Miracle-dz/UniFAR-FTUs huggingface dataset:
VARIANTS AND TRANSFORMATIONS OF AGENCY COSTS IN MANAGEMENT BUY-OUTS AND BUY-INS OF FAMILY FIRMS

We compare the agency costs of family firms prior to a sale to outside parties with the agency costs after a management buy-out MBO or buy-in MBI by non-family investors. The agency costs of a family firm before a sale influence firm value and the potential changes in agency costs after a sale influence the ability of an MBO or MBI to improve firm profits. In transitions from family to non-family ownership, post-sale agency costs are path dependent and contingent. We present a framework and propositions detailing preand post-sale agency issues as they affect firm value and profits.
MxKernel: Rethinking Operating System Architecture for Many-core Hardware.

The computer systems of the future will be characterised by huge numbers of processing units, complex distributed memory architectures and the integration of new kinds of specialised processor types such as GPGPUs or FPGAs acting as peers to conventional CPUs. These systems will more and more resemble heterogeneous distributed computing networks rather than the homogeneous, uniform single and multi-core systems of the past. In advent of these new hardware systems operating system developers have to rethink how an operating system design has to look like in order to cope with the challenges induced by these systems and their rapid change. In this paper we present a conceptual design for addressing the challenges posed to operating systems by the growing number of processors, the new heterogeneity and distributed memory architectures. Our design is based around the idea of abandoning threads as control-flow abstraction in favour of shorter control flow units with more predictable behaviour and weakening the abstraction of the hardware, thus empowering applications to apply hardware-aware optimisations.
CS224N Final Project: Question Answering with SQuAD 2.0

In this paper we explore deep learning techniques for answering questions in the Stanford SQuAD 2.0 dataset. We want to assess the performance of different deep learning architectures for models that can most accurately answer questions based on context paragraphs. We first extend our baseline bidirectional attention flow model to include character level embeddings in addition to word embeddings, and experiment with various hyperparameters to achieve improvements on the baseline model (these improve the baseline performance by about a point for both F1 and EM scores). Second, we implemented a Match-LSTM with Answer Pointer model, trying to leverage a more complex architecture to improve performance. Due to computational and time limitations (training Match-LSTM took upwards of 50 hours), we were only able to train a single version of the Match-LSTM model and it underperformed our baseline architecture on EM and F1 scores. Further work needs to be done to improve the efficiency and accuracy of our Match-LSTM model.
Evaluating the Impact of Radial Distortion in Medical Structure-from-Motion Applications

Minimally invasive surgeries have become standard medical procedures. In these surgeries the access to the surgical field is made by small surgical ports through which surgical instruments are inserted under the visual guidance of an endoscopic camera. Despite the number of well documented benefits for the patient, such as faster recovery time and less trauma to surrounding tissues, MIS are considerably more difficult to execute than their open-body equivalents due to the limited access to the inner body anatomies. Computer Assisted Systems (CAS) that use the endoscopic video as primary source of data have been extensively investigated to aid the surgeon during navigation. Unfortunately, most CAS invariably rely in recovering the camera motion, which in the context of medical endoscopy present additional difficulties, such as clutter and deformable surfaces, low textured surfaces, difficult illumination conditions, and strong radial distortion. This article evaluates recent methods for feature detection, matching, and tracking in the context of medical endoscopy structure-from-motion. We give special emphasis to methods that compensate for the radial distortion of medical endoscopes. These methods are evaluated using a set of benchmark evaluation criteria designed to Miguel Lourenço Vision-Box, SA, Rua do Casal de Canas, Carnaxide, Portugal E-mail: m.lourencoeb@gmail.com Dan Stoyanovr Center of Medical Image Computing, University College of London, London WC1 2BT, United Kingdom E-mail: danayl.stoyanov@ucl.ac. uk João P. Barreto Institute of Systems and Robotics, Faculty of Sciences and Technology, University of Coimbra E-mail: jpbar@isr.uc. pt evaluate their impact in terms of repeatability and accuracy of the camera motion estimations.
The Urban Heat Island Intensities in Greek cities as a function of the characteristics of the built environment

Greece, with an overall land area of approx. 132,000 km2, is located in southeastern Europe (approximately between latitudes 34° and 42° N, and longitudes 19° and 30° E). It is both a maritime country, with numerous islands and a coastline of over 14,000 km in length, and a mountainous one, with four-fifths of the mainland consisting of mountainous terrain. The climate in Greece is predominantly of the Mediterranean type, with mild and rainy winters, relatively warm and dry summers and sunshine almost all year round.
'Keep the user in mind!' Persuasive effects of social robot as personalized nutritional coach

