Miracle-dz/UniFAR-FTUs huggingface dataset:
Disocclusion: a variational approach using level lines

Object recognition, robot vision, image and film restoration may require the ability to perform disocclusion. We call disocclusion the recovery of occluded areas in a digital image by interpolation from their vicinity. It is shown in this paper how disocclusion can be performed by means of the level-lines structure, which offers a reliable, complete and contrast-invariant representation of images. Level-lines based disocclusion yields a solution that may have strong discontinuities. The proposed method is compatible with Kanizsa's amodal completion theory.
Interactive horizon graphs: improving the compact visualization of multiple time series

Many approaches have been proposed for the visualization of multiple time series. Two prominent approaches are reduced line charts (RLC), which display small multiples for time series, and the more recent horizon graphs (HG). We propose to unify RLC and HG using a new technique - interactive horizon graphs (IHG) - which uses pan and zoom interaction to increase the number of time series that can be analysed in parallel. In a user study we compared RLC, HG, and IHG across several tasks and numbers of time series, focusing on datasets with both large scale and small scale variations. Our results show that IHG outperform the other two techniques in complex comparison and matching tasks where the number of charts is large. In the hardest task PHG have a significantly higher number of good answers (correctness) than HG (+14%) and RLC (+51%) and a lower error magnitude than HG (-64%) and RLC (-86%).
Glucocorticoid-Induced Osteoporosis – A Disorder of Mesenchymal Stromal Cells?

Glucocorticoids are a class of steroid hormones that are essential to life but cause serious harm in excess. The main clinical features of glucocorticoid excess are due to adverse effects on cells and tissues that arise from a common developmental precursor – the mesenchymal stromal cell (MSC; sometimes referred to as the mesenchymal stem cell). Interestingly glucocorticoids appear essential for the differentiation of cells and tissues that arise from MSCs. High levels of glucocorticoids are used in tissue engineering strategies to enhance the formation of tissues such as bone, cartilage, and muscle. This article discusses the paradox that glucocorticoids both enhance and impair MSC development and function. It will describe how endogenous glucocorticoids are likely to be important in these processes in vivo and will discuss the implications for therapies aimed at reducing the damage associated with the use of therapeutic glucocorticoids.
Performance of a hybrid EA-DE-memetic algorithm on CEC 2011 real world optimization problems

Evolutionary Algorithms (EAs), in their traditional form or in combination as in Memetic Algorithms (MAs), have been quite successful in solving a variety of optimization problems in the past. More recently, several excellent Differential Evolution (DE) based algorithms have been proposed which have had outstanding success in IEEE Congress on Evolutionary Computation (CEC) competition problems. Inspired by previous studies, we propose an algorithm combining the strengths of EA, DE and MA in this paper. The algorithm utilizes a population of random solutions to start with and generates a child population either through EA or DE based evolution with equal probability. Local search is then performed from one of the solutions in the population for further improvement objective value. To avoid stagnation, re-initialization of the population is performed whenever the local search is unable to improve the values consecutively for a prescribed number of generations. The performance of the proposed algorithm is presented in this paper for the newly introduced real world optimization problems for CEC 2011 competition.
Transverse Compressive Moduli and Yield Behavior of Some Orthotropic, High-Modulus Filaments

An account is given of the experimental determination of the transverse elastic moduli and yield behavior of some commercially available organic and graphite high-modulus filaments. The experimental technique involves transverse compression of single cylindrical filaments between two parallel flat platens and concurrent measurement of the platen- relative displacements and contact forces on the filaments. A theoretical discussion of the transverse compressional behavior of a linear elastic, homogeneous, orthotropic cylinder is presented. Using the theoretical results and the trans verse load-displacement measurements, a technique is developed for calculating the elastic moduli and maximum shear stresses at yield or fracture.
New Properties of Drosophila scs and scs’ Insulators

Insulators are defined as a class of regulatory elements that delimit independent transcriptional domains within eukaryotic genomes. The first insulators to be identified were scs and scs', which flank the domain including two heat shock 70 genes. Zw5 and BEAF bind to scs and scs', respectively, and are responsible for the interaction between these insulators. Using the regulatory regions of yellow and white reporter genes, we have found that the interaction between scs and scs' improves the enhancer-blocking activity of the weak scs' insulator. The sequences of scs and scs' insulators include the promoters of genes that are strongly active in S2 cells but not in the eyes, in which the enhancer-blocking activity of these insulators has been extensively examined. Only the promoter of the Cad87A gene located at the end of the scs insulator drives white expression in the eyes, and the white enhancer can slightly stimulate this promoter. The scs insulator contains polyadenylation signals that may be important for preventing transcription through the insulator. As shown previously, scs and scs' can insulate transcription of the white transgene from the enhancing effects of the surrounding genome, a phenomenon known as the chromosomal position effect (CPE). After analyzing many independent transgenic lines, we have concluded that transgenes carrying the scs insulator are rarely inserted into genomic regions that stimulate the white reporter expression in the eyes.
Knee Cartilage Thickness, T1ρ and T2 Relaxation Time Are Related to Articular Cartilage Loading in Healthy Adults

