Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Video Stabilization Technique for Hand Held Mobile Videos

The videos taken from hand held mobile cameras suffer from different undesired slow motions like track, boom or pan. Hence it is desired to synthesis a new stabilized video sequence, by removing the undesired motion between the successive frames. Most of the previous methods assumes the camera motion model hence have limitation to process gorse motion. The efficiency of feature based methods depend on the ability of feature point selection and might cause temporal inconsistency in case of fast moving object in static scene. By taking consideration of slow motion limitation; the paper proposed a hybrid video stabilization technique using hierarchical differential global motion estimation and combination of gaussian kernel filtering is then added to eliminate accumulation error. The method is simple and computationally efficient, and is experimented on the large variety of videos taken in real time environment with different motions. It is found that proposed method not only effectively removes the undesired motion, but also minimizes the missing frame area.
The impact of attachment to substance in substance dependent individuals’ Psychotherapy relationships: An exploratory mixed-methods study

To date, there are no comprehensive studies that have examined the concept of attachment to substance in a substance dependent population, and how it relates to one’s attachment relationships with others. Specifically, it is unknown how an individual’s attachment to substance may influence the therapeutic relationship with their own therapist. The current research adopted a mixed-methods approach toward exploring these under-researched areas by way of collecting psychometric data with a sample of 450 substance dependent adults undergoing treatment in an anonymous survey study, and 10 individuals from that sample were interviewed in a follow-up study. Results showed that Insecure Attachment to Substance significantly influenced the relationship between one’s Attachment-Anxiety and Preoccupied-Merger attachment to therapist, providing support for the expected mediation effects. While no effect was found between Attachment-Avoidance and any style of attachment to therapist, Attachment-Avoidance was a significant predictor of Avoidant-Fearful attachment to therapist, and a significant negative predictor of Secure attachment to therapist. Men were found to have stronger associations between Attachment-Anxiety and Avoidant-Fearful attachment to therapist, when compared to women. In Study 2, all the clients interviewed experienced a Relational Dilemma, summarising their intra and inter-personal difficulties characterised by their ambivalence and insecurity, in dealing with others; and patterns of substance use. Relationally, these individuals described significant struggles leading to approach and avoidance behaviours, as a function of their personal insecurities, and these difficulties were described to manifest with their therapists. Overall, the findings supported the proposed model of Attachment to Substance, and the view that chronic and dependent substance use is an inadequate and often futile attempt to compensate for the failure to receive adequate love and care from relationships with others. Clinical implications were discussed.
Terrain analysis and locomotion control of a hexapod robot on uneven terrain

A terrain analysis method with a scanning laser range finder was proposed. For five typical kinds of terrain, including flat ground, vertical wall, slope, step and cuboid obstacle, the method can distinguish them successfully. Comparing with some methods used cameras, it's more simple and has less calculation. For the uneven terrain like slope, step, cuboid obstacle and so on, a leg joint adjustment unit was proposed. Based on the locomotion control method with a central pattern generator (CPG), the leg joint adjustment unit can adjust each foot position individually. It can also adjust the height of the center of robot's gravity independently to adapt different uneven terrain. Experiment have proved the rationality and validity of the methods above.
Performance VS quality of experience in a remote control application based on real-time 3D video feedback

This paper presents a preliminary study that relates the ability in performing remote control tasks with 3D video feedback and the users' quality of experience (QoE) when users are asked to judge the video quality only. The QoE has been evaluated by means of traditional subjective quality experiments, while an objective performance indicator (alignment error) has been used in the remote control task. Results show that, unexpectedly, even a relatively poor subjective video quality still provides sufficient 3D perception to achieve satisfactory alignment performance.
The microbiota of the mother at birth and its influence on the emerging infant oral microbiota from birth to 1 year of age: a cohort study

ABSTRACT Background: The acquisition of microbial communities and the influence of delivery mode on the oral microbiota of the newborn infant remains poorly characterised. Methods: A cohort of pregnant women were enrolled in the study (n = 84). All infants were born full term, by Spontaneous vaginal delivery (SVD) or by Caesarean section (CS). At delivery a saliva sample along with a vaginal/skin sample from the mother. Saliva samples were the taken from the infant within one week of birth, and at week 4, week 8, 6 months and 1 year of age. We used high-throughput sequencing of V4-V5 region 16S rRNA amplicons to compare the microbiota of all samples. Results: The vaginal microbiota had a lower alpha diversity than the skin microbiota of the mother, while the infant oral microbiota diversity remained relatively stable from birth to 8 weeks of age. The oral microbiota of the neonate differed by birth modality up to 1 week of age (p < 0.05), but birth modality did not have any influence on the infant oral microbiota beyond this age. Conclusions: We conclude thatbirth mode does not have an effect on the infant oral microbiota beyond 4 weeks of age, and the oral microbiota of infants continues to develop until 1 year of age.
Performance improvement in game playing using evolutionary computation by large search space exploration

