Miracle-dz/UniFAR-FTUs huggingface dataset:
Disruptive Technologies and the Emergence of Competition

We formalize the phenomenon of disruptive technologies (Christensen, 1997) that initially serve isolated market niches and, as they mature, expand to displace established technologies from mainstream segments. Using a model of horizontal and vertical differentiation with discrete customer segmentation, we show how the threat of disruption varies with the rate of technological advance, the number of firms using each technology, segments sizes, marginal costs, and the ability of firms to price discriminate. We characterize the effect of disruption on prices, market shares, social welfare and innovation incentives. We show that a shift from isolation to disruption lowers prices and increases social welfare, but may either increase or decrease the profits of firms using the new technology. By identifying the drivers and implications of technology competition, we contribute to debates about market definition that are often central in anti-trust deliberations. Moreover, we call into question standard results on the effects of mergers in Cournot models. Prior work finds that, absent efficiency gains, mergers among Cournot competitors lower welfare and are only profitable for the merging firms at high levels of concentration. We show that neither of these results need hold when mergers can alter the boundaries of technology competition.
Report on the Seventh International Workshop on Business Intelligence for the Real Time Enterprise (BIRTE 2013)

This paper reports on the 7th International Workshop on Business Intelligence for the Real Time Enterprise (BIRTE 2013), co-located with the VLDB 2013 conference. The BIRTE workshop series aims at providing a forum for presentation of the latest research results, new technology developments, and new applications in the areas of business intelligence and real time enterprises. Building on the success of the previous BIRTE workshops, co-located with the VLDB conferences in Seoul, Auckland, Lyon, Singapore, Seattle, and Istanbul, the seventh workshop in the series was held in Riva del Garda, Italy, on August 26, 2013. Today, business analytics have to use new data sources and technologies in order for the business to be completely up-to-date. Traditional “in-house” data sources about transactions, sales, and finances still form the cornerstone of business analytics applications, but this is no longer enough. Instead, “Big Data” with high velocity such as tweets and other social network updates and sensor data from RFID, GPS, Bluetooth, etc. must be captured and analyzed instantly to understand the latest customer and market trends. Further, analyzing the past and even the present is no longer enough, so predictive analytics solutions are used to make decisions based on the expected future. These new applications and data sources mean that existing business intelligence methods and techniques must be revisited to provide better e ciency, scalability, expressiveness, and ease-of-use. BIRTE 2013 featured an exciting technical program including two keynotes, an invited industrial talk, a panel, and a number of peer-reviewed papers from di↵erent countries in Europe, Africa, and Asia. Each submission received three reviews from the members of the distinguished program committee consisting of leading researchers in the field from academia and industry. From these submissions, two full research papers and one short position paper, along with two demo papers, were selected for presentation at the conference. Based on the feedback of the reviewers and the feedback at the workshop, the authors have made revised versions of their papers which will be published in a joint post-proceedings volume of BIRTE 2013 and 2014 in the Springer LNBIB series [1]. BIRTE 2013 was extremely well attended, with a peak audience of over 70 persons.
Lucky Scheduling for Energy-Efficient Heterogeneous Multi-Core Systems

Heterogeneous multi-core processors with big/high-performance and small/low-power cores have been proposed as an alternative design to improve energy efficiency over traditional homogeneous multi-cores. We make the case for proportional-share scheduling of threads in heterogeneous processor cores aimed at improving combined energy efficiency and performance. Our thread scheduling algorithm, lucky, is based on lottery scheduling and has been implemented using Linux performance monitoring and thread-to-core affinity capabilities at user-level. Our preliminary results show that lucky scheduling can provide better performance and energy savings over state-of-the-art heterogeneous-aware scheduling techniques.
Rearrangement: From wavelength routed to sliced-spectrum optical networks

Instead of the rigid frequency grid, optical networks are evolving toward a flexible and efficient use of the optical spectrum by slicing the spectrum in a series of frequency slots. The spectrum rearrangement consists in changing the spectral order of wavebands (group of frequency slots that must remain contiguous) while keeping the same distribution plan as specified by routing and spectrum assignment. The goal of spectrum rearrangement is to reduce spectral fragmentation and to maximize the bandwidth freedom to provision new lightpaths by privileging the contiguity of unused frequency slots, i.e. filling spectrum voids. Rearrangement in the described concept was first introduced by the authors for a wavelength-routed network to enhance the performance of a hierarchical structure of waveband granularities. In this paper, wavelength rearrangement is revisited all along with the newly introduced spectrum rearrangement in order to highlight on the benefit of approaching the problem from a global network view.
Complexity-Effective Contention Management with Dynamic Backoff for Transactional Memory Systems

