Miracle-dz/UniFAR-FTUs huggingface dataset:
LOSS OF GROUP CARBOHYDRATE DURING MOUSE PASSAGES OF A GROUP A HEMOLYTIC STREPTOCOCCUS

In the course of rapid passages through mice a strain of group A type 27 hemolytic streptococcus was found to have lost its group carbohydrate without the loss of type-specific precipitinogens, agglutinogens, or its capacity to induce protective antibodies in rabbits, and without the acquisition of the carbohydrate of another group. The loss of group carbohydrate was shown to be complete, within the limits of the methods for its detection. Extracts of the anomalous strain did not react with group A antisera; and antisera prepared with organisms of this anomalous strain did not contain demonstrable antibodies for the group carbohydrate. Bacterial suspension of the anomalous strain failed to absorb any appreciable amount of group-specific antibody. The fact that the anomalous strain lacking group-specific carbohydrate, C, was derived from the original was established by the demonstration of persistence of its other characteristics, in particular the precipitinogens, agglutinogens, and antigens responsible for protective antibodies of type 27.
Robust methods based on the hosvd for estimating the model order in PARAFAC models

Parallel factor (PARAFAC) analysis represents a decomposition of a tensor into a minimum sum of rank one tensors. For this task, one crucial problem is the estimation of the number of rank one components that are required to represent the tensor. This problem is also known as model order estimation. Recently we have developed new R-dimensional techniques based on the HOSVD to estimate the number of components in multi-dimensional harmonic retrieval problems (i.e., R-D EFT, R-D AIC, and R-D MDL). In this paper, we apply these R-D methods to the PARAFAC model, which is a more general multi-way data model, and show that they outperform T-CORCONDIA, a nonsubjective form of CORCONDIA, in terms of the probability of detection as well as the required computational complexity.
Know Thy Avatar

Virtual environments enable people to experience extra­ ordinary identities or circumstances. People can take on superhero or super­villain roles using digital avatars in virtual space. By acting as these avatars, individuals may learn new behaviors and model their own, real­life behaviors after them (Bandura, 1977; Bem, 1972). The virtual environment is, thus, a vehicle for observation, imitation, and modeling; players’ avatars may fuel these processes. Recent empirical research confirms that the behavior of players’ avatars can affect players’ self­concepts, cogni­ tions, and feelings (Gentile et al., 2009; Greitemeyer & Osswald, 2010). Thus, concepts related to avatar behav­ iors in general (e.g., fighting against evil) or to particular avatars (e.g., Superman) may affect subsequent behavior (e.g., good deeds). Identification with an avatar is corre­ lated with avatar­consistent behavior in the real world (Rosenberg, Baughman, & Bailenson, 2013). In the exper­ iments reported here, we investigated whether certain types of avatars and avatars’ behaviors could promote pro­ or antisocial actions in everyday behavior.
Bone Morphogenic Protein Signaling Is a Major Determinant of Dentate Development

To understand life-long neurogenesis in the dentate gyrus (DG), characterizing dentate neural stem cells and the signals controlling their development are crucial. In the present study, we show that bone morphogenic protein (Bmp) signaling is a critical regulator of embryonic dentate development, required for initiating neurogenesis in embryonic DG progenitors and required for the establishment of dentate neural stem cells postnatally. We tested the hypothesis that Bmp signaling regulates dentate development in part by controlling the expression of Lef1, a Wnt responsive transcription factor expressed in dentate stem cells and absolutely required for dentate granule cell production. Bmp activation through the Acvr1 receptor induced Lef1 expression and neurogenesis in the embryonic DG. Ectopic expression of Bmp7 in the embryonic midline increased DG neurogenesis and inhibition of local Bmp signaling decreased embryonic DG neurogenesis. Mice with selective loss of Bmp expression due to defective meningeal development or with selective conditional deletion of meningeal Bmp7 also have dentate developmental defects. Conditional deletion of Activin receptor type I (Acvr1) or Smad4 (a downstream target nuclear effector of Bmp signaling) in DG neural stem cells resulted in defects in the postnatal subgranular zone and reduced neurogenesis. These results suggest that Acvr1-mediated meningeal Bmp signaling regulates Lef1 expression in the dentate, regulating embryonic DG neurogenesis, DG neural stem cell niche formation, and maintenance.
Elastic Full-Waveform Inversion for a Synthetic VTI Model from Valhall Field