Soft Computing branch of intelligence research is primarily focused on the path of achieving high performance by mimicking the human approach. The central idea is to capture and encode human knowledge in artificial learning form. Applying AI technology to develop efficient game-playing programs is through realization of search-intensive approach in very large and complex search intensive areas. Many researchers have developed very powerful search techniques over the past two decades and successfully applied these search algorithms to problems domains of optimization, machine learning and soft computing paradigms. This paper extends this approach, by developing a program which is almost completely reliant on search optimization through evolutionary computation. Very efficient evolutionary algorithms and advancement of “intelligent” search along with improved hardware resources like faster processors, larger memories, and larger disks makes it possible to push the limits to solve problem of type and size of Checkers which has search space as high as 5 × 1020 representing a daunting challenge. The collected checkers result pushes the boundary of evolutionary algorithms based problem domains.
Development of a Mechatronical Device to Measure Plantar Pressure for Medical Prevention of Gait Issues

A medically helpful mechatronical device for natural gait analysis is proposed. This consists of an instrumented shoe equipped with eight force sensors. The instantaneous plantar pressures are recorded by a personal digital assistant (PDA) belted on the patient hips. This article describes this original concept and the first results of its use
Petri Net Based Supervisory Control of a Social Robot with LTL Speciﬁcations

—We describe the implementation of a method to control a social robot based on discrete event system supervisory control theory. The sensors and actuators of the robot are modelled as Petri nets, and the target behaviour is given as a set of rules written as linear temporal logic (LTL) formulas. The Petri net models and LTL rules are then used to build a Petri net realization of a supervisor that is guaranteed by construction to restrict the robot’s behaviour such that the rules are fulﬁlled. This approach provides a close-to-natural-language description of the target behaviour, and can be the basis for a human-robot speech interaction device, where the rules for the robot to fulﬁl are described by the human in natural language.
At the next stop sign turn right: the metalloprotease Tolloid-related 1 controls defasciculation of motor axons in Drosophila

Navigation of motoneuronal growth cones toward the somatic musculature in Drosophila serves as a model system to unravel the molecular mechanisms of axon guidance and target selection. In a large-scale mutagenesis screen, we identified piranha, a motor axon guidance mutant that shows strong defects in the neuromuscular connectivity pattern. In piranha mutant embryos, permanent defasciculation errors occur at specific choice points in all motor pathways. Positional cloning of piranha revealed point mutations in tolloid-related 1 (tlr1), an evolutionarily conserved gene encoding a secreted metalloprotease. Ectopic expression of Tlr1 in several tissues of piranha mutants, including hemocytes, completely restores the wild-type innervation pattern, indicating that Tlr1 functions cell non-autonomously. We further show that loss-of-function mutants of related metalloproteases do not have motor axon guidance defects and that the respective proteins cannot functionally replace Tlr1. tlr1, however, interacts with sidestep, a muscle-derived attractant. Double mutant larvae of tlr1 and sidestep show an additive phenotype and lack almost all neuromuscular junctions on ventral muscles, suggesting that Tlr1 functions together with Sidestep in the defasciculation process.
Spectra of random stochastic matrices and relaxation in complex systems

We compute spectra of large stochastic matrices W, defined on sparse random graphs in the configuration model class, i.e. on graphs that are maximally random subject to a given degree distribution. Edges of the graph are given positive random weights Wij > 0 in such a fashion that column sums are normalized to one. We compute spectra of such matrices both in the thermodynamic limit, and for single large instances. Our results apply to arbitrary graphs in the configuration model class, as long as the mean vertex degree remains finite in the thermodynamic limit. Edge weights Wij can be largely arbitrary but we require the Wij to satisfy a detailed balance condition, or in other words that the Markov chains described by them are reversible. Knowing the spectra of stochastic matrices is tantamount to knowing the complete spectrum of relaxation times of stochastic processes described by them, so our results should have many interesting applications for the description of relaxation in complex systems. Contributions to the spectral density related to extended states can be disentangled from those related localized states allowing time scales associated with transport processes and those associated with the dynamics of local rearrangements to be differentiated.
A ug 2 00 0 Glueball Masses in Relativistic Potential Model

The problem of glueball mass spectra using the relativistic Dirac equation is studied. Also the Breit-Fermi approach used to obtaining hyperfine splitting in glueballs. Our approach is based on the assumption, that the nature and the forces between two gluons are the short-range. We were to calculate the glueball masses with used screened potential.
A holistic approach for semantic-based game generation

