Miracle-dz/UniFAR-FTUs huggingface dataset:
Birth weight and income: interactions across generations.

This paper attempts to answer a series of questions regarding the interaction of income and birth weight across generations. First, does the effect of the income of a mother during her pregnancy on her infant's birth weight depend on the family's birth weight history (genetic predisposition)? Second, does the effect of low birth weight status on adult life chances depend on income during early childhood? These questions have implications for the way we envision the biological and social worlds as interacting across generations. To address these issues, this study uses intergenerational data from the Panel Study of Income Dynamics, survey years 1968 through 1992. Results of sibling comparisons (family-fixed-effects models) demonstrate that maternal income has a significant impact on birth weight for those infants who are already at high risk hereditarily (i.e., who have a low birth weight parent). However, it is not clear whether income acts as a developmental buffer for low birth weight infants as their lives progress. These findings suggest the existence of biosocial interactions between hereditary predisposition and socio-economic environment.
Pro- and Anti-Inflammatory Cytokine Balance in Major Depression: Effect of Sertraline Therapy

The specific associations between antidepressant treatment and alterations in the levels of cytokines remain to be elucidated. In this study, we aimed to explore the role of IL-2, IL-4, IL-12, TNF-α, TGF-β1, and MCP-1 in major depression and to investigate the effects of sertraline therapy. Cytokine and chemokine levels were measured at the time of admission and 8 weeks after sertraline treatment. Our results suggest that the proinflammatory cytokines (IL-2, IL-12, and TNF-α) and MCP-1 were significantly higher, whereas anti-inflammatory cytokines IL-4 and TGF-β1 were significantly lower in patients with major depression than those of healthy controls. It seems likely that the sertraline therapy might have exerted immunomodulatory effects through a decrease in the proinflammatory cytokine IL-12 and an increase in the anti-inflammatory cytokines IL-4 and TGF-β1. In conclusion, our results indicate that Th1-, Th2-, and Th3-type cytokines are altered in the depressed patients and some of them might have been corrected by sertraline treatment.
Geodesic manifolds with a transitive subset of smooth biLipschitz maps

This paper is connected with the problem of describing path metric spaces that are homeomorphic to manifolds and biLipschitz homogeneous, i.e., whose biLipschitz homeomorphism group acts transitively. Our main result is the following. Let $X = G/H$ be a homogeneous manifold of a Lie group $G$ and let $d$ be a geodesic distance on $X$ inducing the same topology. Suppose there exists a subgroup $G_S$ of $G$ that acts transitively on $X$, such that each element $g \in G_S$ induces a locally biLipschitz homeomorphism of the metric space $(X,d)$. Then the metric is locally biLipschitz equivalent to a sub-Riemannian metric. Any such metric is defined by a bracket generating $G_S$-invariant sub-bundle of the tangent bundle. The result is a consequence of a more general fact that requires a transitive family of uniformly biLipschitz diffeomorphisms with a control on their differentials. It will be relevant that the group acting transitively on the space is a Lie group and so it is locally compact, since, in general, the whole group of biLipschitz maps, unlikely the isometry group, is not locally compact. Our method also gives an elementary proof of the following fact. Given a Lipschitz sub-bundle of the tangent bundle of a Finsler manifold, then both the class of piecewise differentiable curves tangent to the sub-bundle and the class of Lipschitz curves almost everywhere tangent to the sub-bundle give rise to the same Finsler-Carnot-Carath\'eodory metric, under the condition that the topologies induced by these distances coincide with the manifold topology.
Energy Landscapes of Dynamic Ensembles of Rolling Triplet Repeat Bulge Loops: Implications for DNA Expansion Associated with Disease States

