Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint coding of multiview video and depth data using virtual view synthesis

To compress multiview video and depth information, we synthesize a virtual image for the current view using color and depth data of neighboring views. In this article, we then use a view interpolation prediction scheme at the virtual image to improve the inter‐view prediction. We also propose a solution for overlapping regions and empty holes that are generated during the intermediate view synthesis process due to occlusion and disocclusion situations. Experimental results show that the proposed methods achieve approximately 0.65 dB of Peak Signal‐to‐Noise Ratio (PSNR) gain on average for multiview depth data and 0.17 dB of PSNR gain for multiview video coding, compared with the reference software, Joint Multiview Video Model 1.0. We also show that our method is even more powerful for smaller search ranges. Furthermore, we examine the effects of inter‐view prediction on hierarchical B‐pictures. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 370–377, 2010
An Inertially-Actuated Passive Dynamic Step Climbing Wheeled Robot

For their inherent stability and simplicity, wheeled robots are very common in robotics applications — but a major drawback of wheeled robots is their inability to navigate over large obstacles or steps without assistance. Active systems that have been designed for use on wheeled robots to lift the robot over a step — such as USU’s T3 and Virginia Tech’s IMPASS — are effective, but are limited due to the size, cost, and power required for the additional actuators. A novel, inertially actuated, passive dynamic system, excited by the motion of the robot, is introduced to allow a wheeled robot to “pop a wheelie” on each axle and hop over a step. The system investigated here is a sliding mass-spring that shifts forward and backward based on the acceleration of the base robot. By coordinating the acceleration and deceleration of the robot, the front wheels can lift over a step and the rear wheels can be pulled up afterward — both actions being a product of inertial actuation. Key advantages of this system are that the design is simple, cost-effective, and can be adjusted and retrofit to a different wheeled robot in the future with little effort. This paper presents the development of a novel inertially actuated, passive dynamic step climbing wheeled robot. Derivations of the dynamic model of the inertially actuated system are given and a computer simulation and experiments of an implementation of this sliding mass system are presented, followed by conclusions with possibilities for future work. © 2009 ASME
A Minimax Algorithm Faster than Alpha - Beta

We present two recursive state space search algorithms that are based on SSS* and Dual*. Both dominate Alpha-Beta on a node count basis and one of them is always faster in searching random trees. These results have been derived on arti cial game trees with negligible leaf node evaluation time. For practical applications with more complex leaf evaluation functions we conjecture that our recursive state space search algorithms perform even better and might eventually supersede the popular directional
t Brain-Derived Neurotrophic Factor Overexpression Induces Precocious Critical Period in Mouse Visual Cortex

Brain-derived neurotrophic factor (BDNF) is a candidate molecule for regulating activity-dependent synaptic plasticity on the grounds of its expression pattern in developing visual cortex and that of its receptor, trkB (Castr¿n et al., 1992; Bozzi et al., 1995; Schoups et al., 1995; Cabelli et al., 1996), as well as the modulation of these patterns by activity (Castr¿n et al., 1992; Bozzi et al., 1995; Schoups et al., 1995). Infusing trkB ligands or their neutralizing agents, the trkB-IgG fusion proteins, into visual cortex alters the development and plasticity of ocular dominance columns (Cabelli et al., 1995; Riddle et al., 1995; Galuske et al., 1996 ; Gillespie et al., 1996; Cabelli et al., 1997). To test further the physiological role of BDNF, we studied a transgenic mouse that expresses elevated levels of BDNF in primary visual cortex (V1) postnatally (Huang et al., 1999). We found that unlike the infusion experiments, excess BDNF expressed in mouse visual cortex did not block ocular dominance plasticity. Instead, single neurons in V1 of the BDNF transgenic mice were as susceptible to the effects of monocular deprivation (MD) as neurons in wild-type mice, but only during a precocious critical period. At a time when V1 in the wild-type mouse responded maximally to a 4 d MD with a reduction in its response to deprived eye visual stimulation, the transgenic mouse V1 had already passed the peak of its precocious critical period and no longer responded maximally. This finding suggests a role for BDNF in promoting the postnatal maturation of cortical circuitry.
Motion Capture Based on Body Features

In this paper, a technique based on body features is employed to develop a system for sports training, healthcare and medical diagnosis through reusing huge amount of video resources, and extracting useful information from them. This technique realizes the motion capture by using existing video of human motion. The predefined human model is matched and identified with a sequence of videos. The interested feature points are tracked and calculated using relevant features of the human body. During the tracking, the human model is used for the conditional constraint and corrections. The approach is easy and quick. The efficiency of capture, stability and accuracy of the tracking are improved greatly with a low requirement for hardware specification. The 2D motion data can be generated easily and conveniently for 3D motion reconstruction in this system.
A New Service-Based Computing Security Model with Neural Cryptography