One of the main challenges for full-waveform inversion (FWI) is taking into account both anisotropy and elasticity. Here, we perform FWI for a synthetic 2D elastic VTI (transversely isotropic with a vertical symmetry axis) model based on the geologic section at Valhall field in the North Sea. Multicomponent data are generated by a finitedifference code for surface acquisition. The model is parameterized in terms of the Pand S-wave vertical velocities (VP0 and VS0) and the P-wave NMO and horizontal velocities (Vnmo and Vhor). We apply FWI in the time domain using a multiscale approach with three frequency bands. An approximate Hessian matrix, computed using the L-BFGS algorithm, is employed to scale the gradients of the objective function and improve the convergence. In the absence of significant diving-wave energy, FWI allows us to update the model primarily using reflection data. The largest and most accurate updates are obtained for the velocities VP0 and Vhor. The vertical velocities VP0 and VS0 are updated both in the shallow and deeper parts of the model, whereas Vnmo and Vhor change primarily in the near-surface horizons. Further improvement is expected after extending the frequency range for the inversion.
Delays in dwarf novae I: The case of SS Cygni

Using the disc instability model and a simple but physically reasonable model for the X-ray, extreme UV, UV and optical emission of dwarf novae we investigate the time lags observed between the rise to outburst at dierent wavelengths. We find that for "normal", i.e. fast-rise outbursts, there is good agreement between the model and observations provided that the disc is truncated at a few white dwarf radii in quiescence, and that the viscosity parameter is 0:02 in quiescence and 0:1 in outburst. In particular, the increased X-ray flux between the optical and EUV rise and at the end of an outburst, is a natural outcome of the model. We cannot explain, however, the EUV delay observed in anomalous outbursts because the disc instability model in its standard -prescription form is unable to produce such outbursts. We also find that the UV delay is, contrary to common belief, slightly longer for inside-out than for outside-in outbursts, and that it is not a good indicator of the outburst type.
Investigation on Three Dimensional Image Measurement for Geometrical Parameters of the Cutter

In this investigation, image measurement technology is applied for development of an image measurement system for geometrical parameters of the cutter. CCD, LED light source, servo motor and x-y bi-axial stage have been integrated in this system. It can be utilized for measuring geometrical parameters of the milling cutter and drill. The measured parameters include diameter, helix angle, arc radius and point angle. To analyze repeatability and reproducibility of the system, long form is introduced. Measurement results by the system have been compared with that by ZOLLER. The analysis results show that dimensional precision of 19 µm and the angle precision of 0.4° can be achieved by this measurement system. There are 40 µm differences in dimensional measurement, and 0.8 °differences in angle measurement between the results of the system and ZOLLER.The advantages of this measurement system are compact, lower cost and easier operation. And with the above measurement characteristics, this system can be available for cutter manufacture and inspection.
Motion parallax, stereoscopy, and the perception of depth: practical and theoretical issues

This paper deals with practical and theoretical issues related to motion parallax. Motion parallax implies that the perception of depth can be extracted from a temporal sequence of images that contain different perspectives. The present paper will focus on the relative effectiveness of motion parallax as compared to stereoscopic depth perception. It will be argued that motion parallax alone will generate a strong sense of depth, even in the absence of stereoscopic cues. Two studies directly comparing motion parallax and stereoscopy will be presented showing that, under certain conditions, these cues can be equally efficient and that there can be an additive effect when both cues are present. A theoretical discussion on the effect of optical distortions and how such distortions can influence motion parallax from a viewer's perspective will follow. Particular emphasis will be placed on the optical distortions produced by progressive addition lenses used to correct for presbyopia. Finally, research avenues will be proposed to answer some of the theoretical and practical issues related to motion parallax in our daily activities.
Prediction of permuted super-secondary structures in β-barrel proteins

