts as the reference frame for the mocap system. The calibration step has to be done a priori for every capture session, which is a tedious process, and re-calibration is required whenever cameras are intentionally or accidentally moved. In this letter, we propose a mocap method which uses multiple static and moving extrinsically uncalibrated RGB cameras. The key components of our method are as follows. First, since the cameras and the subject can move freely, we select the ground plane as a common reference to represent both the body and the camera motions unlike existing methods which represent bodies in the camera coordinate system. Second, we learn a probability distribution of short human motion sequences ($\sim$1 sec) relative to the ground plane and leverage it to disambiguate between the camera and human motion. Third, we use this distribution as a motion prior in a novel multi-stage optimization approach to fit the SMPL human body model and the camera poses to the human body keypoints on the images. Finally, we show that our method can work on a variety of datasets ranging from aerial cameras to smartphones. It also gives more accurate results compared to the state-of-the-art on the task of monocular human mocap with a static camera.
3D Neural Sculpting (3DNS): Editing Neural Signed Distance Functions

In recent years, implicit surface representations through neural networks that encode the signed distance have gained popularity and have achieved state-of-the-art results in various tasks (e.g. shape representation, shape reconstruction, and learning shape priors). However, in contrast to conventional shape representations such as polygon meshes, the implicit representations cannot be easily edited and existing works that attempt to address this problem are extremely limited. In this work, we propose the first method for efficient interactive editing of signed distance functions expressed through neural networks, allowing free-form editing. Inspired by 3D sculpting software for meshes, we use a brush-based framework that is intuitive and can in the future be used by sculptors and digital artists. In order to localize the desired surface deformations, we regulate the network by using a copy of it to sample the previously expressed surface. We introduce a novel framework for simulating sculpting-style surface edits, in conjunction with interactive surface sampling and efficient adaptation of network weights. We qualitatively and quantitatively evaluate our method in various different 3D objects and under many different edits. The reported results clearly show that our method yields high accuracy, in terms of achieving the desired edits, while at the same time preserving the geometry outside the interaction areas.
That Depends - Assessing User Perceptions of Authentication Schemes across Contexts of Use

Choosing authentication schemes for a speciﬁc purpose is challenging for service providers, developers, and researchers. Previous ratings of technical and objective aspects showed that available schemes all have strengths and limitations. Yet, the security of authentication also relies on user perceptions which affect acceptance and user behaviour and can deviate from technical aspects. To shine light on the issue and support researchers, developers, and service-providers confronted with authentication choice, we conducted an in-depth analysis of user perceptions of the password, ﬁngerprint, and a smartphone-based scheme in an online study with 201 participants. As authentication is a secondary task that needs to be evaluated in the context of authentication purpose, we also compared perceptions across four contexts of use with varying sensitivity levels: email accounts, online banking, social networks, and smart homes. The results revealed how perceptions of usability, security, privacy, trust, effort, and qualitative features of the schemes are related to user preferences. The results increase awareness for the inﬂuence of subjective perceptions and have practical implications for decision-makers. They can inform a) the choice between several adequate schemes, b) the authentication design to reduce concerns or security-related misconceptions, and c) the development of context-dependent authentication. ( N =21), Google Home ( N =4) and Homeatic ( N =2). The sensitivity of SH data was rated with M =73.12 ( SD =31.66). Because of the small number of people already using smart home devices, we tested whether the sensitivity rating differed between people owning and people not owning smart home devices. A Mann-Whitney-U test revealed no signiﬁcant differences between smart home owners ( M =77.91, SD =23.83, Md =84.00) and non-smart home owners ( M =73.04, SD =32.31, Md =88.00) with Z =-0.057, p =. 955.
Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks

We propose using a two-layered deployment of machine learning models to prevent adversarial attacks. The first layer determines whether the data was tampered, while the second layer solves a domain-specific problem. We explore three sets of features and three dataset variations to train machine learning models. Our results show clustering algorithms achieved promising results. In particular, we consider the best results were obtained by applying the DBSCAN algorithm to the structured structural similarity index measure computed between the images and a white reference image.
Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user-level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps, automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative $Q$Q-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.
A NEW PROOF OF THE PYTHAGOREAN THEOREM INSPIRED BY NOVEL CHARACTERIZATIONS OF UNITAL ALGEBRAS

A new proof of the Pythagorean Theorem is presented, utilizing George Birkhoﬀ’s version of the postulates of Euclidean geometry as incorpo- rating R . Generalization of the mechanism of proof forwards novel characterizations of real ﬁnite-dimensional unital associative algebras which, along with the accompanying functor, are also presented and applied.
Review of solutions to the Cusp-core problem of the $\Lambda$CDM Model

This review aims at proposing to the field an overview of the Cusp-core problem, including a discussion of its advocated solutions, assessing how each can satisfactorily provide a description of central densities. Whether the Cusp-core problem reflects our insufficient grasp on the nature of dark matter, of gravity, on the impact of baryonic interactions with dark matter at those scales, as included in semi-analytical models or fully numerical codes, the solutions to it can point either to the need for a paradigm change in cosmology, or to to our lack of success in ironing out the finer details of the $\Lambda$CDM paradigm.
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated methods have been developed to address the challenges this problem comprises. In this paper, we propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective, and then augment the extremely label-constrained support set in few-shot classification tasks. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it is able to outperform state-of-the-art methods on four benchmark datasets.
THE EVOLUTION OF PLANET–DISK SYSTEMS THAT ARE MILDLY INCLINED TO THE ORBIT OF A BINARY COMPANION