In this paper, we present a new service-based computing security model, which is combined with neural cryptography. Service-based computing is a new and hot research point for telecommunication and computer scientist. Neural cryptography is a new way to create shared secret key. The existed system architecture mentions little about security. The paper presents a new security model, and we discuss the model design based on neural cryptography in the end.
Decomposition of Timed Automata

In this paper we extend the notion of homomorphisms and decomposition to timed automata. This is based on the classical Hartmanis-Stearns decomposition results for nite state automata. As in the classical theory, the existence of non-trivial orthogonal partitions is both necessary and sucient for non-trivial decompositions. Of course, now these partitions have to include both the set of states and the set of timers (or clocks) in the system. We present an example which illustrate the various issues.
The simultaneous loss of Arx and Pax4 genes promotes a somatostatin-producing cell fate specification at the expense of the α- and β-cell lineages in the mouse endocrine pancreas

The specification of the different mouse pancreatic endocrine subtypes is determined by the concerted activities of transcription factors. However, the molecular mechanisms regulating endocrine fate allocation remain unclear. In the present study, we uncover the molecular consequences of the simultaneous depletion of Arx and Pax4 activity during pancreas development. Our findings reveal a so far unrecognized essential role of the paired-box-encoding Pax4 gene. Specifically, in the combined absence of Arx and Pax4, an early-onset loss of mature α- and β-cells occurs in the endocrine pancreas, concomitantly with a virtually exclusive generation of somatostatin-producing cells. Furthermore, despite normal development of the PP-cells in the double-mutant embryos, an atypical expression of the pancreatic polypeptide (PP) hormone was observed in somatostatin-labelled cells after birth. Additional characterizations indicate that such an expression of PP was related to the onset of feeding, thereby unravelling an epigenetic control. Finally, our data provide evidence that both Arx and Pax4 act as transcriptional repressors that control the expression level of one another, thereby mediating proper endocrine fate allocation.
A simple framework for spatio-temporal video segmentation and delayering using dense motion fields

To represent object movement, the conventional approach is to embed the motion information into parametric and/or statistical motion models. However, the inherent complexity of this description is ill suited for application requiring rapid and automatic, albeit significant, responses. Surveillance applications belong to this class and are emerging as the most active field of research in computer vision. For this purpose, we observe that the differential invariants obtained by dense optic flow are capable of accurately describing complex object motion without requiring setting up and initializing models. In this letter, we demonstrate the novel use of such motion descriptors for spatio-temporal object segmentation and delayering of sequences. Our results show the ability of this approach to describe simply and accurately differently moving objects and to be incorporated in segmentation processes that deliver a hierarchical description of object, producing evident improvements to the segmented objects while being computationally efficient
Parallel Generation of the Biological Trees 1

Several biological features are presented by different types of trees. Two types of such trees are considered in this paper. the first type is trees with n external nodes that each internal node have at least two children, and are used in neuro-science and called neuronal dendritic trees. The second type is trees with n internal nodes and m external nodes. This type of trees represent the secondary structure of RNA sequences, and called RNA trees. In this paper, we present two new parallel algorithms for generation of these two biological trees. Both algorithms are adoptive and cost-optimal and generate the trees in B-order. Computations run in an SM SIMD model.
Encoding of Visual Motion Information and Reliability in Spiking and Graded Potential Neurons

We investigated the information about stimulus velocity inherent in the membrane signals of two types of directionally selective, motion-sensitive interneurons in the fly visual system. One of the cells, the H1-cell, is a spiking neuron, whereas the other, the HS-cell, encodes sensory information mainly by a graded shift of its membrane potential. Using a pseudo-random velocity waveform by which a visual grating is moving along the horizontal axis of the eye, both cell types follow the stimulus velocity at higher precision than in response to a step-like velocity function. To measure how much information about the stimulus velocity is preserved in the cellular responses, we calculated the coherence between the stimulus and the neural signals as a function of stimulus frequency. At frequencies up to ∼10 Hz motion information is well contained in the electrical signals of HS- and H1-cells: For HS-cells the coherence value amounts to ∼70%, and for H1-cells this value is ∼60%. Comparing these values with the coherence expected from a linear encoding reveals that the fidelity of the original stimulus is deteriorated in the neural signal partly by neural noise and partly by the nonlinearity inherent in the process of visual motion detection. The degree to which this nonlinearity contributes to the decrease in coherence depends on the maximum velocity used in the experiments; the smaller the stimulus amplitude, the higher the coherence and, thus, the smaller the nonlinearity in encoding of stimulus motion. All these results are in agreement with model simulations in which visual motion is processed by an array of local motion detectors, the spatially integrated output of which is considered the equivalent of the neural signals of HS- and H1-cells.
prebs User Guide