In this paper we investigate the use of a social robot as an interface for providing personalized information about nutrition. In particular, we evaluated the effect of message adaptation to some demographic traits that are automatically recognized by a social robot, Pepper in this case. The proposed approach is based on soft biometrics and it can estimate several traits simultaneously, such as gender and age of people in the field of view of the robot. Our hypothesis is that adding this capability to a social robot improves the persuasive effects, in terms of perceived informational quality, motivational strength and social believability, and the recall of the message. We performed a prelimi-nary experimental study and its results seem to support the persuasiveness of the personalized nutritional coach.
Mechanics of minimum variance investment approach

Bruno Monnier Quantitative analyst bruno.monnier@ossiam.com Ksenya Rulik, PhD, CFA Head of Quantitative research ksenya.rulik@ossiam.com Minimum variance portfolios constructed via mathematical optimization allow to significantly reduce equity risk. Minimum variance optimization, though bearing a certain level of complexity, cannot be regarded anymore as a ”black box” process. We will argue in this paper that properly designed minimum variance investment approach can be relevant for investors seeking risk-efficient passive equity allocation, that it can be made transparent to investors by giving them more disclosure and intuition on the resulting optimized allocation and the choices made in by the optimization procedure. We discuss here how the minimum variance engine works and how to structure the optimization procedure in order to keep the minimum variance portfolio diversified. Mechanics of minimum variance investment approach
How Accurate are ECG Parameters from Wearable Single-lead ECG System for 24-hours Monitoring

Wearable electrocardiogram (ECG) devices have been quickly developed for convenient long-term monitoring. To further verify the accuracy of textile electrode based long-term wearable ECG analysis, a wearable ECG device was used to record 24-hours long-term ECGs simultaneously with a Holter monitor. Clinical parameters were derived from the wearable ECGs, and were compared with the reports from the Holter monitor. Specifically, ECG parameters of the measured total time, the beat number, the slowest heart rate, the mean heart rate, the fastest heart rate, the beat number of tachycardia, the beat number of bradycardias, Heart rate variability (HRV) parameters of SDNN, SDANN, RMSSD, PNN50, as well as the detection of premature atrial contraction (PAC) and premature ventricular contraction (PVC), were analysed and compared. Mean relative errors (MREs) of ECG parameters between the wearable ECG analysis and Holter report were all less than 10 % except the times of bradycardias (13.97 %). MREs for HRV parameters were all less than 14 %, and MREs for counting premature atrial contraction (PAC) and premature ventricular contraction (PVC) were 61.60 and 395.95 %, respectively. The results showed that ECG and HRV parameters from wearable ECGs were comparable to the Holter monitor, while there was large bias for PAC and PVC detection.
Classifying Blogs Using NLP: Challenges and Pitfalls

Blogs are difficult to categorize by humans and by machines alike. In the early days of the web, directories maintain by human labor could not keep up the growth of the web; likewise, blog directories cannot keep up with the explosive growth of blogs. Yet machines learning methods cannot accurately categorize blogs because blog are written in an informal, capricious style. We design a natural language processing (NLP) experiment for categorizing one hundred and twenty blogs into four topic groups: personal diary, politics, news, and sports. The baseline measure is term document frequency. For comparison, we extract two linguistic features: the title of blog entries and the anchor text from inbound links. While these features perform worst than the baseline, they illustrate the challenges involved in designing a blog classification algorithm. We analyze the corpus, features, and result data to formulate a more robust system and explore the benefits and limitations of NLP in organizing blog content.
Fronthaul Compression and Precoding Optimization for NOMA-Based Joint Transmission of Broadcast and Unicast Services in C-RAN

This paper tackles a non-orthogonal multiplexing (NOM)-based realization of broadcast-unicast overlay transmission in cloud-radio access networks (C-RANs). In particular, fronthaul compression and precoding are jointly optimized to maximize the throughput in the considered network. The problem further optimizing the power allocation between broadcast and unicast signals is addressed as well. The algorithmic solutions are obtained by means of majorization minimization (MM)-type difference-of-convex (DC) programming. Moreover, the proposed solutions are further extended to cope with the uncertainty of channel state information (CSI) to enable robust transmission. Computer simulations are performed to verify that the proposed scheme attains substantial achievable rate gain over the conventional decentralized precoding in entire parameter regions considered.
Unsupervised Formal Grammar Induction with Confidence