Computational structure prediction methods based on learning are poorly tractable for transmembrane β-barrel (TMB) proteins, for it is difficult to observe them with standard experimental techniques. Generally, those structures are not only a series of β-strands where each is bonded to the preceding and succeeding ones in the primary sequence, but they may contain Greek key or Jelly roll motifs as well. This may be described as a permutation on the order of the bonded segments. We model the protein folding problem with minimum energy into the search of the longest closed path in a weighted graph with respect to a given permutation. With dynamic programming, the algorithm runs in O(N2) for an identity permutation, and at most O(N4) for the Greek key motifs, where N is the number of amino acids. The prediction accuracy as well as the discrimination ability is favorably comparable with existing works.
Power-efficient delay-insensitive codes for data transmission

Introduces and formalizes the notion of dynamic delay-insensitive codes for data communication. We describe several codes and protocols designed to optimize switching energy expended at the data pins during data transmission in asynchronous systems. These include adaptations of some existing communication methods as well as some new techniques for reducing energy used in dynamic data communication between delay-insensitive circuits. <<ETX>>
Edge Model Based Super Resolution Video Generation

As applications involving the capture of digital images and video become more demanding in the recent years and at the same time more ambitious for critical real time applications like video surveillance. There is a driving need for digital images and videos of higher resolutions and visual quality. Since, the video is a sequence of still images representing scenes in motion, the problem of generation of high resolution video is narrowed down to the problem of high resolution image generation from low resolution video frame. High resolution image can be obtained using the conventional interpolation methods. Conventional linear interpolation schemes fail to capture the fast evolving statistics around edges and consequently produce interpolated images with blurred edges and annoying artifacts. Moreover, the relationship between high- resolution pixels and low-resolution pixels plays the critical role in the performance of a learning based interpolation algorithm. Since edges are presumably very important features in natural images, retaining the geometric regularity of edges becomes the driving need to develop new approaches for image processing tasks. To generate sharp edges with no artifacts in the high resolution video is the main objective of this paper.
Age and the Neural Network of Personal Familiarity

Background Accessing information that defines personally familiar context in real-world situations is essential for the social interactions and the independent functioning of an individual. Personal familiarity is associated with the availability of semantic and episodic information as well as the emotional meaningfulness surrounding a stimulus. These features are known to be associated with neural activity in distinct brain regions across different stimulus conditions (e.g., when perceiving faces, voices, places, objects), which may reflect a shared neural basis. Although perceiving context-rich personal familiarity may appear unchanged in aging on the behavioral level, it has not yet been studied whether this can be supported by neuroimaging data. Methodology/Principal Findings We used functional magnetic resonance imaging to investigate the neural network associated with personal familiarity during the perception of personally familiar faces and places. Twelve young and twelve elderly cognitively healthy subjects participated in the study. Both age groups showed a similar activation pattern underlying personal familiarity, predominantly in anterior cingulate and posterior cingulate cortices, irrespective of the stimulus type. The young subjects, but not the elderly subjects demonstrated an additional anterior cingulate deactivation when perceiving unfamiliar stimuli. Conclusions/Significance Although we found evidence for an age-dependent reduction in frontal cortical deactivation, our data show that there is a stimulus-independent neural network associated with personal familiarity of faces and places, which is less susceptible to aging-related changes.
The Art of Integrating Care: Theories Revisited

Integration of care is high on public health agendas all around the world. The development and implementation of integrative arrangements has been promoted for more or less two decades. Despite this every so often extensive history, there are recognised needs to take research into areas yet poorly explored, which include measures and outcomes of integrated care. On the other hand, existing evidence and knowledge can probably become more ennobled and thereby contribute to a deeper understanding of the compound art of integrating of health care services. Accordingly, the aim of this article is to re-evaluate and synthesise some revisited theories for the facilitation of sustainable integrated care solutions. This exploration shows it is important to have crucial prerequisites for integration in place: both functional and interactional conditions. This appears to be an organic process where the stakeholders go through gradual changes until the optimum level of integration as well as mutualistic interactions are established. It could be argued that refined knowledge could be excerpted from existing research. Then again, this strategy does not exclude actions for new research in poorly explored areas. Both approaches are important for the development of sustainable integrated care.
Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing

Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with bounded relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1% in server consolidation quality for a reduction about 50% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.
A Novel JSCC Scheme for UEP-Based Scalable Video Transmission Over MIMO Systems

