Miracle-dz/UniFAR-FTUs huggingface dataset:
The Growth of SMEs: An Empirical Investigation of Norwegian Exporters

This thesis consists of two articles. Article one investigates the interdependent relationships between motivation for growth, the firm’s international orientation, its past growth and subsequent performance. Although these construct to a limited degree have been treated in previous literature, hardly any studies have examined their comparative and complementary effects on growth in revenue, employment and export sales. Our findings reveal a close and interdependent relationship between the motivation for growth and the international orientation of the firm: Firms with a strong motivation for growth tend to have a high international orientation and display superior growth both domestically and abroad. The positive connection between international orientation and performance indicate that even though international involvement may be resource demanding and put additional strain on the domestic activities, a high international orientation is positive for overall firm growth. Additionally, our results also reveal that some firms were able to systematically outperform the rest throughout the whole eleven year period. Article two takes a slightly different perspective on firm growth and investigates the importance of R&D activities during a financial crisis. Though several studies have found a positive connection between R&D and subsequent performance in periods of normal growth, existing literature provide limited guidance to managers about the particular role of R&D during a recession. As the external environment of the firm greatly differs during a recession, so may also the importance of R&D activities. Our findings reveal that firms who devoted considerable resources to R&D activities performed significantly better than the rest through the late 2000’s financial crisis. The connection between R&D and performance actually turned out to be comparatively stronger during the financial crisis than in the growth period from 2004-2008. Thus, the importance of R&D activities seems to be accentuated during a financial crisis. For business managers, this implies that R&D activities can serve both to boost growth in normal times and as a way to bolster the firm for the inevitable next recession.
Impact of mobility on QoE in wireless networks. (Rôle du vidéo streaming mobile qui dépend du contexte dans l'amélioration de la qualité d'expérience)

The strong emergence of smartphones on human daily life as well as the highbroadband access supplied by operators have triggered pervasive demand on videostreaming mobile services, requiring the exploration of novel approaches on videocontent delivery. To afford video streaming services at sustainable quality, the idea of adjusting the streaming to the time-varying users’ contexts has been actively investigatedduring the recent years. Since the users’ perceptions on the video qualitydirectly impact their engagement in video streaming sessions, many interests havebeen accorded to the user’s Quality of Experience (QoE). Today streaming solutions mostly rely on the user’s contextual information suchas his link capacity or his available bandwidth to provide an acceptable final QoE.Such contextual information can be easily acquired thanks to the existence of wirelesssensors and dedicated smart applications on today mobile devices. At the core, liesthe idea of exploiting the strong correlation between users’ locations and contexts. Tothat end, radio maps with historical average signal strength have been geographicallymapped. Various studies on users’ mobility patterns also showed that people dailyroutes exhibit a high degree of spatial and temporal regularity, especially on publictransportation or on road ways to/from frequently visited places. Coupled with radiomaps, these mobility patterns can give high accuracy on context predictability alongusers’ trips.In this thesis, we analyse the impact of adapting video streaming to the user’scontext on the final QoE.We start by proposing CAMS (Context Aware Mode Switching),a context-aware resource allocation mechanism, for real (i.e, non adaptive) videostreaming delivery to reduce the number of video stalling. CAMS is designed to beapplied in a particular network topology under a particular mobility of users. Then,we explore the impact of knowing the future throughput variations on video quality adaptation and delivery cost in adaptive video streaming. We propose NEWCAST(aNticipating qoE With threshold sCheme And aScending biTrate levels) as a proactivealgorithm for cost adjustment and quality adaptation under the assumption of aperfect throughput estimation. We then extend the study to the case where throughputprediction errors may exist and propose a bench of adaptive algorithms inspiredfrom NEWCAST. To explore the feasibility of implementing these algorithms in realworld streaming, we conduct some experiments with the DASH-If Reference playerin an emulated environment. Finally, we explore the impact of knowing the futurethroughput variations when exploited with machine learning on the global QoE enhancementin adaptive video streaming. We propose a closed-loop framework basedon users’ feedbacks to progressively learn their QoE profiles and to fittingly optimizevideo deliveries. This framework is in particular suited for heterogenous populationswhere the QoE profiles of users are quite different and unknown in advance.
Coloration, jeux et marquages dans les graphes

