Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Inverse Covariance Estimation with Hierarchical Matrices

In statistics, a frequent task is to estimate the covariance matrix of a set of normally distributed random variables from given samples. In a high-dimensional regime, this problem becomes particularly challenging as the number of samples is typically much smaller than the number of variables. If the inverse covariance matrix happens to be sparse, an `1-based sparsity constraint often leads to a successful identification of the underlying correlations. The resulting convex optimization problem can then efficiently be solved by Newton-like techniques with super-linear or even quadratic convergence rates. A drawback of the associated quadratic model is the required computation of (dense) inverses of sparse matrices. We address this problem by the usage of hierarchical matrices which allow for an (approximate) data-sparse representation of large dense matrices. This explicit representation enables us to further exploit the simultaneous treatment of groups of variables in a block-wise manner and to easily ensure positive definiteness of each iterate. Numerical examples indicate an at most quadratic scaling in time in the number of variables under moderate storage consumptions even for high-dimensional problems.
FDI entry mode choice and ownership structure in Turkish market: A firm-level analysis

Abstract The choice of a foreign firm’s entry mode into a host country is a strategic decision which impacts its future survival and success in other countries. By employing multinomial logit regression, this study aims to investigate the impact of the firm’s level of financial data on the entry mode decisions of investors and their ownership structure in Turkey from 2005 to 2012. The empirical findings have revealed that larger firms with high rate of profitability ratios are more likely to choose the full-ownership mode over others. On the other hand, a higher rate of return on equity increases the probability of investors choosing the shared-ownership mode.
Estimating syntagmatic association strEngth Using DistribUtional WorD rEprEsEntations

In the paper we present distributed vector space models based on word embeddings and a specific association-oriented count-based distributional algorithm which have been applied to measuring association strength in Russian syntagmatic relations (namely, between nouns and adjectives). We discuss the compositional properties of the vectors representing nouns, adjectives and adjective-noun compositions and propose two methods of detecting the syntactic association possibility. The accuracy of the proposed measures is evaluated by means of a pseudo-disambiguation test procedure and all models show considerably high results. The errors are manually annotated, and the model errors are classified in terms of their linguistic nature and compositionality features.
Application of Interactive Fuzzy Data Mining to the Analysis of Inter-Vehicle Communication in Traffic Simulations

Recently, Inter-Vehicle Communication (IVC) has actively been studied to avoid traffic congestion. Vehicles with IVC can obtain the latest traffic information on the congestion through local communication among neighboring vehicles. In this paper, we examine the effectiveness of IVC through computer simulations. Each vehicle chooses a link (i.e., route) according to the traffic information. The traffic information for each link is represented by a link weight. Each link weight corresponds to the travel time that a vehicle can pass the link. We examine when and where the traffic information is useful for link selections. Thus, we collect travel records (e.g., predicted and actual travel times, travel path, traffic volumes around the path) of vehicles with IVC from traffic simulations. Since we can obtain a huge amount of data, we analyze the data by fuzzy data mining. It can linguistically represent the data by fuzzy rules. We use multiobjective genetic fuzzy rule selection to obtain a set of fuzzy rules with high accuracy and interpretability. For the interpretability, we incorporate user’s preference into our genetic rule selection method. User’s preference is represented by satisfaction levels. The satisfaction levels are interactively modified by its user in our interactive fuzzy data mining.
Occurrence and diversity of the tetracycline resistance gene tet ( M ) in enteric bacteria of Antarctic Adélie penguins

that the presence of histidines at codons 122 and/or 126 in HBV subgenotype A2 could restore the susceptibility to adefovir in the presence of R217. There are important implications derived from these findings. First, HBV subgenotype A2 is the most frequent genotype A variant in Europe in contrast with most Asian and African regions. In this regard, response to adefovir could be lower in European chronic hepatitis B patients. Second, introduction of HBV genotyping seems to be warranted before prescription of adefovir in treatment-naive subjects.
Dietary Probiotic Bacillus licheniformis TC 22 Increases Growth , Immunity , and Disease Resistance , against Vibrio splendidus Infection in Juvenile Sea Cucumbers Apostichopus japonicus