DNA repeat domains can form ensembles of canonical and noncanonical states, including stable and metastable DNA secondary structures. Such sequence-induced structural diversity creates complex conformational landscapes for DNA processing pathways, including those triplet expansion events that accompany replication, recombination, and/or repair. Here we demonstrate further levels of conformational complexity within repeat domains. Specifically, we show that bulge loop structures within an extended repeat domain can form dynamic ensembles containing a distribution of loop positions, thereby yielding families of positional loop isomers, which we designate as “rollamers”. Our fluorescence, absorbance, and calorimetric data are consistent with loop migration/translocation between sites within the repeat domain (“rollamerization”). We demonstrate that such “rollameric” migration of bulge loops within repeat sequences can invade and disrupt previously formed base-paired domains via an isoenthalpic, entropy-driven process. We further demonstrate that destabilizing abasic lesions alter the loop distributions so as to favor “rollamers” with the lesion positioned at the duplex/loop junction, sites where the flexibility of the abasic “universal hinge” relaxes unfavorable interactions and/or facilitates topological accommodation. Another strategic siting of an abasic site induces directed loop migration toward denaturing domains, a phenomenon that merges destabilizing domains. In the aggregate, our data reveal that dynamic ensembles within repeat domains profoundly impact the overall energetics of such DNA constructs as well as the distribution of states by which they denature/renature. These static and dynamic influences within triplet repeat domains expand the conformational space available for selection and targeting by the DNA processing machinery. We propose that such dynamic ensembles and their associated impact on DNA properties influence pathways that lead to DNA expansion.
Young children's acquisition of wh-questions: the role of structured input

Two-year-olds learn language quickly but how they exploit adult input remains obscure. Twenty-nine children aged 2;6 to 3;2, divided into three treatment groups, participated in an intervention experiment consisting of four sessions 1 week apart. Pre- and post-intervention sessions were identical for all children: children heard a wh-question and attempted to repeat it; a ‘talking bear’ answered. That same format was used for the two intervention sessions for children in a quasicontrol condition (Group QC). Children receiving modelling (Group M) heard a question twice before repeating it; those receiving implicit correction (Group IC) heard a question, attempted to repeat it, and heard it again. All groups improved in supplying and inverting an auxiliary for target questions with trained auxiliaries. Only experimental children generalized to auxiliaries on which they had not been trained. Very little input, if concentrated but varied, and presented so that the child attends to it and attempts to parse it, is sufficient for the rapid extraction and generalization of syntactic regularities. Children can learn even more efficiently than has been thought.
EEG Study on the Neural Patterns of Brain with Music Stimuli: An Evidence of Hysteresis?

A basic and one of the oldest socio-cognitive domains of the human species is music. Listening to music regularly helps keep the neurons and synapses more active. Neurological studies have identified that music is a valuable tool for evaluating the brain system. It has been observed that different parts of the brain are involved in processing music. They include the auditory cortex, frontal cortex, cerebral cortex and even the motor cortex. The objective of this study is to analyze the effect of Hindustani music on brain activity during normal relaxing conditions using electroencephalography (EEG). Two male (age 20-23) healthy subjects without special musical education participated in the study. EEG signals were acquired at the frontal (F3/F4, F7/F8), temporal (T3/T4, T5/T6), central (C3/C4), parietal (P3/P4), and occipital (O1/O2) lobes of the brains listening to music in three conditions, namely rest before music, with music, and after withdrawal of the music. Frequency analysis was done for the alpha and delta brain rhythms. The finding shows that arousal based activities were enhanced while listening to Hindustani music of contrasting emotions (romantic/sorrow) for both the subjects. The important conclusion is that when the music stimuli is removed, discernible alpha and delta brain rhythms remain for some time, showing residual arousal. This is analogous to ‘Hysteresis’ where the system retains some ‘memory’ of the former stimulated state. This was further corroborated by a non linear analysis (Detrended Fluctuation Analysis) of the alpha rhythms.
YouTube Traffic Monitoring and Analysis by Georgios Dimopoulos

Master of Science in Information and Communication Technologies by Georgios Dimopoulos In this thesis, the YouTube service and the traffic generated from its usage was analyzed. The purpose of this study was to identify the information in the monitored traffic that could be used as metrics or indicators of the progress of individual video sessions. For this purpose, a tool was developed to perform YouTube traffic measurements by means of passive network monitoring. The tool was designed to obtain information regarding the delays introduced in YouTube video sessions, as well as statistical parameters generated during the video playback. Subsequently, the tool was used to perform long duration measurements in the UPC campus network. The analysis of the obtained data set created from the measurements, gave fruit to interesting results concerning how these parameters affect the progress of the video sessions. Moreover, the results of this study will be used to further research the correlation of the studied parameters with users’ quality of experience. Directors: Pere Barlet-Ros, Josep Sanjuas-Cuxart
Regeneration, Plasticity, and Induced Molecular Programs in Adult Zebrafish Brain