Nous etudions plusieurs problemes de coloration dans les graphes, pour certains avec une composante ludique. La coloration a distance 2 d'un graphe est une coloration de ses sommets telle que deux sommets a distance au plus 2 ont des couleurs differentes. Le L(p; q)-etiquetage est une generalisation de ce probleme ou les contraintes a distance 1 et 2 sont differentes. Nous donnons des resultats pour ces deux problemes dans plusieurs classes de graphes peu denses (ayant un faible degre moyen maximum). Le jeu de coloration sur un graphe est un jeu ou deux joueurs, Alice et Bob, colorent tour a tour un des sommets non colories d'un graphe, construisant ainsi une coloration propre partielle de plus en plus etendue de ce graphe. Alice tente d'etendre la coloration a l'ensemble du graphe, et Bob tente de l'en empecher. Nous travaillons sur un invariant de graphe, le degre minmax, dont l'etude permet de deduire des resultats pour le jeu de coloration via l'etude d'un probleme structurel, la (1; k)-decomposition d'un graphe, c'est-a-dire la partition de ses aretes en une foret et un sous-graphe de degre inferieur ou egal a k.Nous travaillons enfin sur une variante du jeu de coloration nommee jeu de coloration d'incidences, ou Alice et Bob colorient les incidences d'un graphe, pour lequel nous donnons une strategie efficace pour Alice.Enfin, tout au long de notre memoire, nous etudions les liens entre la notion de coloration est celle de marquage. Un marquage est un ordre sur les sommets (ou aretes, ou incidences.) d'un graphe possedant des caracteristiques utiles pour le colorer. Pour nos differents problemes, nous questionnons l'utilite ou les limites de l'usage de cette notion.
PFCG: Improving the Restore Performance of Package Datasets in Deduplication Systems

Data deduplication, a lossless data compression technique, has been widely deployed in backup systems to save storage space. However, data fragmentation introduced by data deduplication seriously degrades restore performance. To alleviate the fragmentation problem, rewriting algorithms such as CBR, CAP, and HAR have been proposed. In certain backup datasets containing packed files like UNIX tar files, we have observed that a large amount of rewritten fragmented chunks tend to remain fragmented in following backups and thus get rewritten repeatedly in consecutive backups. Such repeatedly rewritten chunks are referred to as persistent fragmented chunks (PFCs), and they severely impact the efficiency of rewriting algorithms and restore performance. In this paper, we propose PFCG, an efficient scheme to enhance the efficiency of rewriting algorithms and restore performance. The central idea of PFCG is to identify and differentiate PFCs from regular fragmented chunks, i.e., non-PFCs, and store these two types of fragmented chunks into separate containers during backups to prevent the proliferation of PFCs in following backups. Our experimental results based on six real-world datasets demonstrate that PFCG significantly improves the restore performance by 21% to 47% over the state-of-the-art rewriting algorithms, without sacrificing deduplication efficiency.
Generalized Seiberg-Witten equations and hyperKähler geometry

