Miracle-dz/UniFAR-FTUs huggingface dataset:
Lateral cell movement driven by dendritic interactions is sufficient to form retinal mosaics

The formation of retinal mosaics is thought to involve lateral movement of retinal cells from their clonal column of origin. The forces underlying this lateral cell movement are currently unknown. We have used a model of neurite outgrowth combined with cell movement to investigate the hypothesis that lateral cell movement is guided by dendritic interactions. We have assumed that cells repel each other in proportion to the degree of dendritic overlap between neighbouring cells. Our results first show that small cell movements are sufficient to transform random cell distributions into regular mosaics, and that all cells within the population move. When dendritic fields are allowed to grow, the model produces regular mosaics across all cell densities tested. We also find that the model can produce constant coverage of visual space over varying cell densities. However, if dendritic field sizes are fixed, mosaic regularity is proportional to the cell density and dendritic field size. Our model suggests that dendritic mechanisms may therefore provide sufficient information for rearrangement of cells into regular mosaics. We conclude by mentioning possible future experiments that might suggest whether dendritic interactions are adaptive or fixed during mosaic formation.
Identity for NPMLE in Missing and Biased Sampling Models.

We derive an identity for NPMLE in non-parametric missing data models and biased sampling models, which almost says that the NPMLE is ecient. Application of empirical process theory to the identity provides us with a straightforward consistency and eciency proof. The identity is illustrated with the random truncation model.
Recognition of gene regulatory sequencesby bagging of neural

We use an ensemble of multilayer perceptrons to build a model for a type of gene regulatory sequence called a G-box. A variant of the bagging method (bootstrap-and-aggregate) improves the performance of the ensemble over that of a single network. Through a decomposition of the generalization error of the ensemble into bias and variance components, we estimate this error from the hold-out samples of the individual networks. We test the model on putative G-boxes, on sequences upstream of light-regulated genes, and on a control group and demonstrate that the model separates these groups eeciently. 1 Regulatory sequences Genome-sequencing projects have churned out gi-gabytes of data and their pace is increasing. Millions of sequences made up from a small alphabet (four letters for DNA or RNA data, twenty letters for protein data) are available together with their biological description. Confronted to the amount and complexity of the data, molecular biology has turned to statistical and machine learning techniques to assist in the interpretation of this data. Neural networks are an important technique for such tasks because they can model the complex nonlinear relationships present in biological sequences 1]. The small alphabets describing biological sequences provide a powerful and homogeneous quan-tiication of biological data. Because of this quantitative aspect, many problems from molecular biology are amenable to computational techniques. The two main classes of problems when trying to discover relevant patterns in biological sequences are search-by-content and search-by-signal. In search-by-content, we try to identify biological regions having a signiicant extension (for example , more than a few hundred bases letters]) by modeling their sequential content. Examples of important extended regions in the case of DNA sequences are genes or coding regions in genes (called exons). Methods for solving these problems are hidden Markov models, hybrids of hidden Markov models and neural networks, and stochastic grammars 1]. In search-by-signal, we try to identify smaller regions (typically, a few bases to a few tens of bases) by modeling the statistical correlations between the bases. Examples of important DNA signals are splice sites, which determine the beginning and end of non-coding regions of genes, or the signals controlling the activity of a gene (called regulatory sequences). Neural networks such as multilayer per-ceptrons 7, 10] (MLPs) and time-delay neural networks 12] have been widely used for these tasks. Such neural networks work as a window of several tens of bases sliding along the DNA sequence. Each …
3D Densely Connected Convolutional Network for the Recognition of Human Shopping Actions

In recent years, deep convolutional neural networks (CNNs) have shown remarkable results in the image domain. However, most of the neural networks in action recognition do not have very deep layer compared with the CNN in the image domain. This thesis presents a 3D Densely Connected Convolutional Network (3D-DenseNet) for action recognition that can have more than 100 layers without exhibiting performance degradation or overfitting. Our network expands Densely Connected Convolutional Networks (DenseNet) [32] to 3D-DenseNet by adding the temporal dimension to all internal convolution and pooling layers. The internal layers of our model are connected with each other in a feed-forward fashion. In each layer, the feature-maps of all preceding layers are concatenated along the last dimension and are used as inputs to all subsequent layers. We propose two different versions of 3D-DenseNets: general 3D-DenseNet and lite 3D-DenseNet. While general 3D-DenseNet has the same architecture as DenseNet, lite 3D-DenseNet adds a 3D pooling layer right after the first 3D convolution layer of general 3D-DenseNet to reduce the number of training parameters at the beginning so that we can reach a deeper network. We test on two action datasets: the MERL shopping dataset [69] and the KTH dataset [63]. Our experiment results demonstrate that our method performs better than the state-of-the-art action recognition method on the MERL shopping dataset and achieves a competitive result on the KTH dataset.
Yoga and the Ability to Counteract Negative Effects of Stress and Trauma