Regenerative capacity of the brain is a variable trait within animals. Aquatic vertebrates such as zebrafish have widespread ability to renew their brains upon damage, while mammals have—if not none—very limited overall regenerative competence. Underlying cause of such a disparity is not fully evident; however, one of the reasons could be activation of peculiar molecular programs, which might have specific roles after injury or damage, by the organisms that regenerate. If this hypothesis is correct, then there must be genes and pathways that (a) are expressed only after injury or damage in tissues, (b) are biologically and functionally relevant to restoration of neural tissue, and (c) are not detected in regenerating organisms. Presence of such programs might circumvent the initial detrimental effects of the damage and subsequently set up the stage for tissue redevelopment to take place by modulating the plasticity of the neural stem/progenitor cells. Additionally, if transferable, those “molecular mechanisms of regeneration” could open up new avenues for regenerative therapies of humans in clinical settings. This review focuses on the recent studies addressing injury/damage-induced molecular programs in zebrafish brain, underscoring the possibility of the presence of genes that could be used as biomarkers of neural plasticity and regeneration.
Energy Saving Additive Neural Network

In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multiplications at each stage. Multiplication operation is not an energy efficient operation and consequently it drains the battery of the mobile device. In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called, additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplier-free implementation. In ef-operation, the "product" of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This "product" is used to construct a vector product in $R^N$. The vector product induces the $l_1$ norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron and convolutional neural networks (LeNet).
Next best view system in a 3D object modeling task

Sensor placement for 3D modeling is a growing area of computer vision and robotics. The objective of a sensor placement system is to make task-directed decisions for optimal pose selection. We propose a next best view solution to the sensor placement problem. Our algorithm computes the next best view by optimizing an objective function that measures the quantity of unknown information in each of a group of potential viewpoints. The potential views are either placed uniformly around the object or are calculated from the surface normals of the occupancy grid model. To initiate the collection of new data, the optimal pose is selected from the objective function calculation. The model is incrementally updated from the information acquired in each new view. This process terminates when the number of recovered voxels ceases to increase, yielding the final model. We tested two different algorithms on 8 objects of various complexity, including objects with simple concave, simple hole, and complex hole self-occlusions.
Knowledge management in collaborative learning

Computer-supported collaborative learning is one important mode of Internet learning. Group members communicated with each other with synchronous or asynchronous communication tools. Current researches on collaborative learning paid more attention to the communication tools than to the knowledge management function. A knowledge management method in collaborative learning came up in this paper. The experiment showed that the knowledge extraction, maintenance and share could be accomplished by this method.
A MapReduce Approach for SIFT Feature Extraction

SIFT feature extraction is a computationally intensive problem, for the large scale image, which will take a long time to extract SIFT feature. This paper presents a novel approach, based on MapReduce, to accelerate SIFT feature extraction. A MapReduce based SIFT feature extraction model is established, and the original SIFT feature extraction progress is reformed to fit the model. We have implemented the MapReduce based algorithm and evaluated it on a Hadoop cluster. The experimental results show that this approach can extract SIFT feature simultaneously on Hadoop cluster with a good speed up rate.
All-Optical Code Division Multiplexing Switching Network Based on Self-Routing Principle

The tera-bit order capacity of ultrahigh-speed and wide-band networks will become necessary to provide highly advanced multimedia services. In conventional networks, electronic circuits limit the speed capability of the networks. Consequently, all-optical networks are essential to realize ultrahighspeed and wide-band communications. In this paper, we propose the configuration of an all-optical code division multiplexing (CDM) switching network based on self-routing principles and the structure of a nonlinear all-optical switching device as one of the key components for the network. We show that the required performances of the optical devices used in the CDM switching fabric are lower than those used in the TDM and illustrate the basic transmission characteristics of the switching device utilizing FD-BPM. To evaluate the multiplexing performance, we demonstrate the maximum number of channels under an error-free condition and the BER characteristics when the Gold sequence is applied as one of the CDM code sets, and show that the network of the sub-tera-bit order capacity is realizable by adopting TDM, WDM and CDM technologies. We also illustrate the packet assembly method suitable for self-routing transmissions and one of network architectures where the proposed switching fabric can be exploited. key words: all-optical switch, code division multiplexing, self-
Thymus and autoimmunity: capacity of the normal thymus to produce pathogenic self-reactive T cells and conditions required for their induction of autoimmune disease