In this thesis we study a certain generalization of the gauge-theoretical Seiberg-Witten equations over a source 4-manifold X. The generalization involves a hyperKahler manifold M with certain symmetries and a nonlinear Dirac operator D acting on equivariant maps u (called spinors) with values in M.We prove a classification theorem for such hyperKahler manifolds and propose a new method for their construction. This allows us to obtain new examples of hyperKahler and closely related to them quaternionic Kahler manifolds. Our construction is quite explicit and, in some cases, this allows to obtain not only existence results but also hyper- and quaternionic- Kahler structures themselves.We also prove that harmonic spinors, i.e. solutions of the equation Du=0, are closely related to solutions of the so-called Cauchy-Riemann-Fueter equation. We then prove that solutions of the Cauchy-Riemann-Fueter equation, which are believed to be a "right" analogue of holomorphic maps in quaternionic context, are exactly those maps, whose differential has no triholomorphic component. Hence we introduce the term "aholomorphic" for such maps. It is also shown that harmonic spinors can be regarded as twisted version (in an appropriate sense) of aholomorphic maps.The last part of the thesis is devoted to the generalized Seiberg-Witten equations over Kahler surfaces. In this case we prove that the space of solutions has a holomorphic description (in the usual complex sense). Further, if X is a product of two holomorphic curves we show (modulo the adiabatic limit conjecture) that there exists a relation between holomorphic curves (in the sense of Gromov theory), the symplectic vortex equations and the generalized Seiberg-Witten equations.
Musical prodigies within the virtual stage of YouTube

Created in February 2005, YouTube has become the most vibrant, popular, and accessible platform for online usercreated video sharing. As such, it is transforming the way many internet users around the world find information of interest to them, thereby shaping the way they make sense of the world and come to acquire beliefs that shape their lives. YouTube has videos on almost every topic, with music of all kinds being an integral feature of the medium. Its importance in a book such as this is that YouTube has become the single largest resource for viewing the talents of musicians of all kinds. Within the resources of YouTube can be found many examples of children who might be classified as “music prodigies” by the general public. For example, any keyword search on “next Mozart” and “music prodigy” will return thousands if not tens of thousands of hits. Headings on these video sites range from descriptions about the performer— “Tiffany Koo (Aged 5) – Chopin Nocturne #20 C Sharp Minor”— to headlines asserting the child’s unique potential and similarity to famous musicians: “The Next Mozart? 6Year Old Piano Prodigy Wows All”. Among the more prominent examples is a TV documentary on the American composer and pianist Emily Bear (b. 2001) that has received almost 24 million viewings since it was uploaded in 2008. Given that YouTube has become the most internationally recognized resource available to showcase the perceived or actual abilities of young musicians, it is appropriate that the examples shown on YouTube are analysed and discussed in a book on music prodigies. This chapter examines YouTube as a medium for accessing videos of young music performers. We start by comparing the characteristics of prodigy performances in the past and present. Young performers today face a particular challenge to stand out as exceptional individuals among the vast volume of competing high level performances that are available on the internet. This issue is discussed with attention to the role of the audiences, and notions of popularity and commercial success. Our aim is to expose some of the stereotypes and myths that will be helpful to the overarching aim of this publication which is to explain some of the attributes and issues which frame the phenomenon that we label “musical prodigy. ” An important part of our discussion will focus on the stark difference between how the general public typically classifies and defines musical prodigies as opposed to the scientific models presented elsewhere in f this book.
Sparse gaussian processes for large-scale machine learning