Cartilage is responsive to the loading imposed during cyclic routine activities. However, the local relation between cartilage in terms of thickness distribution and biochemical composition and the local contact pressure during walking has not been established. The objective of this study was to evaluate the relation between cartilage thickness, proteoglycan and collagen concentration in the knee joint and knee loading in terms of contact forces and pressure during walking. 3D gait analysis and MRI (3D-FSE, T1ρ relaxation time and T2 relaxation time sequence) of fifteen healthy subjects were acquired. Experimental gait data was processed using musculoskeletal modeling to calculate the contact forces, impulses and pressure distribution in the tibiofemoral joint. Correlates to local cartilage thickness and mean T1ρ and T2 relaxation times of the weight-bearing area of the femoral condyles were examined. Local thickness was significantly correlated with local pressure: medial thickness was correlated with medial condyle contact pressure and contact force, and lateral condyle thickness was correlated with lateral condyle contact pressure and contact force during stance. Furthermore, average T1ρ and T2 relaxation time correlated significantly with the peak contact forces and impulses. Increased T1ρ relaxation time correlated with increased shear loading, decreased T1ρ and T2 relaxation time correlated with increased compressive forces and pressures. Thicker cartilage was correlated with higher condylar loading during walking, suggesting that cartilage thickness is increased in those areas experiencing higher loading during a cyclic activity such as gait. Furthermore, the proteoglycan and collagen concentration and orientation derived from T1ρ and T2 relaxation measures were related to loading.
Identification of Direct Transcriptional Targets of the Kaposi's Sarcoma-Associated Herpesvirus Rta Lytic Switch Protein by Conditional Nuclear Localization

ABSTRACT Lytic reactivation from latency is critical for the pathogenesis of Kaposi's sarcoma-associated herpesvirus (KSHV). We previously demonstrated that the 691-amino-acid (aa) KSHV Rta transcriptional transactivator is necessary and sufficient to reactivate the virus from latency. Viral lytic cycle genes, including those expressing additional transactivators and putative oncogenes, are induced in a cascade fashion following Rta expression. In this study, we sought to define Rta's direct targets during reactivation by generating a conditionally nuclear variant of Rta. Wild-type Rta protein is constitutively localized to cell nuclei and contains two putative nuclear localization signals (NLSs). Only one NLS (NLS2; aa 516 to 530) was required for the nuclear localization of Rta, and it relocalized enhanced green fluorescent protein exclusively to cell nuclei. The results of analyses of Rta NLS mutants demonstrated that proper nuclear localization of Rta was required for transactivation and the stimulation of viral reactivation. RTA with NLS1 and NLS2 deleted was fused to the hormone-binding domain of the murine estrogen receptor to generate an Rta variant whose nuclear localization and ability to transactivate and induce reactivation were tightly controlled posttranslationally by the synthetic hormone tamoxifen. We used this strategy in KSHV-infected cells treated with protein synthesis inhibitors to identify direct transcriptional targets of Rta. Rta activated only eight KSHV genes in the absence of de novo protein synthesis. These direct transcriptional targets of Rta were transactivated to different levels and included the genes nut-1/PAN, ORF57/Mta, ORF56/Primase, K2/viral interleukin-6 (vIL-6), ORF37/SOX, K14/vOX, K9/vIRF1, and ORF52. Our data suggest that the induction of most of the KSHV lytic cycle genes requires additional protein expression after the expression of Rta.
Pilot-Aided Low-Complexity CFO and I/Q Imbalance Compensation for OFDM Systems