In this paper, we propose a novel joint source-channel coding (JSCC) scheme for scalable video transmission over multiple-input multiple-output (MIMO) systems. By exploiting the diversity of MIMO antennas and forward error correction (FEC)-based protection, our method aims to provide unequal error protection (UEP) for the video layers, which are mapped to appropriate antennas. Moreover, JSCC is also considered that we extract a proper subset of video layers and allocate suitable FEC redundancy to them. Jointly considering video layer extraction, FEC rate allocation, and video layer scheduling, we are able to achieve UEP so as to minimize end-to-end distortion. We formulate the scheme as a nonlinear integer optimization problem, which is known to be NP-hard. To find a near-optimal solution efficiently, we propose a low-complexity branch-and-bound algorithm, which partitions the original problem into a series of subproblems by a video layer branching technique. In each branch, the upper and lower distortion bounds are derived. In particular, we transform the video layer scheduling subproblem into a 0/1 multiple knapsack problem, which is NP-complete, and employ an evolutionary Lagrangian method to find a solution efficiently. For the FEC allocation subproblem, a Lagrange duality algorithm with fuzzy surrogate subgradient is proposed. The experimental results demonstrate that the proposed method has good efficiency while achieving close performance to the optimal results.
Bit Error Rate Analysis for BPSK Modulation in Presence of Noise and Two Co-channel Interferers

Summary In this paper the bit error rate (BER) analysis of binary phase shift keying (BPSK) modulated signal detection in presence of two BPSK modulated co-channel interfering signals and additive white Gaussian noise is presented. The detection of a desired signal with minimum achievable BER in co-channel interfering environment with two interferers was analyzed. The minimum achievable BER was obtained at interferers gain less than -10dB. Noise effect of AWGN channel can be observed using scatter plot rather than BER values.
Asynchronous Contention Resolution Diversity ALOHA: Making CRDSA Truly Asynchronous

Following the introduction of contention resolution diversity slotted ALOHA (CRDSA), a number of variants of the scheme have been proposed in literature. A major drawback of these slotted random access (RA) schemes is related to the need to keep slot synchronization among all transmitters. The volume of signaling generated to maintain transmitters' slot synchronization is impractical for large networks. In this paper, we describe in detail asynchronous contention resolution diversity ALOHA (ACRDA), which represents the evolution of the CRDSA RA scheme. ACRDA provides better throughput performance with reduced demodulator complexity and lower transmission latency than its predecessor while allowing truly asynchronous access to the shared medium. The performance of the ACRDA protocol is evaluated via mathematical analysis and computer simulations and is compared with that of CRDSA.
Inverse kinematics of a 6 DOF industrial robot manipulator based on bio-inspired multi-objective optimization techniques

This paper presents the solution of the inverse kinematics problem of a 6-Degrees-of-Freedoom (6 DOF) industrial robot manipulator using two bio-inspired multiobjective optimization techniques: The Fast and Elitists NonDominated Sorting Genetic Algorithm (NSGA-II) and the recently developed Bacterial Chemotaxis Multi-Objective Algorithm (BCMOA). The optimization problem described establishes reaching a desired set of position and orientation for the robot end effector (EF). The desired set to be solved is taken from the surface of a Computer Aided Design (CAD) based workpiece model. Once forward kinematics of the studied robot MOTOMAN MH6 is modeled, the workpiece is placed inside the known robot workspace based on its operational standing. Then, the objective functions that minimize the position and orientation errors are defined and the techniques are implemented. Finally, the performance to solve the inverse kinematics problem of both techniques is compared.
IRIS Texture Analysis and Feature Extraction for Biometric Pattern Recognition

In this paper we propose a new biometric-based Iris feature extraction system. The system automatically acquires the biometric data in numerical format (Iris Images) by using a set of properly located sensors. We are considering camera as a high quality sensor. Iris Images are typically color images that are processed to gray scale images. Then the Feature extraction algorithm is used to detect “IRIS Effective Region (IER)” and then extract features from “IRIS Effective Region (IER)” that are numerical characterization of the underlying biometrics. Later on this work will be helping to identify an individual by comparing the feature obtained from the feature extraction algorithm with the previously stored feature by producing a similarity score. This score will be indicating the degree of similarity between a pair of biometrics data under consideration. Depending on degree of similarity, individual can be identified.