Many individuals today struggle with the effects of chronic stress, whether due to social and environmental factors or as a result of trauma embedded in their psychophysiology. Research has demonstrated that once an individual endures a traumatic event, there are undesirable changes that occur in the brain and body It is of the utmost importance that mental health counselors consider the relationship between the body and mind. The purpose of this paper is to describe the negative impact traumatic experiences and chronic stress has on the brain and body. Furthermore, the ways in which yoga practice can mitigate trauma symptoms and stress will be explored. Incorporating yoga as an adjunct to talk therapy has been found to lower stress levels while also working to repair biological mechanisms holding these ongoing stress patterns in place. Implications for mental health counselors will be discussed.
Lossless Subband Image Compression Using Integer Coe(cid:14)cient Filter Banks

A new class of two-channel (cid:12)lter banks having integer (cid:12)lter coe(cid:14)cients is proposed. The inte-ger (cid:12)lter coe(cid:14)cients are obtained by maximizing for subband coding gain. The numberof bits required to represent the subband samples are kept low through e(cid:14)cient nonlinear implementation techniques. Several integer coe(cid:14)cient (cid:12)lter banks are tested for lossless image compression. An octave-band (dyadic) frequency partitioning with 6-stages is preferred. In this way, we achieve a progressive image transmission based on multiresolution image representation in a lossless mode. Two di(cid:11)erent coding strategies are investigated. Firstly, enumeration techniques from pyramid vector quantization (PVQ) scheme are adopted. Secondly, a classwise entropy coding scheme is employed. Both coding schemes tend to perform fairly well, however, the classwise entropy coding scheme performs better than the PVQ based scheme. The compression ratios are encouraging compared to the best results re-ported so far in the literature.
Literally unplanned literary tourism in two municipalities in rural Sweden

Recently, artistic places and places known from movies induced an increasing tourism. However, the fast development also challenges the rural planning authorities that are not prepared to manage the high number of tourists. In this paper focus is put on two rural municipalities in southern Sweden. The purpose of this paper is to scrutinize and compare tourism development and tourism planning in these municipalities. This was accomplished by mapping tourism facilities and by in-depth interviews with the local tourism managers. It is argued that tourism planning should be an integrated part of the rural planning process for achieving sustainable development. However, the case studies show that tourism planning does not match this requirement. Although tourism is important to the local labor market, the municipal authorities are hardly engaged in tourism at all. Instead tourism planning was outsourced to public-private companies mainly representing the interests of the involved tourism entrepreneurs.
Mediating Effects of Social Support and Internalized Homonegativity on the Association Between Population Density and Mental Health Among Gay and Bisexual Men.

PURPOSE Depression negatively impacts the health and well-being of gay and bisexual men (GBM). However, little is known about the contexts in which rural GBM live relative to those living in urban areas and their overall mental health. The aim of this study was to examine associations between population density and depressive symptoms and the role of internalized homonegativity and social support as potential mediators. METHODS A nationally representative sample of 1071 GBM (mean age = 40.24) was enrolled. Participants provided their zip codes, which were categorized according to population density and rank-normalized. RESULTS In a path analysis model adjusted for race/ethnicity, college education, age, and relationship status, higher population density was significantly associated with increased social support (B = 0.11, P = 0.002) and decreased internalized homonegativity (B = -0.06, P < 0.001). In turn, lower social support (B = -2.93, P < 0.001) and greater internalized homonegativity (B = 4.93, P < 0.001) were significantly associated with greater depressive symptoms. The indirect effects of population density on depression through social support (B = -0.33, P < 0.001) and internalized homonegativity (B = -0.31, P < 0.001) were statistically significant, suggesting evidence for mediation of the effects. CONCLUSIONS These results indicate that living in less inhabited areas acts on depressive symptoms through mechanisms of lower social support and higher internalized homonegativity. These findings suggest that social contexts in which GBM live can affect mental health outcomes and indicate the need for further support and inclusion of GBM, especially in less inhabited areas.
Linguistic Evolution Through Language Acquisition: Learning, bottlenecks and the evolution of recursive syntax