The Web contains vast sources of content that could be reused to reduce the development time and effort to create games. However, most Web content is unstructured and lacks meaning for machines to be able to process and infer new knowledge. The Web of Data is a term used to describe a trend for publishing and interlinking previously disconnected datasets on the Web in order to make them more valuable and useful as a whole. In this paper, we describe an innovative approach that exploits Semantic Web technologies to automatically generate games by reusing Web content. Existing work on automatic game content generation through algorithmic means focuses primarily on a set of parameters within constrained game design spaces such as terrains or game levels, but does not harness the potential of already existing content on the Web for game generation. We instead propose a holistic and more generally-applicable game generation solution that would identify suitable Web information sources and enrich game content with semantic meta-structures.
Performance of Multimedia Traffic in Multi Hop Wireless Sensor Network

Quality of Service (QoS) is a requirement for proper functioning of traditional and modern networks. Wireless Sensor Networks (WSN) is adversely affected because of mobility, harsh communication medium, and environment behaviours. Quality of Service is a crucial aspect in WSN design information sent by nodes and delivered according to QoS requirements to provide outside observers accurate vision of monitored phenomena. In WSNs for effective transmission of still images, audio, and video information enforces rigorous necessities on the energy consumption and throughput. In this paper, it is proposed to investigate the performance of routing traffic for multimedia traffic in WSN for multi hop nodes from the sink. Keywords-Wireless S e n s o r N e t w o r k , p a c k e t scheduling, Dynamic Source Routing, Multimedia traffic, one hop nodes.
A compact hamiltonian with the same asymptotic mean spectral density as the Riemann zeros

For the classical hamiltonian (x + 1/x)(p + 1/p), with position x and conjugate momentum p, all orbits are bounded. After a symmetrization, the corresponding quantum integral equation possesses a family of self-adjoint extensions: compact operators on the entire positive x axis, labelled by an angle α specifying the boundary condition at the origin, with a discrete spectrum of real energies E. On the cylinder {−∞ < E < ∞, 0 ≤ α < 2π}, there is a single eigencurve in the form of a helix winding clockwise. The rise between successive windings gets sharper as the scaled Planck's constant decreases. This behaviour can be understood semiclassically. The first two terms of the asymptotic eigenvalue density are the same as those for the density of heights of the Riemann zeros.
Efficient algorithms for Thai tweet summarization

Nowadays, Twitter is one of the most popular microblogging services. A user may follow many people who may post a 140 character status (tweet) often. Thus, if the user does not continuously read tweets, users may find an excessive number of unread tweets. Such incident causes a burden on the user to find the relevant tweet. It is one of the reasons why Twitter can lead users to feel overloaded with information. This article implemented and evaluated six automatic summarization algorithms for finding similar Thai tweets. The experimental results showed that TextRank algorithm performed the best because this algorithm selected the tweets with the highest scores. On the other hand, Hybrid TF-IDF algorithm could detect similar tweets the least because this algorithm calculated the score by taking the sum frequency of words in a tweet instead of considering the similarity in the level of sentences.
Human Skeleton Reconstruction for Optical Motion Capture

In this paper, we propose a method of automatically estimating the pose of the underlying human skeleton to available in optical motion capture. These problems are addressed in the method: identifying markers over individual frames, predicting the missing marker locations and estimating the joint positions. Firstly, we identify markers according to local rigidity and space-time constraints of markers on the same limb segment in each frame. After that, based on small inter-frame motion, we predict the missing marker locations using the adjacent visible marker locations. Finally, we estimate the joint locations in two different scenarios, using a nonlinear optimization in the circumstance of less than three markers on one given limb segment. Results show that the approach is able to estimate the joint locations correctly, and our method is scalable in marker prediction and human pose estimation.
Curvelet-based seismic data processing : A multiscale and nonlinear approach

Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that determine image quality. Curvelets, little “plane waves,” complete with oscillations in one direction and smoothness in the other directions, sparsify a property we leverage explicitly with sparsity promotion. With this principle, we recover seismic data with high fidelity from a small subset (20%) of randomly selected traces. Similarly, sparsity leads to a natural decorrelation and hence to a robust curvelet-domain primary-multiple separation for North Sea data. Finally, sparsity helps to recover migration amplitudes from noisy data. With these examples, we show that exploiting the curvelet's ability to sparsify wavefrontlike features is powerful, and our results are a clear indication of the broad applicability of this transform to exploration seismology.
Welcome Bank Consolidation and Consumer Loan Interest Rates