Reducing memory access conflicts is a crucial part of the design of Transactional Memory (TM) systems since the number of running threads increases and long latency transactions gradually appear: without an efficient contention management, there will be repeated aborts and wasteful rollback operations. In this paper, we present a dynamic backoff control algorithm developed for complexity-effective and distributed contention management in Hardware Transactional Memory (HTM) systems. Our approach aims at controlling the restarting intervals of aborted transactions, and can be easily applied to the various TM systems. To this end, we have profiled the applications of the STAMP benchmark suite and have identified those “problem” transactions which repeatedly cause aborts in the applications with the attendant high contention rate. The proposed algorithm alleviates the impact of these repeated aborts by dynamically adjusting the initial exponent value of the traditional backoff approach. In addition, the proposed scheme decreases the number of wasted cycles down to 82% on average compared to the baseline TM system. Our design has been integrated in LogTM-SE where we observed an average performance improvement of 18%.
Web site quality evaluation combining eyetracking and physiologicial measures to self-reported emotions: an exploratory research

Studying Human Computer interaction, standard usability measures have focused on effectiveness, efficiency and satisfaction and do not incorporate emotions [1]. Recently, the importance of the affective quality in the design and in the evaluation of interactive systems has been stressed [2]. With e-commerce and the multiplication of retail Web sites, taking emotions into considerations become crucial as it has been clearly established that consumers are not always rational in their choices, nor utilitarian in their motivations [3]. If there is much more than cognitive information processing going on while consumers are online [4], measuring behavior for interactive systems evaluation has almost exclusively limited its focus to cognitive activities. Models combining emotions with human information processing variables are scarce and when available, they rely on reported data [5]. Research has shown though, that emotions are experienced both at the conscious and preconscious levels [6]. We will present an exploratory project, where we attempted to tackle this complex phenomenon. We triangulated non-consciously informed data derived from eyetracking and physiological measures, with post activity self-report of emotions on a scale to measure emotional reaction of users who are exploring a commercial Web site [7].
Computational approaches for inferring the functions of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.
Exploring the role of phonetic bottleneck features for speaker and language recognition

Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification. For language identification, the features' dense phonetic information is believed to enable improved performance by better representing language-dependent phone distributions. For speaker recognition, the role of these features is less clear, given that a bottleneck layer near the DNN output layer is thought to contain limited speaker information. In this article, we analyze the role of bottleneck features in these identification tasks by varying the DNN layer from which they are extracted, under the hypothesis that speaker information is traded for dense phonetic information as the layer moves toward the DNN output layer. Experiments support this hypothesis under certain conditions, and highlight the benefit of using a bottleneck layer close to the DNN output layer when DNN training data is matched to the evaluation conditions, and a layer more central to the DNN otherwise.
Theory and Algorithm for SPFD-Based Global Rewiring

In this paper we present the theory and the algorithm for SPFDbased global rewiring (SPFD-GR), which allows us to replace a target wire globally in the circuit (by some wire possibly far away from the target). It successfully overcomes the limitation of the existing SPFD-based local rewiring (SPFD-LR) that can only replace a wire with another wire having the same sink node. We apply SPFD-GR to the post-mapping area reduction for LUTbased FPGAs under circuit depth restriction. Experimental results show that the rewiring ability of SPFD-GR, in terms of the number of target wires found to ha ve alternative wires, is 1.45 and 3 times that of SPFD-LR and an ATPG algorithm (with a preliminary experimental flow), respectively, and the run time is quite acceptable. Using partitioning, the SPFD-GR algorithm scales well to large circuits with good synthesis quality.
StackGhost: Hardware Facilitated Stack Protection

Conventional Security have relied on overwriting the saved return pointer on the stack to hijack the path of execution. Under Sun Microsystem's Sparc processor architecture, we were able to implement a kernel modification to transparently and automatically guard application's return pointers. Our implementation called Stack Ghost Open-BSD 2.8 acts as a ghost in the machine. Stack-Ghost advances exploit prevention in that it protects every application run on the system without their knowledge nor does it require their source or binary modification. We will document several of the methods devised to preserve the sanctity of the system and will explore the performance ramifications of Stack Ghost.
Social Modulation during Songbird Courtship Potentiates Midbrain Dopaminergic Neurons