We determine the evolution of a giant planet–disk system that orbits a member of a binary star system and is mildly inclined with respect to the binary orbital plane. The planet orbit and disk are initially mutually coplanar. We analyze the evolution of the planet and the disk by analytic means and hydrodynamic simulations. We generally find that the planet and the disk do not remain coplanar unless the disk mass is very large or the gap that separates the planet from the disk is very small. The relative planet–disk tilt undergoes secular oscillations whose initial amplitudes are typically of the order of the initial disk tilt relative to the binary orbital plane for disk masses ∼1% of the binary mass or less. The effects of a secular resonance and the disk tilt decay enhance the planet–disk misalignment. The secular resonance plays an important role for disk masses greater than the planet mass. At later times, the accretion of disk gas by the planet causes its orbit to evolve toward alignment, if the disk mass is sufficiently large. The results have several implications for the evolution of massive planets in binary systems.
VREN: Volleyball Rally Dataset with Expression Notation Language

This research is intended to accomplish two goals: The first goal is to curate a large and information rich dataset that contains crucial and succinct summaries on the players' actions and positions and the back-and-forth travel patterns of the volleyball in professional and NCAA Div-I indoor volleyball games. While several prior studies have aimed to create similar datasets for other sports (e.g. badminton and soccer), creating such a dataset for indoor volleyball is not yet realized. The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset. Based on the curated dataset and our descriptive sports language, we introduce three tasks for automated volleyball action and tactic analysis using our dataset: (1) Volleyball Rally Prediction, aimed at predicting the outcome of a rally and helping players and coaches improve decision-making in practice, (2) Setting Type and Hitting Type Prediction, to help coaches and players prepare more effectively for the game, and (3) Volleyball Tactics and Attacking Zone Statistics, to provide advanced volleyball statistics and help coaches understand the game and opponent's tactics better. We conducted case studies to show how experimental results can provide insights to the volleyball analysis community. Furthermore, experimental evaluation based on real-world data establishes a baseline for future studies and applications of our dataset and language. This study bridges the gap between the indoor volleyball field and computer science.
Learning Asynchronous and Error-prone Longitudinal Data via Functional Calibration

In many longitudinal settings, time-varying covariates may not be measured at the same time as responses and are often prone to measurement error. Naive last-observation-carried-forward methods incur estimation biases, and existing kernel-based methods suﬀer from slow convergence rates and large variations. To address these challenges, we propose a new functional calibration approach to eﬃciently learn longitudinal covariate processes based on sparse functional data with measurement error. Our approach, stemming from functional principal component analysis, calibrates the unobserved synchronized covariate values from the observed asynchronous and error-prone covariate values, and is broadly applicable to asynchronous longitudinal regression with time-invariant or time-varying coeﬃcients. For regression with time-invariant coeﬃcients, our estimator is asymptotically unbiased, root-n consistent, and asymptotically normal; for time-varying coeﬃcient models, our estimator has the optimal varying coeﬃcient model convergence rate with inﬂated asymptotic variance from the calibration. In both cases, our estimators present asymptotic properties superior to the existing methods. The feasibility and usability of the proposed methods are veriﬁed by simulations and an application to the Study of Women’s Health Across the Nation, a large-scale multi-site longitudinal study on women’s health during mid-life.
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception

Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing novel uncertainty quantification metrics and actions that interact with the cable to reduce perception uncertainty. We present Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0), a system that autonomously untangles cables approximately 3 meters in length with a bilateral robot using estimates of uncertainty at each step to inform actions. By interactively reducing uncertainty, SGTM 2.0 significantly reduces run-time. Physical experiments with 84 trials suggest that SGTM $2.0$ can achieve 83% untangling success on cables with 1 or 2 overhand and figure-8 knots, and 70% termination detection success across these configurations, outperforming SGTM 1.0 by 43% in untangling accuracy and 200% in completion time. Supplementary material, visualizations, and videos can be found at sites.google. com/view/sgtm2.
Efficient and Near-Optimal Online Portfolio Selection

In the problem of online portfolio selection as formulated by Cover (1991), the trader repeatedly distributes her capital over $ d $ assets in each of $ T>1 $ rounds, with the goal of maximizing the total return. Cover proposed an algorithm, termed Universal Portfolios, that performs nearly as well as the best (in hindsight) static assignment of a portfolio, with an $ O(d\log(T)) $ regret in terms of the logarithmic return. Without imposing any restrictions on the market this guarantee is known to be worst-case optimal, and no other algorithm attaining it has been discovered so far. Unfortunately, Cover's algorithm crucially relies on computing certain $ d $-dimensional integral which must be approximated in any implementation; this results in a prohibitive $ \tilde O(d^4(T+d)^{14}) $ per-round runtime for the fastest known implementation due to Kalai and Vempala (2002). We propose an algorithm for online portfolio selection that admits essentially the same regret guarantee as Universal Portfolios -- up to a constant factor and replacement of $ \log(T) $ with $ \log(T+d) $ -- yet has a drastically reduced runtime of $ \tilde O(d^2(T+d)) $ per round. The selected portfolio minimizes the current logarithmic loss regularized by the log-determinant of its Hessian -- equivalently, the hybrid logarithmic-volumetric barrier of the polytope specified by the asset return vectors. As such, our work reveals surprising connections of online portfolio selection with two classical topics in optimization theory: cutting-plane and interior-point algorithms.