Miracle-dz/UniFAR-FTUs huggingface dataset:
Price Discrimination Under Customer Recognition and Mergers

This paper studies the interaction between horizontal mergers and price discrimination by endogenizing the merger formation process in the context of a repeated purchase model with two periods and three firms wherein firms may engage in Behaviour-Based Price Discrimination (BBPD). From a merger policy perspective, this paper's main contribution is two-fold. First, it shows that when firms are allowed to price discriminate, the (unique) equilibrium merger gives rise to significant increases in profits for the merging firms (the ones with information to price-discriminate), but has no effect on the outsider firm's profitability, thereby eliminating the so called "free-riding problem". Second, this equilibrium merger is shown to increase industry profits at the expense of consumers' surplus, leaving total welfare unaffected and, therefore, suggesting that competition authorities should scrutinize with greater zeal mergers in industries where firms are expected to engage in BBPD.
Integral factorial ratios: irreducible examples with height larger than 1

This paper gives over 50 new examples of two-parameter families of integral factorial ratios of height 2. These examples are also shown to be irreducible, in the sense that they do not arise by putting together two factorial ratios of height 1. This article is part of a discussion meeting issue ‘Srinivasa Ramanujan: in celebration of the centenary of his election as FRS’.
Lattice Coding for Downlink Multiuser Transmission

In this thesis, we mainly investigate the lattice coding problem of the downlink communication between a base station and multiple users. The base station broadcasts a message containing each user's intended message. The capacity limit of such a system setting is already well-known while the design of practical coding and modulation schemes to approach the theoretical limit has not been fully studied and investigated in the literature. This thesis attempts to address this problem by providing a systematic design on lattice coding and modulation schemes for downlink multiuser communication systems. The main idea is to exploit the structure property of lattices to harness interference from downlink users.
Conditional Computation for Continual Learning

Catastrophic forgetting of connectionist neural networks is caused by the global sharing of parameters among all training examples. In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example. At one extreme, if each input example uses a disjoint set of parameters, there is no sharing of parameters thus no catastrophic forgetting. At the other extreme, if the parameters are the same for every example, it reduces to the conventional neural network. We then introduce a clipped version of maxout networks which lies in the middle, i.e. parameters are shared partially among examples. Based on the parameter sharing analysis, we can locate a limited set of examples that are interfered when learning a new example. We propose to perform rehearsal on this set to prevent forgetting, which is termed as conditional rehearsal. Finally, we demonstrate the effectiveness of the proposed method in an online non-stationary setup, where updates are made after each new example and the distribution of the received example shifts over time.
Variability in transport of fish eggs and larvae. II. Effects of hydrodynamics on the transport of Downs herring larvae

A 10-layered, finite-volume advection-dispersion model with real-time meteorological and freshwater runoff drivers investigated the interannual differences in the transport of Downs her- ring Clupea harengus L. larvae in the southern North Sea. Simulations were carried out for the win- ters of 1989 and 1996 to 2003. As they grew, the concentrations of herring larvae developed vertical behaviour. Meteorological forcing transported Downs herring larvae to the nursery grounds in the eastern North Sea with large interannual differences. Diel vertical movement was relatively un- important in the transport of larvae in the hydrographically mixed southern North Sea. Year classes with less transport of larvae from the hatching area (which was generally warmer) were associated with greater abundances of young Downs herring in the ICES International Bottom Trawl Survey. This implies that retention, rather than dispersal or delivery of larvae to nursery grounds, is associ- ated with the determination of year class strength in Downs herring.
Deep Learning of Preconditioners for Conjugate Gradient Solvers in Urban Water Related Problems

Solving systems of linear equations is a problem occuring frequently in water engineering applications. Usually the size of the problem is too large to be solved via direct factorization. One can resort to iterative approaches, in particular the conjugate gradients method if the matrix is symmetric positive definite. Preconditioners further enhance the rate of convergence but hitherto only handcrafted ones requiring expert knowledge have been used. We propose an innovative approach employing Machine Learning, in particular a Convolutional Neural Network, to unassistedly design preconditioning matrices specifically for the problem at hand. Based on an in-depth case study in fluid simulation we are able to show that our learned preconditioner is able to improve the convergence rate even beyond well established methods like incomplete Cholesky factorization or Algebraic MultiGrid.
Process Innovation: A Study of Malaysian Small andMedium Enterprises (SMEs)

The main purpose of the paper is to explore process innovation of small and medium enterprises (SMEs) in Malaysia. A survey of 50 Peninsular Malaysiabased manufacturing SMEs was conducted. The respondents were asked questions about their perception on the company’s innovative characteristic which in this study is focusing mainly on process innovation (e.g. new product development, systems and technology). In this study, a definition of innovation was established based on past studies and a systematic approach to measure company innovativeness was adopted. Company innovativeness was measured using ten indicators. The top 20 per cent firms were compared with bottom 80 per cent firms in terms of product innovation management, systems and technology. Means of responses were compared for two sets of companies. The results showed that the characteristics of more innovative SMEs manufacturing firms are perceived to be different as compared to the less innovative SMEs manufacturing firms.
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy

Adversarial robustness has become a central goal in deep learning, both in theory and practice. However, successful methods to improve adversarial robustness (such as adversarial training) greatly hurt generalization performance on the clean data. This could have a major impact on how adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve performance on the clean data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases clean test error from 5.8% to 16.7%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a clean test error of only 6.5%. With our technique, the relative error increase for the robust model is reduced from 187.9% to just 12.1%.
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition

This study introduces PV-RNN, a novel variational RNN inspired by predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how latent variables can learn meaningful representations and how the inference model can transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation—rather than external inputs during the forward computation—are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. As in other variational Bayes RNNs, our model learns by maximizing a lower bound on the marginal likelihood of the sequential data, which is composed of two terms: the negative of the expectation of prediction errors and the negative of the Kullback-Leibler divergence between the prior and the approximate posterior distributions. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on those two terms. We test the model on two data sets with probabilistic structures and show that with high values of the meta-prior, the network develops deterministic chaos through which the randomness of the data is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows us to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure.
On Quasi-Infinitely Divisible Random Measures

In this work we show that a family of quasi-infinitely divisible (QID) completely random measures (CRMs) is dense in the space of all CRMs with respect to convergence in distribution. We prove the same results for point processes with independent increments. Further, we provide a complete general theory for QID stochastic integrals, thus extending the integration theory of Rajput and Rosinski's 1989 paper \cite{RajRos} to the QID case and so improving the recent results of \cite{Pass}. An example on nonparametric Bayesian statistics is provided.
Fixing the train-test resolution discrepancy

Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. We then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if we use extra training data we get 82.5% with the ResNet-50 train with 224x224 images. Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date.
USING REMOTE SENSING AND GIS FOR INTEGRATED COASTAL ZONE MANAGEMENT A CASE STUDY: THE CORAL REEFS IN THE NORTHWESTERN RED SEA (HURGHADA, EGYPT)*

Remote sensing is very suitable for coral reef monitoring as it provides information about the configuration and composition of the coral reef; enables the monitoring of the biophysical parameters of the seas and oceans in which the coral reefs occur; and supports the detection of changes over time of these elements. This remote sensing-derived data is integrated in a Geographic Information System (GIS) together with additional environmental information. The GIS not only forms an inventory of all available information but is also used as a tool to analyze this information in order to support integrated coastal zone management (ICZM). The example is given of a risk assessment map constructed for the coral reefs offshore Hurghada, Egypt, indicating the coastal zones most endangered by human activities. It is shown that about 86% of the marine, coastal area near Hurghada is at medium to high risk of being deteriorated.
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities

This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm, converges to the true location parameter from a randomly initialized point. We establish the explicit convergence rates and sample complexity bounds, revealing their dependence on the signal-to-noise ratio and the tail property of the log-concave distribution. Moreover, we show that this global convergence property is robust under model mis-specification. Our analysis generalizes previous techniques for proving the convergence results for Gaussian mixtures. In particular, we make use of an angle-decreasing property for establishing global convergence of Least Squares EM beyond Gaussian settings, as $\ell_2$ distance contraction no longer holds globally for general log-concave mixtures.
Does the Sensation-Seeking trait differ among participants engaged in sports with different levels of physical risk?

This paper aims to establish to what extent participants engaged in different levels of physical risk sports differ in levels of Sensation Seek- ing (SS) trait. A systematic searching of the literature gave 36 peer- reviewed studies published until September 2010. Results suggested that high scores on Thrill and Adventure Seeking seem characteristic of par- ticipants engaged in sports with high and medium levels of risk, and to a lesser extent of low risk sports as well. High scores on Experience Seeking seem characteristic only of participants engaged in sports with high levels of risk. Scores on Boredom Susceptibility only differ when comparing par- ticipants engaged in high risk with those engaged in low risk sports. Fi- nally, high scores on Disinhibition and high Total score seem characteris- tic of athletes at any level of risk as compared to controls, and of athletes engaged in high risk sports as compared to those engaged in low risk sports. We conclude that the SS scale is a useful tool to assess and inter- pret individual differences in personality that exist between sportspersons practicing sports with different levels of risk.
On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that enlarges the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that our deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.
Dementia, interaction, and bilingualism: An exploratory case study