I present a novel algorithm for minimally supervised formal grammar induction using a linguistically-motivated grammar formalism. This algorithm, called the Missing Link algorithm (ML), is built off of classic chart parsing methods, but makes use of a probabilistic confidence measure to keep track of potentially ambiguous lexical items. Because ML uses a structured grammar formalism, each step of the algorithm can be easily understood by linguists, making it ideal for studying the learnability of different linguistic phenomena. The algorithm requires minimal annotation in its training data, but is capable of learning nuanced data from relatively small training sets and can be applied to a variety of grammar formalisms. Though evaluating an unsupervised syntactic model is difficult, I present an evaluation using the Corpus of Linguistic Acceptability and show state-of-the-art performance.1
Neural Network Approach for Irony Detection from Arabic Text on Social Media

Irony plays an important part in human social interaction which is used to emphasize occurrences that deviate from the expected. Humans manipulate each other in a very negative way by writing the opposite of what they mean. However, irony detection is a complex task even for humans. In this research, we study the problem of irony detection as a classification problem and utilized the dataset offered by the IDAT workshop. We also propose a classification system for detecting irony in the Arabic tweets using neural networks 1 .
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability

In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed. Specifically, a Convolutional Neural Network (CNN) predicts a waypoint that is used by the dynamics model for planning and tracking a trajectory to the waypoint. However, the CNN inevitably makes prediction errors which often lead to collisions in cluttered and tight spaces. In this paper, we present a novel Hamilton-Jacobi (HJ) reachability-based method to generate supervision for the CNN for waypoint prediction in an unseen environment. By modeling CNN prediction error as "disturbances" in robot's dynamics, our generated waypoints are robust to these disturbances, and consequently to the prediction errors. Moreover, using globally optimal HJ reachability analysis leads to predicting waypoints that are time-efficient and avoid greedy behavior. Through simulations and hardware experiments, we demonstrate the advantages of the proposed approach on navigating through cluttered, narrow indoor environments.
On the structure of divergence-free measures on $\mathbb R^2$

We consider the structure of divergence-free vector measures on the plane. We show that such measures can be decomposed into measures induced by closed simple curves. More generally, we show that if the divergence of a planar vector-valued measure is a signed measure, then the vector-valued measure can be decomposed into measures induced by simple curves (not necessarily closed). As an application we generalize certain rigidity properties of divergence-free vector fields to vector-valued measures. Namely, we show that if a locally finite vector-valued measure has zero divergence, vanishes in the lower half-space and the normal component of the unit tangent vector of the measure is bounded from below (in the upper half-plane), then the measure is identically zero.
When Nash Meets Stackelberg

This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous noncooperative games where the players solve sequential Stackelberg games. Specifically, each player solves a Stackelberg game where a leader optimizes a (parametrized) linear objective function subject to linear constraints, whereas its followers solve convex quadratic problems subject to the standard optimistic assumption. Although we prove that deciding if a NASP instance admits a Nash equilibrium is generally a [Formula: see text]-hard decision problem, we devise two exact and computationally efficient algorithms to compute and select Nash equilibria or certify that no equilibrium exists. We use NASPs to model the hierarchical interactions of international energy markets where climate change aware regulators oversee the operations of profit-driven energy producers. By combining real-world data with our models, we find that Nash equilibria provide informative, and often counterintuitive, managerial insights for market regulators. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022. 03418 .
The CoCo-Beholder: Enabling Comprehensive Evaluation of Congestion Control Algorithms

The recent endeavors of the research community to unite efforts on the design and evaluation of congestion control algorithms have created a growing collection of congestion control schemes called Pantheon. However, the virtual network emulator that comes with the collection has very limited capabilities: it can run flows of only one scheme at once, and the flows cannot have individual network settings, as the topology is point-to-point. This thesis addresses those limitations and presents CoCo-Beholder, a human-friendly emulator providing the popular dumbbell topology of any size, each link of which may have individual rate, delay, and queue size. The central link of the topology may also have a variable delay with optional jitter. Flows of different schemes may run between the halves of the topology at once, and for each flow, the direction and starting time can be chosen. CoCo-Beholder's reliability is ensured by testing schemes in the dumbbell topology of size one and comparing the results against those of Pantheon emulator. With CoCo-Beholder, the thesis successfully reproduces experiments from a recent paper that evaluated the fairness and RTT-fairness of schemes using a real hardware dumbbell testbed. Finally, the thesis explores the behavior of schemes under the square-wave delay using CoCo-Beholder's variable delay feature.
ALPHA TOXIN OF STAPHYLOCOCCUS AUREUS CAUSES IN SITU CHONDROCYTE DEATH