In direct-conversion orthogonal frequency division multiplexing receivers, the compensation of carrier frequency offset (CFO) and I/Q imbalance is critical. In the existing modified periodic pilot based scheme, I/Q imbalance compensation completely depends on CFO estimation, which however needs a computational expensive one-dimensional search and correct timing synchronization. In this paper, we propose a novel periodic pilot and an associated compensation method. Taking advantage of the pilot's periodicity, the proposed scheme simultaneously estimates the CFO and coefficients of I/Q imbalance compensation, in closed forms based on the linear least squares algorithm. The proposed scheme not only has significantly reduced complexity, but also is inherently robust to timing error. Simulations confirm the validity and superiority of the proposed scheme.
Modeling player experience in Super Mario Bros

This paper investigates the relationship between level design parameters of platform games, individual playing characteristics and player experience. The investigated design parameters relate to the placement and sizes of gaps in the level and the existence of direction changes; components of player experience include fun, frustration and challenge. A neural network model that maps between level design parameters, playing behavior characteristics and player reported emotions is trained using evolutionary preference learning and data from 480 platform game sessions. Results show that challenge and frustration can be predicted with a high accuracy (77.77% and 88.66% respectively) via a simple single-neuron model whereas model accuracy for fun (69.18%) suggests the use of more complex non-linear approximators for this emotion. The paper concludes with a discussion on how the obtained models can be utilized to automatically generate game levels which will enhance player experience.
Scalable Collaborative Bayesian Preference Learning

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, yet most prior work does not scale up to realistic data sizes. We shed light on why inference for such settings is computationally difficult for standard machine learning methods, most of which focus on predicting explicit ratings only. To simplify the difficulty, we present a novel expectation maximization algorithm, driven by expectation propagation approximate inference, which scales to very large datasets without requiring strong factorization assumptions. Our utility model uses both latent bilinear collaborative filtering and non-parametric Gaussian process (GP) regression. In experiments on large real-world datasets, our method gives substantially better results than either matrix factorization or GPs in isolation, and converges significantly faster.
Wet and dry model granulates under mechanical load - a confocal microscopy study

Granular matter, also known as bulk solids, consist of discrete particles with sizes between micrometers and meters. They are present in many industrial applications as well as daily life, like in food processing, pharmaceutics and cosmetics or in the oil and mining industry. When handling granular matter in industries or process engineering, the bulk solids are stored, mixed, conveyed or filtered. These techniques are based on observations in macroscopic experiments, i.e. rheological examinations of the bulk properties. Despite the amply investigations of bulk mechanics, the relation between single particle motion and macroscopic behavior is still not well understood and experimentally challenging. For exploring the microscopic properties on a single particle level, 3D imaging techniques are required. The objective of this work was the investigation of single particle motions in a bulk system in 3D under an external mechanical load, i.e. compression and shear. During the mechanical load applied with nanoindentation the structural and dynamical properties of these systems were examined with confocal microscopy. To satisfy the requirements for confocal microscopy, new granular model systems in the wet and dry state were designed and prepared. These new model systems required the development of new analysis algorithms and the customization of the existing analysis programs. As the particles are solid bodies, their motion is described by six degrees of freedom. To explore their entire motion with all degrees of freedom, a technique to visualize the rotation of spherical micrometer sized particles in 3D was developed. One of the foci during this dissertation was a model system for dry cohesive granular matter. In such systems the translation and rotation during a compression of the granular matter was investigated. In general the rotation of single particles was the more sensitive parameter compared to the translation. In regions with large structural changes the rotation had an earlier onset than the translation. In granular systems under shear, shear dilatation and shear zone formation were observed. Globally the granular sediments showed a shear behavior, which was known already from classical shear experiments, for example with Jenike cells. Locally a different shear behavior was observed. The shear zone formation was enhanced, when near the applied load a locally pre-diluted region existed. But even in regions with constant volume fraction a reorganization of particles occurred. A mixing between the different particle layers occurred, in particular an exchange of particles between the current flowing region and the non-flowing region was observed. Hence for understanding the flow properties of granular cohesive systems, they have to be described not only by bulk parameters, e.g. the volume fraction but also with single particle dynamics, such as translation and rotation. The second focus was on model systems for wet granular matter, where an additional
A basic study of activity type detection and energy expenditure estimation for children and youth in daily life using 3-axis accelerometer and 3-stage cascaded artificial neural network

It is important to prevent obesity in childhood given that many obese adults have been obese since childhood. An activity monitor could provide an effective aid in preventing obesity if it records not only the calorie assessment but also activity detection to check how active a child is in daily life. The current study is for activity monitoring algorithm and we designed 3-stage cascaded artificial neural network. To develop the algorithm, we recruited 76 participants, made 3-axis accelerometer for them, and acquired activity data and calorie consumption data through them. Finally, we designed 3-stage cascaded network to classify the activities and to assess energy consumption. The 3-stage network classifies 4 activities of walking, running, stairs moving, and jumping rope with overall accuracy of 94.70%, and predicts calorie consumption with average accuracy of 81.91%, which is better than the results of the 2-stage network. Future work would include the enhancement of the network performance.
Investigating the Viability of Bufferless NoCs in Modern Chip Multi-processor Systems