Gaussian Processes (GPs) are non-parametric, Bayesian models able to achieve state-of-the-art performance in supervised learning tasks such as non-linear regression and classification, thus being used as building blocks for more sophisticated machine learning applications. GPs also enjoy a number of other desirable properties: They are virtually overfitting-free, have sound and convenient model selection procedures, and provide so-called “error bars”, i.e., estimations of their predictions’ uncertainty. Unfortunately, full GPs cannot be directly applied to real-world, large-scale data sets due to their high computational cost. For n data samples, training a GP requires O(n3) computation time, which renders modern desktop computers unable to handle databases with more than a few thousand instances. Several sparse approximations that scale linearly with the number of data samples have been recently proposed, with the Sparse Pseudo-inputs GP (SPGP) representing the current state of the art. Sparse GP approximations can be used to deal with large databases, but, of course, do not usually achieve the performance of full GPs. In this thesis we present several novel sparse GP models that compare favorably with SPGP, both in terms of predictive performance and error bar quality. Our models converge to the full GP under some conditions, but our goal is not so much to faithfully approximate full GPs as it is to develop useful models that provide high-quality probabilistic predictions. By doing so, even full GPs are occasionally outperformed. We provide two broad classes of models: Marginalized Networks (MNs) and Inter- Domain GPs (IDGPs). MNs can be seen as models that lie in between classical Neural Networks (NNs) and full GPs, trying to combine the advantages of both. Though trained differently, when used for prediction they retain the structure of classical NNs, so they can be interpreted as a novel way to train a classical NN, while adding the benefit of input-dependent error bars and overfitting resistance. IDGPs generalize SPGP by allowing the “pseudo-inputs” to lie in a different domain, thus adding extra flexibility and performance. Furthermore, they provide a convenient probabilistic framework in which previous sparse methods can be more easily understood. All the proposed algorithms are tested and compared with the current state of the art on several standard, large-scale data sets with different properties Their strengths and weaknesses are also discussed and compared, so that it is easier to select the best suited candidate for each potential application.
Real-Time Performance Analysis of a Modern Data-Parallel Stream Processing Engine

Identifying performance bottlenecks of modern, distributed stream processing engines is a serious challenge. At the same time, such engines are widely used to perform data-parallel tasks such as machine learning, graph processing or sophisticated streaming data analysis. Oftentimes, these tasks are required to produce low-latency results as well as to achieve a high throughput. As computations often consist of complex, iterative dataflows and are distributed over multiple physical machines — with hundreds of worker threads in total — finding the source of a performance problem is a difficult task. While profiling can be used to quantify the time spent in the various steps of a parallel computation, it does not take into account the dependencies between the steps. As a result, optimization efforts are often wasted on components that have little to no influence on query latency or the overall runtime of a program. In this work, we offer a more effective alternative by applying critical path analysis, a dependency-aware technique. The critical path is defined as the longest sequence of dependent steps in a parallel program’s execution. Any increase in the execution time of a step on the critical path will therefore result in an equal increase in the total runtime of the computation. We refine existing critical path-based models and apply them to data-parallel systems, which often share common low-level principles. We provide guidelines on the instrumentation necessary to apply our model, as well as a set of trace properties that help verify the correctness of that instrumentation. Furthermore, we develop a novel method to identify phases in a worker thread’s execution during which it is waiting — e.g. for a message from a different worker — even in the absence of blocking system calls. Through critical path analysis, we can then identify performance bottlenecks in system components, dataflow operators as well as in network communication. To demonstrate our ideas, we implemented a prototype system capable of performing a critical path analysis of the Timely Dataflow stream processing engine. We show that our system can effectively identify the factors limiting a data-parallel computation’s overall performance. Furthermore, we demonstrate that our analysis is both efficient and scalable, and can even be performed in real-time in certain configurations.
Seawater Alkalinity Determination by the pH Method

The coefficient fm used in the seawater alkalinity method of Anderson and Robinson (1946), has been redetermined at 25°C. We have found that fH = 0.741 ±0,005 for salinities between 30°/oo and 41 °/oo• The effect of the formation of HS04 and HF of fH and on the hydrogen-ion concentration is discussed; the apparent dissociation constants of HS04 and HF are given for the salinities 26.7°/oo and 34.6°/oo at 25°C. Introduction. "(his work was performed to increase the accuracy of the method of Anderson and Robinson ( 1946) for determining the total alkalinity of seawater and to provide an interpretation of the term fH, which appears in their equation for the alkalinity. In the method of Anderson and Robinson, 25 or 30 ml of 0.01 N HCl are added to I oo ml of sea water. The alkalinity is calculated from the following equation (Anderson and Robinson 1946), using the measured pH of the acidified seawater and a tabulated value of fH, which is an empirical coefficient that is related to the activity coefficient of hydrogen ions: TA= iVoooV·Ni;oo(Va+V)~H. s s H TA = total alkalinity in meq/1 at 25°C; V 8 = volume of seawater m ml; V = volume of HCl; N = normality of HCI; aH = 10-PH. Meaning off H· The activity of an ion (I) can be represented in two equivalent ways: a1 = (f1h(l)F or a1 = (f1h(lh (Pytkowicz et al. 1966). The subscripts F and T refer to the free species and the total (free plus complexed) species, respectively, and f is the activity coefficient. Garrels ( 1967) showed that free1. Accepted for publication and submitted to press 18 August 1969.
Make Up Your Mind: Towards Consistent Answer Predictions in VQA Models