Synaptic transmission onto dopaminergic neurons of the mammalian ventral tegmental area (VTA) can be potentiated by acute or chronic exposure to addictive drugs. Because rewarding behavior, such as social affiliation, can activate the same neural circuitry as addictive drugs, we tested whether the intense social interaction of songbird courtship may also potentiate VTA synaptic function. We recorded glutamatergic synaptic currents from VTA of male zebra finches who had experienced distinct social and behavioral conditions during the previous hour. The level of synaptic transmission to VTA neurons, as assayed by the ratio of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) to N-methyl-D-aspartic acid (NMDA) glutamate receptor mediated synaptic currents, was increased after males sang to females, and also after they saw females without singing, but not after they sang while alone. Potentiation after female exposure alone did not appear to result from stress, as it was not blocked by inhibition of glucocorticoid receptors. This potentiation was restricted to synapses of dopaminergic projection neurons, and appeared to be expressed postsynaptically. This study supports a model in which VTA dopaminergic neurons are more strongly activated during singing used for courtship than during non-courtship singing, and thus can provide social context-dependent modulation to forebrain areas. More generally, these results demonstrate that an intense social encounter can trigger the same pathways of neuronal plasticity as addictive drugs.
Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network

A novel application of neural network approach to classify the fault types and to identify the faulty lines in a non radial power system network is demonstrated in this paper. EMTP software has been used to simulate the transmission line model. The simulation is followed by analysis of the current waveforms using wavelet transform in the MATLAB environment. The entropy values of the current signal obtained from wavelet transform have been fed into the neural network for automatic fault type classification and faulty line identification. It has been found that only three level of decomposition of the current signal is sufficient for analysis of faults. The adaptive discrimination scheme is tested under different types of faults, such as single line to ground faults, line-to-line faults, double line to ground faults and three phase symmetrical faults for different varying fault locations and fault resistances, on a given power system model. Results of performance studies show that the proposed scheme for fault diagnosis is able to classify all the faults and to identify the exact faulty lines on the non radial power system network rapidly and correctly.
A Preliminary Investigation of the Psychological Inventory of Criminal Thinking Styles–Layperson Edition–Short Form

The Psychological Inventory of Criminal Thinking Styles (PICTS) is commonly used to assess criminal thinking (thoughts related to criminal behavior); however, the item wording may not be an appropriate assessment for individuals without a criminal history (laypersons) who still may be at risk of engaging in crime. Therefore, a layperson version of the PICTS may more accurately assess criminal thinking among this group. This study examined the psychometric properties of the PICTS–Layperson–Short Form (PICTS-L-SF). Participants were 619 college students without a criminal justice involvement history. Analyses of the PICTS-L-SF indicated that a bifactor model fit the data better than a one- and two-factor model (general criminal thinking; proactive and reactive criminal thinking). Results provide strong evidence for the reliability and validity of the PICTS-L-SF, suggesting it can be used with individuals who are not criminal justice involved to assess criminal thinking.
Integrating Learning Styles into Adaptive e-Learning System

This paper provides an overview and update on my PhD research project which focuses on integrating learning styles into adaptive e-learning system. The project, firstly, aims to develop a system to classify students’ learning styles through their online learning behaviour. This will be followed by a study on the complex relationship between learning styles, learning supports and learning outcomes. The findings can contribute significantly to the area that is still left with several unanswered questions. In addition, based on the results, meaningful recommendations and suitable online adaptation can also be made to a wide range of stakeholders of the education system.
A fast band matching technique for impedance extraction

We present an efficient technique for the fast and accurate extraction of inductance of large-scale on-chip interconnects. Several simulation techniques exploit the sparsity of L-1 for reducing storage and computational costs during computation. Recently, band matching technique was introduced for sparsification of L-1. We extend the band-matching technique for inductance extraction problems. We introduce an efficient technique to invert the approximate band-matched impedance matrix Rf + jomegaLf. The new technique relies on a numerically stable and efficient representation for inverses of banded matrices. Numerical results show that our approach is both accurate as well as computationally efficient, with orders-of-magnitude improvement over traditional techniques.
Knowledge Management : Enablers and Barriers to Knowledge Sharing in Organizations