The recent wave of bank mergers has raised concern with its effect on competition. This paper examines the influence of concentration and merger activity on consumer loan interest rates. It uses Bank Rate Monitor, Inc. survey data on loan rates quoted weekly by large commercial banks in ten major U.S. cities during the 1989 to 1997 period. The pricing behavior of banks is analyzed for two types of loans: new automobile loans and unsecured personal loans. Market concentration is found to have a positive and significant impact on the level of personal loans, but not automobile loans. Consistent with the exercise of market power, we find that personal loan rates rise in markets following a significant merger. However, there is a significant decrease in automobile loan rates charged by banks participating in within-market mergers, a finding consistent with economies of scale in the origination of automobile loans. The paper also tests for the existence of leader-follower relationships in loan pricing and finds that it is more widespread in markets for automobile loans. Interest rates on both types of loans respond asymmetrically to a change in equivalent maturity Treasury security rates, being more sensitive to a rise than a fall. In addition, personal loan rates are less responsive in more concentrated markets.
Interferon-Induced Ifit2/ISG54 Protects Mice from Lethal VSV Neuropathogenesis

Interferon protects mice from vesicular stomatitis virus (VSV) infection and pathogenesis; however, it is not known which of the numerous interferon-stimulated genes (ISG) mediate the antiviral effect. A prominent family of ISGs is the interferon-induced with tetratricopeptide repeats (Ifit) genes comprising three members in mice, Ifit1/ISG56, Ifit2/ISG54 and Ifit3/ISG49. Intranasal infection with a low dose of VSV is not lethal to wild-type mice and all three Ifit genes are induced in the central nervous system of the infected mice. We tested their potential contributions to the observed protection of wild-type mice from VSV pathogenesis, by taking advantage of the newly generated knockout mice lacking either Ifit2 or Ifit1. We observed that in Ifit2 knockout (Ifit2 −/−) mice, intranasal VSV infection was uniformly lethal and death was preceded by neurological signs, such as ataxia and hind limb paralysis. In contrast, wild-type and Ifit1 −/− mice were highly protected and survived without developing such disease. However, when VSV was injected intracranially, virus replication and survival were not significantly different between wild-type and Ifit2−/− mice. When administered intranasally, VSV entered the central nervous system through the olfactory bulbs, where it replicated equivalently in wild-type and Ifit2 −/− mice and induced interferon-β. However, as the infection spread to other regions of the brain, VSV titers rose several hundred folds higher in Ifit2 −/− mice as compared to wild-type mice. This was not caused by a broadened cell tropism in the brains of Ifit2 −/− mice, where VSV still replicated selectively in neurons. Surprisingly, this advantage for VSV replication in the brains of Ifit2−/− mice was not observed in other organs, such as lung and liver. Pathogenesis by another neurotropic RNA virus, encephalomyocarditis virus, was not enhanced in the brains of Ifit2 −/− mice. Our study provides a clear demonstration of tissue-, virus- and ISG-specific antiviral action of interferon.
Approximate inversion algorithm of the elliptical Radon transform

We address the fundamental question of image reconstruction in bistatic regime in which the measurements represent line integrals over a family of ellipses with foci at the source and receiver locations. An integral transform, the elliptical Radon transform is introduced and used to model the data. This paper presents some new numerical results about the inversion of the elliptical Radon in 2D. A new approximate inversion formula is presented in the case of circular acquisition geometry when the source and the receiver are rotating around the origin at a fixed distance from each other. We demonstrate the efficiency of the suggested algorithm by presenting a computational implementation of the method on a numerical phantom. This novel algorithm can be efficiently implemented as a numerical method in several bistatic imaging modalities e.g. in biomedical imaging.
Characterizing the benefits of model-based vs . direct-control learning in exploration

We consider the problem of learning a good control function f r m reinforcement. Although much studied, the issue of how (or even whether) to u se prior knowledge to scale reinforcement learning up to real-world problems remains a important open issue. We investigate the inherent data-complexity of reinforcem ent learning when prior knowledge is expressed either as a set of possible reward models (m odel-based learning) or as a set of possible controllers (direct-control learning). Ou r key observation is that the distinction between these two approaches arises solely from the par tial evaluation nature of the task (i.e. that each training instance evaluates only a subs et of the controllers), and does not arise from temporal credit assignment or exploration/e xploitation concerns per se. This allows us to compare these two learning approaches in a very s imple batch setting. Our results show that it is not always beneficial to pursue a pu rely model-based approach, even though this can yield significant benefits at tim es. We show that in our simple setting there is a hybrid learning strategy which combines t h benefits of both model-based and direct-control learning, and dominates the performanc e of both. This work contributes preliminary steps towards understanding how prior domain k nowledge can be properly exploited to improve the data-efficiency of (a simple version o f) reinforcement learning, and reveals the subtleties and difficulties that must be faced in general.