BALB/c athymic nu/nu mice spontaneously developed organ-specific (gastritis, thyroiditis, oophoritis, or orchitis) and systemic (arteritis, glomerulonephritis, and polyarthritis) autoimmune diseases when transplanted with neonatal BALB/c thymuses. Transplantation of thymuses from adult BALB/c mice was far less effective in inducing histologically evident organ-specific autoimmune disease in nu/nu mice. Autoimmune disease developed, however, when adult thymuses were irradiated at a T cell-depleting dose before transplantation. Engrafting newborn thymuses into BALB/c mice T cell depleted by thymectomy, irradiation, and bone marrow transplantation produced similar organ-specific autoimmune disease as well, but thymus engrafting into T cell-nondepleted BALB/c mice (i.e., mice thymectomized as adults, but not irradiated) did not, despite the fact that transplanted thymuses grew well in both groups of mice. The mice with organ-specific autoimmune disease produced autoantibodies specific for the respective organ components, such as gastric parietal cells, thyroglobulins, oocytes, or sperm. The thymus-transplanted nu/nu mice also had hypergammaglobulinemia and developed anti-DNA autoantibodies, rheumatoid factors, and immune complexes in the circulation. These results indicate that: (a) the thymus of a murine strain that does not develop spontaneous autoimmune disease can produce pathogenic self- reactive T cells that mediate organ-specific and/or systemic autoimmune diseases; and (b) such self-reactive T cells, especially those mediating organ-specific autoimmune disease, spontaneously expand and cause autoimmune disease when released to the T cell-deficient or - eliminated periphery.
Object-Guided Ant Colony Optimization Algorithm with Enhanced Memory for Traveling Salesman Problem

In this study, we presents an object-guided ACO algorithm which is consisted of ants with enhanced memory. In the process of solution construction, each ant stores a complete solution in its enhanced memory. Each time ant selects a solution component probabilistically, it will calculate the difference between current solution and the new solution after adding the selected component and then Metropolis accepting rule, which has been used in simulated annealing algorithm successfully, is used to decide whether to accept the component or discard it. The simulation results, which were carried on benchmark traveling salesman problems, show that the improvement of individual intelligence can improve the performance of ACO algorithm remarkably.
Managing End-to-End Delay for VoIP Calls in Multi-Hop Wireless Mesh Networks

In current wireless networks, channel access is controlled via either a TDMA- or a CSMA-based scheme. While usable in single-hop networks, these techniques are far from optimal and result in significant per-hop and per-packet delay an jitter. This makes multi-hop mesh networks unsuitable for several in-demand applications, including VoIP. We present a new medium access control (MAC) protocol for wireless mesh networks caring delay-sensitive isochronous data. In our scheme, nodes use simple local mechanisms to form adaptive transmission schedules which attain the desired quality of service. Simulations show that our scheme achieves near-optimal capacity, minimal jitter, and a weaker correlation between route length and end-to-end delay.
Cell surgery and growth factors in dry age-related macular degeneration: visual prognosis and morphological study

Background The aim of this research was to study the overall restoration effect on residual retinal cells through surgically grafted autologous cells onto the surrounding tissue, choroid and retina in order to produce a constant secretion of growth factors (GFs) in dry age-related macular degeneration (AMD) patients. Results 6 months after surgery, several values were statistically significant in the group with higher RTA. Also patient compliance analysis (PCA) in relation to functional change perception appeared to be very good. Methods Thirty-six eyes of 25 patients (range 64-84 years of age) affected by dry AMD were included in study, and divided in two groups by spectral domain-optical coherence tomography (SD-OCT): group A with retinal thickness average (RTA) less than 250 microns (μm) and group B with RTA equal to or more than 250 μm. Adipocytes, adipose-derived stem cells from the stromal-vascular fraction, and platelets from platelet-rich plasma were implanted in the suprachoroidal space. Particularly, the following parameters were evaluated: best corrected visual acuity (BCVA) for far and near distance, retinal thickness maps, scotopic and photopic electroretinogram (ERG), and microperimetry (MY). All statistical analyses were performed with STATA 14.0 (Collage Station, Texas, USA). Conclusions The available set of GFs allowed biological retinal neuroenhancement. After 6 months it improved visual performance (VP), but the increase was better if RTA recorded by OCT was higher, probably in relation to the presence of areas with greater cellularity.
The Latest Trend of Low Power High-Speed CMOS I/O Technology for Photonic Network

We introduce the latest trend of high-speed input/output (I/O) interface technology for the next generation high-efficiency network. The development of high-speed I/O technology contributes to lower power and lower cost system in photonic network. In this paper, we introduce the high-speed I/O trends, and discuss the CMOS process technology and circuit design technique which support it. Finally we describe the problems and future outlooks.
Natural Language Engineering Discourse Structure and Language Technology Discourse Structure and Language Technology

An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.