In this study we examined the effects of probiotic Bacillus licheniformis TC22 on growth, immunity, and disease resistance against Vibrio splendidus in juvenile sea cucumbers Apostichopus japonicus. For 30 days, sea cucumbers were fed diets with TC22 at 0 (control), 10, 10, and 10 CFU/g respectively. Results showed that dietary TC22 at 10 CFU/g significantly improved (P<0.05) growth of sea cucumbers. Dietary TC22 had no significant effects on total coelomocytes count, acid phosphatase activity, and superoxide dismutase activity in these animals (P>0.05). Dietary TC22 at 10 CFU/g significantly improved phagocytosis, and total nitric oxide synthase activity in sea cucumbers (P<0.05). Compared to the control, dietary TC22 had no significant effects on respiratory burst activity (P>0.05). Respiratory burst in sea cucumbers fed dietary TC22 at 10 CFU/g was significantly higher than those fed dietary TC22 at 10 CFU/g (P<0.05). Cumulative mortality after V. splendidus challenge decreased significantly in the sea cucumbers fed with TC22 at 10 CFU/g (P<0.05). The present study confirmed dietary B. licheniformis TC22 at 10 CFU/g could significantly improve immunity and disease resistance in juvenile A. japonicus. The IJA appears exclusively as a peer-reviewed on-line open-access journal at http://www.siamb. org.il. To read papers free of charge, please register online at registration form. Sale of IJA papers is strictly forbidden.
Improving the usability of online usability surveys with an interactive Stripe scale

The paper introduces Stripe, an interactive continuous scale for online surveys that makes it easy to compare multiple answers on a single screen. The Stripe is evaluated as an alternative to the n-point Likert scale, which is commonly used in online usability questionnaires like the System Usability Scale (SUS). The paper presents the results of a user study, which confirmed the validity of results gained with the proposed Stripe interface by applying both the Stripe and the Likert interface to an online SUS questionnaire. Additionally, the results of our study show that the participants favor the Stripe interface in terms of intuitiveness and ease of use, and even perceive the Stripe interface as less time consuming than the standard Likert scaled interface based on radio buttons.
Spillover Effects in Local Labor Markets: Evidence from Mass Layoffs

Governments are often willing to subsidize firms on the verge of bankruptcy. The main economic rationale behind these interventions is that a plant closure would not only harm the workers employed in that plant, but create a domino effect on the regional economy as a whole. Yet, little is still known empirically how important these spillover effects are for economic development in the region. In this paper, we use administrative data of all workers and firms in Germany to quantify the spillover effects of mass layoffs. For the empirical analysis, we combine a difference-in-differences estimator with an event-study approach. We find sizeable negative spillover effects on the regional economy: regions, and especially firms producing in the same sector as the firm hit by a mass layoff, lose many more jobs than in the initial layoff. In contrast, we find few negative effects for workers employed (but not directly affected by the layoff) in the region. As such, mass layoffs seem to have negative spillovers locally, but not nationally.
Adjustment with a Fixed Exchange Rate: Cameroon, Cote d'Ivoire, and Senegal

As members of the CFA Franc Zone, Cameroom, Cote d'Ivoire, and Senegal cannot use the nominal exchange rate as a tool of macroeconomic adjustment. This article considers these countries' responses to the commodity and oil price shocks of the 1970s in light of this and other institutional constraints. Using a two sector model, it shows that there exist instruments that, in principle, permit the real exchange rate depreciation necessary for adjusting to macroeconomic imbalances. The authors interpret the very different adjustment experiences of the three countries (despite their common economic structure and institutional setting) in terms of different uses of these instruments. Alternative assumptions about the labour market leave the qualitative nature of the results unaltered. Statistical analysis of data from the three countries confirms the model's linking of the current account and real exchange rate with the instruments of adjustment.
Supremum of Perelman's entropy and Kähler-Ricci flow on a Fano manifold