In tackling the issue of the factors that can lead to either a barrier or an enabler in knowledge sharing, we came to view these from three perspectives; informational, cultural and technological, and these factors can then be seen in either view by their interpretation. This piece aims to firstly introduce explain the concept of Knowledge Management and explore the barriers and enablers from each of these views. In the case of technological, through the use of an insightful and relevant case study. This will then be concluded with enabling recommendations that have been discovered and drawn throughout the course of this area of study.
Predicting Cerebral Amyloid Angiopathy-Related Intracerebral Hemorrhages and Other Cerebrovascular Disorders in Alzheimer’s Disease

Cerebral amyloid angiopathy (CAA) of amyloid β-protein (Aβ) type is common in Alzheimer’s disease (AD). Aβ immunotherapies have been reported to induce CAA-related intracerebral hemorrhages (ICH) or vasogenic edema. For the purpose of developing a method to predict CAA-related ICH and other cerebrovascular disorders in AD, the biomarkers, and risk factors are reviewed. The biomarkers include (1) greater occipital uptake on amyloid positron emission tomography imaging and a decrease of cerebrospinal fluid Aβ40 levels as markers suggestive of CAA, and (2) symptomatic lobar ICH, lobar microhemorrhages, focal subarachnoidal hemorrhages/superficial siderosis, cortical microinfarcts, and subacute encephalopathy (caused by CAA-related inflammation or angiitis) as imaging findings of CAA-related ICH and other disorders. The risk factors include (1) old age and AD, (2) CAA-related gene mutations and apolipoprotein E genotype as genetic factors, (3) thrombolytic, anti-coagulation, and anti-platelet therapies, hypertension, and minor head trauma as hemorrhage-inducing factors, and (4) anti-amyloid therapies. Positive findings for one or more biomarkers plus one or more risk factors would be associated with a significant risk of CAA-related ICH and other cerebrovascular disorders. To establish a method to predict future occurrence of CAA-related ICH and other cerebrovascular disorders in AD, prospective studies with a large number of AD patients are necessary, which will allow us to statistically evaluate to what extent each biomarker or risk factor would increase the risk. In addition, further studies with progress of technologies are necessary to more precisely detect CAA and CAA-related cerebrovascular disorders.
On the political and democratic preconditions of equal recognition

Alan Patten’s Equal Recognition is a compelling justification of a liberal, procedural conception of recognition. This conception is built upon a convincing conception of moral equality, but it does not offer a full theoretical discussion of recognition. I argue that the liberal recognition provided by Patten is too formal and narrow to address all relevant issues regarding conflicts of recognition in democratic societies. In particular, it does not consider the political and democratic preconditions that should be granted to minority groups or immigrants in order to provide them fair opportunities to effectively (and not only formally) reach equal recognition.
Effects of shoot-level structure on narrowband vegetation indices for Scots pine

We measured simultaneously the spectral albedo of ten Scots pine shoots, and the needles constituting the shoots, in Zurich, Switzerland. Next, we used the spectral information to calculate the photochemical reflectance index (PRI) which allows to retrieve photosynthetic productivity from imaging spectroscopy data. Using p-theory, we showed that PRI values can be scaled between needle and shoot levels using photon recollision probability and the harmonic mean of needle spectral albedo at the wavelengths used in the index. As a comparison, we used narrowband NDVI and demonstrated that despite mathematical similarity, the two indices scale differently. Contrary to NDVI, the needle-level information contained in PRI scaled well to the level of the shoot.
In the Beginning Was the Experience

Danish University of Education, Emdrupvej 101, DK-2400 Copenhagen, lg@dpu.dk Communication Studies, Roskilde University, DK-4000 Roskilde, bruno@ruc.dk The tradition of media reception studies is primarily based on qualitative methods in which the informants are interviewed about their experiences and interpretations of the media or program investigated (Schrøder 2001). Their words are seen as the track to the conscious and unconscious experience from viewing, seeing or reading a media product in a naturalistic setting open for subjective interpretations. The tradition of experimental media reception studies, are in contrast often using quantitative methods. The materials that are tested are often consisting of different versions of the same message or format and the findings are often contrasted to those of a control group. The experimental media receptions studies are relatively few and in the following we will give an overview and seek to characterise the differences through carefully selected prototypes. These differences we see as based primarily on epistemological interests. In this article we are looking at the two traditions and how they may merge, in an experimental reception study with is focused on the informants experiences, and which involve the action of selecting and judging as a process that constitute the experience at a pre-verbal level. In the beginning was – not the word – but the experience. Our aim here is to establish and discuss the concept of experience as the foundation for the experimental reception study. What Is Experimental Reception Studies