The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using standard microarray summarization algorithms: RPA (Lahti et al., 2011) or RMA (Irizarry et al., 2003). The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. A more detailed algorithm description can be found in (Uziela and Honkela, 2013).
Measuring Student Knowledge and Skills: A New Framework for Assessment.

students, the public and those who run education systems need to know whether children are acquiring the necessary skills and knowledge to become tomorrow's citizens and to continue learning throughout life. International indicators can describe the levels of achievement in various countries, to which others might aspire. They can provide direction for schools' instructional efforts and for students' learning as well as insights into curriculum strengths and weaknesses.
Model Based Non-Rigid Registration Framework for High Dynamic Range Mammography

Mammography screening is the most prominent method for finding breast cancer at an early stage. The acquired mammograms are High Dynamic Range images having a 12 bit gray scale resolution. When viewed by a radiologist, a single image must be examined several times, each time focusing on a different intensity range. So this paper proposes the Computer aided approach for enhancing standard lesions in digital mammograms, where we have developed a Model based Non-rigid Registration Framework (MBNRF) for High Dynamic Range mammogram image enhancement in a fully automatic way. The proposed system considers two views of mammogram images such as MLO (Mediolateral Oblique View) and CC (Craniocaudal) for processing and consists of two parts: 1) Preliminary processing operations involves in use of Contrast limited Adaptive Histogram Equalization technique to remove noise and intensity in homogeneities and 2) Registering the CC (Craniocaudal) model with MLO (Mediolateral Oblique View) mammogram view image by entropy based registration. The algorithm’s performance has been tested on few mammography images in collaboration with radiologists. Key-Words: Image Registration, High Dynamic Range Images, Model based Image registration, Mammogram
An Abstract Generic Framework for Web Site Verification

In this paper, we present an abstract framework for Web site verification which improves the performance of a previous, rewriting-based Web verification methodology. The approximated framework is formalized as a source-to-source transformation which is parametric w.r. t. the chosen abstraction. This transformation significantly reduces the size of the Web documents by dropping or merging contents that do not influence the properties to be checked. This allows us to reuse all verification facilities of the previous system WebVerdi-M to efficiently analyze Web sites. In order to ensure that the verified properties are not affected by the abstraction, we develop a methodology which derives the abstraction of Web sites from their Web specification. An experimental implementation shows a huge speedup w.r. t. a previous methodology which did not use this transformation.
Automatic Recognition of Urban Sound Sources

The goal of the FDAI project is to create a general system that computes an efficient representation of the acoustic environment. More precisely, FDAI has to compute a noise disturbance indicator based on the identification of six categories of sound sources. This paper describes experiments carried out to identify acoustic features and recognition models that were implemented in FDAI. This framework is based on EDS – Extractor Discovery System – an innovative acoustic feature extraction system for sound feature extraction. The design and development of FDAI raised two critical issues. Completeness: it is very difficult to design descriptors that identify every sound source in urban environments, and Consistency: some sound sources are not acoustically consistent. We solved the first issue with a conditional evaluation of a family of acoustic descriptors, rather than the evaluation of a single general-purpose extractor. Indeed, a first hierarchical separation between vehicles (moped, bus, motorcycle and car) and non-vehicles (bird and voice) significantly raised the accuracy of identification of the buses. The second issue turned out to be more complex and is still under study. We give here preliminary results.
Hycon2 Benchmark: Power Network System

As a benchmark exercise for testing software and methods developed in Hycon2 for decen- tralized and distributed control, we address the problem of designing the Automatic Genera- tion Control (AGC) layer in power network systems. In particular, we present three different scenarios and discuss performance levels that can be reached using Centralized Model Predic- tive Control (MPC). These results can be used as a milestone for comparing the performance of alternative control schemes. Matlab software for simulating the scenarios is also provided in an accompanying file.
On combining frequency warping and spectral shaping in HMM based speech recognition

Frequency warping approaches to speaker normalization have been proposed and evaluated on various speech recognition tasks. These techniques have been found to significantly improve performance even for speaker independent recognition from short utterances over the telephone network. In maximum likelihood (ML) based model adaptation a linear transformation is estimated and applied to the model parameters in order to increase the likelihood of the input utterance. The purpose of this paper is to demonstrate that significant advantage can be gained by performing frequency warping and ML speaker adaptation in a unified framework. A procedure is described which compensates utterances by simultaneously scaling the frequency axis and reshaping the spectral energy contour. This procedure is shown to reduce the error rate in a telephone based connected digit recognition task by 30-40%.
Vulnerability Assessment Enhancement for Middleware for Computing and Informatics