In this paper, we extend the method in [TZhu5] to study the energy level $L(\cdot)$ of Perelman's entropy $\lambda(\cdot)$ for K\"ahler-Ricci flow on a Fano manifold. Consequently, we first compute the supremum of $\lambda(\cdot)$ in K\"ahler class $2\pi c_1(M)$ under an assumption that the modified Mabuchi's K-energy $\mu(\cdot)$ defined in [TZhu2] is bounded from below. Secondly, we give an alternative proof to the main theorem about the convergence of K\"ahler-Ricci flow in [TZhu3].
Children Age and Gender Classification Based on Speech Using ConvNets

In this paper, we present a study about building age and gender automatic classifiers for children at their first school years (between 6 and 11 years old). We created a speech corpus with 174 children interacting with a couple of robots in a Wizard of Oz scenario. The recorded speech was manually segmented and then characterized with low-level acoustic features. Next, we trained the classification models using a convolutional neural network architecture. Due to the complexity in the tuning process for the correct selection of the parameters used for this type of neural network, we integrated the use of a mathematical object called covering arrays to generate the set of optimal parameters for neural network architecture. Given the complexity of the classification of children speech, we obtained encouraging results. Our results indicate that it is difficult to achieve an accurate classification of children with very close ages. By grouping the subjects into two or three ages, the results improved significantly. On the other hand, the task of gender identification was less challenging, and we obtained higher classification performance measures.
Ride-through of PMSG Wind Power System Under the Distorted and Unbalanced Grid Voltage Dips

This paper presents a ride-through skill of PMSG wind turbine system under the distorted and unbalanced grid voltage dips. When voltage dips occur in the grid, pitch control and generator speed control as well as a parallel resistor of DC-link help to keep the turbine’s safety. Modern grid code requires a wind turbine to supply reactive currents to help voltage recovery after grid faults clearance. In order to supply reactive currents to the grid in case of the distortedly unbalanced grid voltage dips, a special PLL is needed to control the grid side converter and to regulate the grid voltages symmetrically. The proposed method is applied to 2MW multi-pole PMSG wind turbine system, and verified by simulation.
The Effects of Releasing Greenbelt Restrictions on Land Development in the Case of Medium-Sized Cities in Korea

Many metropolitan areas around the world aim to control urban growth with a view to achieving efficiency and containing urban problems. Among many urban growth policy tools, the green belt (GB) policy is known as the most rigid and strongest. However, there has been no study on the consequences when GB restrictions are completely removed. The primary purpose of this study is to analyse the spatial effects of greenbelt removal on land development in Korea’s medium-sized cities between 2000 and 2017. To do so, we used the Landsat thematic mapper (TM) 5 satellite image (2000) and Landsat OLI TIRS 8 satellite image (2017) along with various attribute data to model the spatial effects of greenbelt removal in the cases of three medium-sized cities in Korea. The result of difference-in-difference (DID) analysis confirms that the effects of GB removal on land development vary depending on the local conditions of land development.
CORN: In-Buffer Computing for Binary Neural Network