Staphylococcus aureus is a highly virulent pathogen and is implicated in approximately 50% of cases of septic arthritis. Studies investigating other S. aureus-related infections have suggested that alpha (Hla), beta (Hlb) and gamma (Hlg) toxins are key virulence factors. In particular, the ‘pore-forming’ alpha toxin is believed to be most potent. In this study, we have assessed the influence of alpha toxin on in situ chondrocyte viability. Osteochondral explants were harvested from the metacarpophalangeal joints of 3-year-old cows and placed into flasks containing Dulbecco9s Modified Eagle9s Medium. The flasks were then inoculated with the following isogenic ‘knockout’ strains of S. aureus: DU5946 (Hla+Hlb-Hlg-) or DU1090 (Hla-Hlb+Hlg+). The explants were incubated (37°C) and stained after 18, 24 and 40hrs with chloromethylfluorescein di-acetate and propidium iodide, labelling living chondrocytes green and dead cells red, respectively. Axial sections were imaged by confocal microscopy and the percentage cell death obtained using Volocity 4 software. The alpha toxin-producing S. aureus caused rapid cell death, with 24.8+/−3.7% at 18hrs and 44.6+/−7.2% at 24hrs. At 40hrs, there was significantly more chondrocyte death (87.4+/−3.6%; p In situ chondrocyte viability was significantly compromised by alpha toxin, with beta and gamma toxins having minimal effect. Further work will clarify the exact mechanism through which this important toxin induces chondrocyte death. Thereafter, it is hoped that targeted treatments can be developed to reduce the extent of cartilage destruction during, and after, an episode of septic arthritis.
On Information Coefficient and Directional Statistics

Cross-sectional "Information Coefficient"(IC) is a widely and deeply accepted measure in portfolio management. In this paper, we propose that IC is a linear operator on the components of a standardized random vector of next period cross-sectional returns. From the probability perspective, IC is a linear combination of the components of a directionally projected degenerated random vector. We deduct a solution to its optimization in expectation and obtain the maximum. Their closed-form expressions are given by directional statistics in a specific condition. Simulation analysis discloses the influence of market information, such as the number of stocks, on IC. The empirical analysis of the Chinese stock market uncovers a set of interesting facts about the standardized vectors of cross-sectional returns and helps to obtain the time series of the measure in the real market. Our research discovers a potential application of directional statistics in finance, reveals the nature of the IC measure, and deepens the understanding of active portfolio management.
Model uncertainty in financial forecasting

Models necessarily capture only parts of a reality. Prediction models aim at capturing a future reality. In this paper we address the question of how the future is constructed (or: imagined) in an investment context where market participants form expectations on the returns of a risky investment. We observe that the participants' model choices are subject to unforeseeable change. The objective of the paper is to demonstrate that the resulting uncertainty may be reduced by incorporating relations among competing models in the estimation process.
Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control

—Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Speciﬁcally, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that uniﬁes the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from ofﬂine samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the ﬁrst to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.
A Robust Non-Linear and Feature-Selection Image Fusion Theory

The human visual perception system has strong robustness in image fusion. This robustness is based on human visual perception system's characteristics of feature selection and non-linear fusion of different features. In order to simulate the human visual perception mechanism in image fusion tasks, we propose a multi-source image fusion framework that combines illuminance factors and attention mechanisms. The framework effectively combines traditional image features and modern deep learning features. First, we perform multi-scale decomposition of multi-source images. Then, the visual saliency map and the deep feature map are combined with the illuminance fusion factor to perform high-low frequency nonlinear fusion. Secondly, the characteristics of high and low frequency fusion are selected through the channel attention network to obtain the final fusion map. By simulating the nonlinear characteristics and selection characteristics of the human visual perception system in image fusion, the fused image is more in line with the human visual perception mechanism. Finally, we validate our fusion framework on public datasets of infrared and visible images, medical images and multi-focus images. The experimental results demonstrate the superiority of our fusion framework over state-of-arts in visual quality, objective fusion metrics and robustness.
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily. Unfortunately, popular NR prediction models perform poorly on real-world distorted pictures. To advance progress on this problem, we introduce the largest (by far) subjective picture quality database, containing about 40, 000 real-world distorted pictures and 120, 000 patches, on which we collected about 4M human judgments of picture quality. Using these picture and patch quality labels, we built deep region-based architectures that learn to produce state-of-the-art global picture quality predictions as well as useful local picture quality maps. Our innovations include picture quality prediction architectures that produce global-to-local inferences as well as local-to-global inferences (via feedback). The dataset and source code are available at https: //live.ece. utexas.edu/research.php.