Security on Grid computing is often an afterthought. However assessing security of middleware systems is of the utmost importance because they manage critical resources owned by different organizations. To fulfill this objective we use First Principles Vulnerability Assessment (FPVA), an innovative analystic-centric (manual) methodology that goes beyond current automated vulnerability tools. FPVA involves several stages for characterizing the analyzed system and its components. Based on the evaluation of several middleware systems, we have found that there is a gap between the initial and the last stages of FPVA, which is filled with the security practitioner expertise. We claim that this expertise is likely to be systematically codified in order to be able to automatically indicate which, and why, components should be assessed. In this paper we introduce key elements of our approach: Vulnerability graphs, Vulnerability Graph Analyzer, and a Knowledge Base of security configurations.
Tiling Optimizations for 3D Scientific Computations

Compiler transformations can significantly improve data locality for many scientific programs. In this paper, we show iterative solvers for partial differential equations (PDEs) in three dimensions require new compiler optimizations not needed for 2D codes, since reuse along the third dimension cannot fit in cachefor larger problem sizes. Tiling is a program transformation compilers can apply to capture this reuse, but successful application of tiling requires selection of non-conflicting tiles and/or padding array dimensions to eliminate conflicts. We present new algorithms and cost models for selecting tiling shapes and array pads. We explain why tiling is rarely needed for 2D PDE solvers, but can be helpful for 3D stencil codes. Experimental results show tiling 3D codes can reduce miss rates and achieve performance improvements of 17-121% for key scientific kernels, including a 27% average improvement for the key computational loop nest in the SPEC/NAS benchmark MGRID.
Forecasting Scanner Data by Choice-Based Conjoint Models

Choice Based Conjoint analysis is considered the tool of choice for pricing in the U.S. Most Choice Based Conjoint studies are performed on the basis of aggregate models, given insufficient data for individual-level estimation of part-worths. It is known, however, that households can differ considerably. Therefore, managers are interested in the underlying segment structures and make use of a priori defined segments (e.g., choice frequencies) or apply segmentation procedures like k-means on the basis of additional data. DeSarbo, Ramaswamy and Cohen (1995) have proposed a latent class model that allows to consider heterogeneity within Choice Based Conjoint analysis without the necessity to collect additional data. Up to date, it is unclear how different segmentation approaches perform in terms of forecasting (external) real world aggregate shop data. In this contribution, we compare the performance of various segmentation approaches not with an experimental holdout sample but with aggregate scanning data. Our analysis shows that the ignorance of heterogeneity in the aggregate model leads to biased forecasts. In our study the latent class model turns out to perform significantly better than the other approaches considered. Furthermore, we demonstrate that the methodology of modeling heterogeneity has a strong impact on the optimal attribute level. We find that the consideration of a global correction vector is important. We show that this correction vector should be updated frequently while the price effects are valid for longer periods of time.
Structural Basis for Thermostability of β-Glycosidase from the Thermophilic Eubacterium Thermus nonproteolyticus HG102

ABSTRACT The three-dimensional structure of a thermostable β-glycosidase (GlyTn) from the thermophilic eubacterium Thermus nonproteolyticus HG102 was determined at a resolution of 2.4 Å. The core of the structure adopts the (βα)8 barrel fold. The sequence alignments and the positions of the two Glu residues in the active center indicate that GlyTn belongs to the glycosyl hydrolases of retaining family 1. We have analyzed the structural features of GlyTn related to the thermostability and compared its structure with those of other mesophilic glycosidases from plants, eubacteria, and hyperthermophilic enzymes from archaea. Several possible features contributing to the thermostability of GlyTn were elucidated.
Community Structures Are Definable in Networks, and Universal in Real World

Community detecting is one of the main approaches to understanding networks \cite{For2010}. However it has been a longstanding challenge to give a definition for community structures of networks. Here we found that community structures are definable in networks, and are universal in real world. We proposed the notions of entropy- and conductance-community structure ratios. It was shown that the definitions of the modularity proposed in \cite{NG2004}, and our entropy- and conductance-community structures are equivalent in defining community structures of networks, that randomness in the ER model \cite{ER1960} and preferential attachment in the PA \cite{Bar1999} model are not mechanisms of community structures of networks, and that the existence of community structures is a universal phenomenon in real networks. Our results demonstrate that community structure is a universal phenomenon in the real world that is definable, solving the challenge of definition of community structures in networks. This progress provides a foundation for a structural theory of networks.