Binary Neural Networks (BNNs) have obtained great attention since they reduce memory usage and power consumption as well as achieve a satisfying recognition accuracy on Image Classification. In particular to the computation of BNNs, the multiply-accumulate operations of convolution-layer are replaced with the bit-wise operations (XNOR and pop-count). Such bit-wise operations are well suited for the hardware accelerator such as in-memory computing (IMC). However, an additional digital processing unit (DPU) is required for the pop-count operation, which induces considerable data movement between the Process Engines (PEs) and data buffers reducing the efficiency of the IMC. In this paper, we present a BNN computing accelerator, namely CORN, which consists of a Spin-Orbit-Torque Magnetic RAM (SOT-MRAM) based data buffer to perform the majority operation (to replace the pop-count process) with the SOT-MRAM-based IMC to accelerate the computing of BNNs. CORN can naturally implement the XNOR operation in the NVM memory array, and feed results to the computing data buffer for the majority write operation. Such a design removes the pop-counter implemented by the DPU and reduces data movement between the data buffer and the memory array. Based on the evaluation results, CORN achieves 61% and 14% power saving with 1.74× and 2.12× speedup, compared to the FPGA and DPU based IMC architecture, respectively.
VARIATIONS IN NONCOMMUTATIVE POTENTIAL THEORY: FINITE-ENERGY STATES, POTENTIALS AND MULTIPLIERS

In this work we undertake an extension of various aspects of the potential theory of Dirichlet forms to noncommutative C ∗ -algebras with trace. In particular we introduce ﬁnite-energy states, potentials and multipliers of Dirichlet spaces. We prove several results among which are the celebrated Deny’s embedding theorem, Deny’s inequality, the fact that the carr´e du champ of bounded potentials are ﬁnite-energy functionals and the fact that bounded eigenvectors are multipliers. Deny’s embedding theorem and Deny’s inequality are also crucial to prove that the algebra of ﬁnite-energy multipliers is a form core and that it is dense in A provided the resolvent has the Feller property. Examples include Dirichlet spaces on group C ∗ -algebras associated to negative deﬁnite functions, Dirichlet forms arising in free probability, Dirichlet forms on algebras associated to aperiodic tilings, Dirichlet forms of Markovian semigroups on locally compact spaces, in particular on post critically ﬁnite self- similar fractals, and Bochner and Hodge-de Rham Laplacians on Riemannian manifolds.
How much of a problem is problem gambling

Problem gambling is conventionally defined by the score in a specific questionnaire exceeding some critical value and data suggests is that 0.7% of adults in the UK could be afflicted. However, the literature has not evaluated the size of the harm associated with such an affliction and this research evaluates the effect of problem gambling on self-reported well-being which, together with a corresponding effect of income on well-being, allows us to construct a money-metric of the (self) harm associated with being a problem gambler. Our estimates suggest that problem gambling imposes a very large reduction in individual well-being.
A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation

In illuminant estimation, we attempt to estimate the RGB of the light. We then use this estimate on an image to correct for the light's colour bias. Illuminant estimation is an essential component of all camera reproduction pipelines. How well an illuminant estimation algorithm works is determined by how well it predicts the ground truth illuminant colour. Typically, the ground truth is the RGB of a white surface placed in a scene. Over a large set of images an estimation error is calculated and different algorithms are then ranked according to their average estimation performance. Perhaps the most widely used publically available dataset used in illuminant estimation is Gehler's Colour Checker set that was reprocessed by Shi and Funt. This image set comprises 568 images of typical everyday scenes. Curiously, we have found three different ground truths for the Shi-Funt Colour Checker image set. In this paper, we investigate whether adopting one ground truth over another results in different rankings of illuminant estimation algorithms. We find that, depending on the ground truth used, the ranking of different algorithms can change, and sometimes dramatically. Indeed, it is entirely possible that much of the recent 'advances' made in illuminant estimation were achieved because authors have switched to using a ground truth where better estimation performance is possible.
SYNTHESIS AND CHARACTERIZATION OF PIEZO-MAGNETO ( PVDF-FE 3 O 4 ) COMPOSITES