The unique capability of human beings to communicate through the use of natural language is a skill that is normally developed in the first few years of life and then refined and maintained throughout the lifespan. In certain language areas, though, some deterioration in linguistic skills is evident in normal aging. This typically affects lexical retrieval and comprehension of complex structures (Goral, 2013). A variety of brain diseases commonly associated with advancing age may however impact even more negatively on the individual’s language and/or communication skills. Among these are illnesses that may cause dementia in some form, the most common one being Alzheimer’s disease (Mandell & Green, 2014). Dementia refers to a set of symptoms in different cognitive and linguistic domains, and characteristically, these symptoms are persistent and progressive, causing a deterioration of skills and knowledge. The domains affected are memory, executive functions, language, visual-spatial processing, personality and general behavior and interaction skills (Mandell & Green, 2014). In Norway, an estimated number of 70,000 individuals have a form of dementia (Strand et al., 2014), and the number is expected to increase in the years to come with the general rise of elderly individuals in the population.
Curl-free magnetic fields for stellarator optimization

This paper describes a new and efficient method of defining an annular region of a curl-free magnetic field with specific physics and coil properties that can be used in stellarator design. Three statements define the importance: (1) Codes can follow an optimized curl-free initial state to a final full-pressure equilibrium. The large size of the optimization space of stellarators, approximately fifty externally-produced distributions of magnetic field, makes success in finding a global optimum largely determined by the starting point. (2) The design of a stellarator is actually improved when the central region of the plasma has rapid transport with the confinement provided by a surrounding annulus of magnetic surfaces with low transport. (3) The stellarator is unique among all fusion concepts, inertial as well as magnetic, in not using the plasma itself to provide an essential part of its confinement concept. This permits reliable computational design, which opens a path to faster, cheaper, and more certain achievement of fusion energy.
Scalar Products in Twisted XXX Spin Chain. Determinant Representation

We consider XXX spin-$1/2$ Heisenberg chain with non-diagonal boundary conditions. We obtain a compact determinant representation for the scalar product of on-shell and off-shell Bethe vectors. In the particular case, we obtain a determinant representation for the norm of on-shell Bethe vector and prove orthogonality of the on-shell vectors corresponding to the different eigenvalues of the transfer matrix.
Unified theory of global flow and squirt flow in cracked porous media

Approximations for frequency-dependent and complex-valued effective stiffness tensors of cracked porous media (saturated with a single fluid) are developed on the basis of an inclusion-based model (the T-matrix approach to rock physics) and a unified treatment of the global-flow and squirt-flow mechanisms. Essentially, this study corrects an inconsistency or error related to fluid-mass conservation in an existing expression for the t-matrix (wave-induced deformation) of a communicating cavity, a cavity that is isolated with respect to stress propagation (through the solid matrix) but that can exchange fluid mass with other cavities because of global and/or local pressure gradients associated with passage of a long viscoelastic wave. An earlier demonstration of Gassmann consistency remains valid because the new theory of global flow and squirt flow (which also takes into account solid mechanical effects of stress interaction by us-ing products of communicating t-matrices associated with two-point correlati.
Technology based firm's financing: How venture capital nurture firms

Universally, Venture Capital Firms (VCFs) have been identified as important innovation financing companies supporting Research and Development (R & D) operations, from boosting elementary scientific research to technology advancement and commercialization. The main objective of this study is to investigate how VCFs groom Technology Based Firms (TBFs) to successful exit through equity and or board participation. This research is a face to face interview of 47 respondents which comprise of both TBFs and VCFs in Malaysia. Interview responses were transcribed and coded for the items that emerged were used for the theme development required for the discussions of findings. This research concludes that the involvement of venture capital firms on board of portfolio companies is vital for the performance of technology based firms in Malaysia.
Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems

The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonstrate that our approach is an effective tool for handling FRBSs’ accuracy-interpretability trade-off, i.e, designing FRBSs characterized by various levels of such a trade-off (in particular, for designing highly interpretability-oriented systems of still competitive accuracy).
Application of nine species-specific microsatellite loci to characterize three pike-perch ( Sander lucioperca ) populations from the Aral Sea basin in Uzbekistan

The pike-perch is one of the main commercial fish species in Uzbekistan. Historically, it inhabited the Aral Sea and deltas of inflowing rivers (Amu-Darya and Syr-Darya). At the beginning of the 1960s it was introduced into water bodies of the middle Amu-Darya and Syr-Darya from the Ural River. The present study was aimed to evaluate the current genetic status of wild pike-perch populations from different water bodies using nine recently isolated species-specific microsatellite markers. The three examined Uzbek pike-perch populations expressed significantly higher average numbers of alleles per locus (5.56 to 14.22) than three year-classes of a German wild population (3.11 to 3.78) included for comparison. On the other hand, average observed and expected heterozygosities were not Application of nine species-specific microsatellite loci to characterize three pike-perch (Sander lucioperca) populations from the Aral Sea basin in Uzbekistan Ernest Khurshut1, Klaus Kohlmann2 1 Institute of Zoology, Niyazov St. 1, 100095 Tashkent, Uzbekistan. E‐mail: ernest.khurshut@gmail.com 2 Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Muggelseedamm 310, 12587 Berlin, Germany. Corresponding author: E-mail: kohlmann@igb-berlin.de; Phone: +49-30-64 181 634; Fax: +49-30-64 181 799