Chip Multi-Processors are quickly growing to dozens and potentially hundreds of cores, and as such the design of the interconnect for on chip resources has become an important field of study. Of the available topologies, tiled mesh networks are an appealing approach in tiled CMPs, as they are relatively simple and scale fairly well. The area has seen recent focus on optimizing network on chip routers for performance as well as power and area efficiency. One major cost of initial designs has been their power and area consumption, and recent research into bufferless routing has attempted to counter this by entirely removing the buffers in the routers, showing substantial decreases in NoC energy consumption. However, this research has shown that at high network loads, the energy benefits of bufferless schemes are vastly outweighed by performance degradation. When evaluated with pessimistic traffic patterns, the proposed router designs significantly increase network delays in last level cache traffic, and can lower the throughput of the system significantly. We evaluate these router designs as one component of the entire memory hierarchy design. They are evaluated alongside simple cache mapping mechanisms designed to reduce the need for cross-chip network traffic, as well as packet prioritization mechanisms proposed for high performance. We conclude, based on our evaluations, that with intelligent, locality-aware mapping of data to on-chip cache slices, bufferless network performance can get very close to buffered network performance. Locality-aware data mapping also significantly increases the network power advantage of bufferless routers over buffered ones.
Comparative Population Genomics of African Montane Forest Mammals Support Population Persistence across a Climatic Gradient and Quaternary Climatic Cycles

The Eastern Afromontane biodiversity hotspot (EABH) has the highest concentration of biodiversity in tropical Africa, yet few studies have investigated recent historical diversification processes in EABH lineages. Herein, we analyze restriction-site associated DNA-sequences (RAD-Seq) to study recent historical processes in co-distributed mouse (Hylomyscus) and shrew (Sylvisorex) species complexes, with an aim to better determine how historical paleoenvironmental processes might have contributed to the EABH’s high diversity. We analyzed complete SNP matrices of > 50,000 RAD loci to delineate populations, reconstruct the history of isolation and admixture, and discover geographic patterns of genetic partitioning. These analyses demonstrate that persistently unsuitable habitat may have isolated multiple populations distributed across montane habitat islands in the Itombwe Massif and Albertine Rift to the west as well as Mt Elgon and Kenyan Highlands to the east. We detected low genetic diversity in Kenyan Highland populations of both genera, consistent with smaller historical population sizes in this region. We additionally tested predictions that Albertine Rift populations are older and more persistently isolated compared to the Kenyan Highlands. Phylogenetic analyses support greater historical isolation among Albertine Rift populations of both shrews and mice compared to the Kenyan Highlands and suggest that there are genetically isolated populations from both focal genera in the Itombwe Massif, Democratic Republic of Congo. The Albertine Rift ecoregion has the highest mammalian tropical forest species richness per unit area on earth. Our results clearly support accelerating efforts to conserve this diversity.
Anton, a special-purpose machine for molecular dynamics simulation

The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond--about three orders of magnitude beyond the duration of the longest current MD simulations. In this paper, we describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods.
Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI]

A biomechanical model of the brain is presented, using a finite-element formulation. Emphasis is given to the modeling of the soft-tissue deformations induced by the growth of tumors and its application to the registration of anatomical atlases, with images from patients presenting such pathologies. First, an estimate of the anatomy prior to the tumor growth is obtained through a simulated biomechanical contraction of the tumor region. Then a normal-to-normal atlas registration to this estimated pre-tumor anatomy is applied. Finally, the deformation from the tumor-growth model is applied to the resultant registered atlas, producing an atlas that has been deformed to fully register to the patient images. The process of tumor growth is simulated in a nonlinear optimization framework, which is driven by anatomical features such as boundaries of brain structures. The deformation of the surrounding tissue is estimated using a nonlinear elastic model of soft tissue under the boundary conditions imposed by the skull, ventricles, and the falx and tentorium. A preliminary two-dimensional (2-D) implementation is presented in this paper, and tested on both simulated and patient data. One of the long-term goals of this work is to use anatomical brain atlases to estimate the locations of important brain structures in the brain and to use these estimates in pre-surgical and radiosurgical planning systems.