Visual Question Answering (VQA) involves answering natural language questions on images [1]. While state-of-the-art models can answer such questions satisfactorily well on a standard VQA dataset [1], we observe that it still often makes blatant mistakes while answering questions from slightly different perspectives. This reduces their perceived trust and raises concerns as to whether they can represent the semantics of concepts in questions and images accurately. We introduce the task of belief consistency in VQA models, i.e. the task of answering different semantically-grounded questions about a certain concept consistently. For example, if the answer to “is it a vegetarian pizza? ” is “yes”, the answer to “is there pepperoni on the pizza? ” should be “no”. Current VQA datasets do not have such multiple consistent questions about a single concept to test the consistency of a model. We propose a simple approach to auto-generate a consistent VQA dataset (ConVQA) on top of Visual Genome [2] that comprises such consistent QA sets. We propose a new performance metric and benchmark a current VQA model on ConVQA. We also release a fine-tuned baseline model that slightly improves both ConVQA and VQA2.0 performance.
A Snapshot of Pre- and Post-intervention Changes Among Cardiovascular Disease Patients Participating in the New Smoking Cessation Program

Background: The study evaluates the changes of pre and post stages of behavioral change, motivation level, and smoking status among cardiovascular disease patients, participating in the new smoking cessation program in Hospital Tengku Ampuan Afzan, Kuantan. Methods: A total of 65 adult cardiac patients were randomly distributed into two groups, i.e., intervention and control group, who were baseline smokers and assessed in three phases. Initially, the first, second, and third phase collected the information about their demographic details, their smoking status, and smoking status using cotinine amylase strip, respectively. Results: It showed that behavioral change was significant for the control group (P value = 0.031), while motivation level improved for both groups (i.e., control, P value = 0.000 and intervention group, P value = 0.001). The smoke quitting status percentage was higher for intervention group 41.9% and lower for control group 20.6%; however, the P value was insignificant for both control group (1.000) and intervention group (0.250). Conclusions: This study suggests a need for more personal testimonial videos to focus on other smoking-related diseases.
BASGD: Buffered Asynchronous SGD for Byzantine Learning

Distributed learning has become a hot research topic, due to its wide application in cluster-based large-scale learning, federated learning, edge computing and so on. Most distributed learning methods assume no error and attack on the workers. However, many unexpected cases, such as communication error and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with attack or error, has recently attracted much attention. Most existing BL methods are synchronous, which will result in slow convergence when there exist heterogeneous workers. Furthermore, in some applications like federated learning and edge computing, synchronization cannot even be performed most of the time due to the online workers (clients or edge servers). Hence, asynchronous BL (ABL) is more general and practical than synchronous BL (SBL). To the best of our knowledge, there exist only two ABL methods. One of them cannot resist malicious attack. The other needs to store some training instances on the server, which has the privacy leak problem. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for BL. BASGD is an asynchronous method. Furthermore, BASGD has no need to store any training instances on the server, and hence can preserve privacy in ABL. BASGD is theoretically proved to have the ability of resisting against error and malicious attack. Moreover, BASGD has a similar theoretical convergence rate to that of vanilla asynchronous SGD (ASGD), with an extra constant variance. Empirical results show that BASGD can significantly outperform vanilla ASGD and other ABL baselines, when there exists error or attack on workers.