Synthesis and Characterization of Piezo-Magneto (PVDF Fe3O4) Composites. (May 2011) Oliver Kasongo Mulamba, B.S., West Texas A&M Chair of Advisory Committee: Dr. Hong Liang This research entails the synthesis and characterization of a novel class of materials which incorporate both magnetic and piezoelectric characteristics. The composite is made up of the piezoelectric polymer PVDF and magnetic nanoparticles. The testing samples are produced using a spin casting process. The characterizations of the samples were performed using X-ray diffraction, Atomic force microscopy, linear staging, Dynamic mechanical analysis, Differential scanning calorimetry, and Fourier transform Infra-Red. X-ray diffraction and Atomic force microscopy showed that the presence of the Fe3O4 particles have no effect on the crystallinity of the polymer matrix, therefore allowing for the incorporation of inclusions without directly affecting the piezoelectric property. Changes in the thermal characteristics of the polymer matrix, observed using Differential scanning calorimetry, indicated increases in the thermal conductivity of the composite. Decreases in the heat of melting and crystallization were also observed and further solidified the conclusion that the presence of the Fe3O4 nanoparticles changes the thermal behavior of the polymer. It was observed from the DMA results that the presence of the Fe3O4 nanoparticles caused
Hydrodynamical wind on a magnetized ADAF with thermal conduction

We examine the effects of a hydrodynamical wind on advection-dominated accretion flows (ADAFs) with thermal conduction in the presence of a toroidal magnetic field under a selfsimilar treatment. The disc gas is assumed to be isothermal. For a steady state structure of such accretion flows a set of self-similar solutions are presented. The mass-accretion rate u M decreases with radiusr as u M ∝ r s+1/2 , wheres is an arbitrary constant. We show that existence of wind will lead to an increase of the accretion velocity. Cooling effects of outflows or winds are noticeable and should be taken into account for calculating luminosity and effective temperature of optically thin and thick ADAFs. Increasing the effect of wind decreases the disc’s temperature because of energy flux which is taken away by winds. We will see that for a given set of input parameters, the solution reaches a non-rotating limit at a specific value of φs. The values of this limitation on φs will increase by adding s, the wind parameter. In fact, the higher values of s will allow the disc to have a physical rotating solution for larger φs.
Numerical Modeling of Solitary Wave Interaction with a Submerged Perforated Barrier

In this paper, we investigate the interaction between an isolated solitary wave and an underwater perforated barrier numerically. With the advantages on enhancing water circulations and sediment movements, the perforated barrier is considerably more cost effective and less environmental impact than the conventional hard structures. The numerical results are calculated by a two-dimensional volume of fluid (VOF)-type model which solves the Reynolds-Averaged Navier-Stokes (RANS) equations and the nonlinear k-e turbulence closure model. The main attentions would be paid on tracing the trajectories of fluid particles around the object to help understand possible sediment movements for suspended loads and on evaluating the wave reflection, transmission, and dissipation coefficients using the energy integral method.
Geometric Scene Labeling for Long-Range Obstacle Detection

Autonomous Driving or self driving vehicles are concepts of vehicles knowing their environment and making driving manoeuvres without instructions from a driver. The concepts have been around for decades but has improved significantly in the last years since research in this area has made significant progress. Benefits of autonomous driving include the possibility to decrease the number of accidents in traffic and thereby saving lives.A major challenge in autonomous driving is to acquire 3D information and relations between all objects in surrounding traffic. This is referred to as \textit{spatial perception}. Stereo camera systems have become a central sensor module for advanced driver assistance systems and autonomous driving. For object detection and measurements at large distances stereo vision encounter difficulties. This includes objects being small, having low contrast and the presence of image noise. Having an accurate perception of the environment at large distances is however of high interest for many applications, especially autonomous driving.This thesis proposes a method which tries to increase the range to where generic objects are first detected using a given stereo camera setup. Objects are represented by planes in 3D space. The input image is segmented into the various objects and the 3D plane parameters are estimated jointly. The 3D plane parameters are estimated directly from the stereo image pairs. In particular, this thesis investigates methods to introduce geometric constraints to the segmentation or labeling task, i.e assigning each considered pixel in the image to a plane.The methods provided in this thesis show that despite the difficulties at large distances it is possible to exploit planar primitives in 3D space for obstacle detection at distances where other methods fail.