Human language is a unique natural communication system for two reasons.1 Firstly, the mapping from meanings to signals in language has structural properties that are not found in any other animal’s communication systems. In particular, syntax gives us the ability to produce an infinite range of expressions through the dual tools of compositionality and recursion. Compositionality is defined here as the property whereby “the meaning of an expression is a monotonic function of the meaning of its parts and the way they are put together. ” (Cann 1993:4) Recursion is “the phenomenon by which a constituent of a sentence dominates another instance of the same syntactic category . recursion is the principle reason that the number of sentences in a natural language is normally taken to be infinite” (Trask 1993:229-230). Together with recursion‘ compositionality is the reason that this infinite set of sentences can be used to express different meanings. Secondly, at least some of the content of this mapping is learned by children through observation of others’ use of language. This seems not to be true of most, maybe all, of animal communication (see review in Oliphant, this volume). In this chapter I formally investigate the interaction of these two unique properties of human language: the way it is learned and its syntactic structure.
Keplerian Motion of Broad-Line Region Gas as Evidence for Supermassive Black Holes in Active Galactic Nuclei

Emission-line variability data on NGC 5548 argue strongly for the existence of a mass of order 7 × 107 M☉ within the inner few light-days of the nucleus in the Seyfert 1 galaxy NGC 5548. The time-delayed response of the emission lines to continuum variations is used to infer the size of the line-emitting region, and these determinations are combined with measurements of the Doppler widths of the variable line components to estimate a virial mass. The data for several different emission lines spanning an order of magnitude in distance from the central source show the expected V ∝ r-1/2 correlation and are consistent with a single value for the mass.
Mechanisms for sealing of porous low-k SiOCH by combined He and NH3 plasma treatment

Porous dielectric materials, such as SiOCH, are used as the insulator in interconnect wiring in microelectronics devices to lower the dielectric constant and so decrease the RC time delay. Sealing of the pores (up to a few nm in diameter) is necessary to prevent degradation of the low-k properties during subsequent processing steps by diffusion of reactants through the pores into the material. Sequential treatment of porous SiOCH by He and NH3 plasmas is potentially a means of sealing pores while maintaining the low-k of the dielectric. The He plasma activates surface sites to accelerate the reactions responsible for pore sealing. NH3 plasma treatment completes the sealing through one of two mechanisms resulting from the adsorption of NHx radicals — catalyzing the formation of a densified surface layer or formation of Si-N, C-N and N-N bonds to bridge over the pore. In this paper, we discuss mechanisms for pore sealing bridging bonds based on results from an integrated computational investigation of the e.
Tie2 Signaling Enhances Mast Cell Progenitor Adhesion to Vascular Cell Adhesion Molecule-1 (VCAM-1) through α4β1 Integrin

Mast cell (MC) activation contributes considerably to immune responses, such as host protection and allergy. Cell surface immunoreceptors expressed on MCs play an important role in MC activation. Although various immunoreceptors on MCs have been identified, the regulatory mechanism of MC activation is not fully understood. To understand the regulatory mechanisms of MC activation, we used gene expression analyses of human and mouse MCs to identify a novel immunoreceptor expressed on MCs. We found that Tek, which encodes Tie2, was preferentially expressed in the MCs of both humans and mice. However, Tie2 was not detected on the cell surface of the mouse MCs of the peritoneal cavity, ear skin, or colon lamina propria. In contrast, it was expressed on mouse bone marrow–derived MCs and bone marrow MC progenitors (BM-MCps). Stimulation of Tie2 by its ligand angiopoietin-1 induced tyrosine phosphorylation of Tie2 in MEDMC-BRC6, a mouse embryonic stem cell-derived mast cell line, and enhanced MEDMC-BRC6 and mouse BM-MCp adhesion to vascular cell adhesion molecule-1 (VCAM-1) through α4β1 integrin. These results suggest that Tie2 signaling induces α4β1 integrin activation on BM-MCps for adhesion to VCAM-1.
Relationship Between Serum Albumin Levels and Infections in Newborn Late Preterm Infants

Background We aimed to evaluate the clinical value of serum albumin levels for the evaluation and prognosis of late preterm infants with infections. Material/Methods This was a retrospective study performed in late preterm infants admitted at the neonatal intensive care unit (NICU) of the Liaocheng People’s Hospital between July 2012 and March 2013. Data, including laboratory test results, neonatal critical illness score (NCIS), perinatal complications and prognosis, were analyzed. The newborn infants were divided into 3 groups according to their serum albumin levels, (≥30 g/L, 25–30 g/L and ≤25 g/L for high, moderate, and low, respectively). Results Among 257 patients, birth weight was 2003±348 g, gestational age was 35.7±2.3 weeks, and 59.1% were male. In addition, 127 (49.4%) were in the low albumin group. There were 32 patients with sepsis, 190 with infections, and 35 without infection, and their rates of hypoalbuminemia were 86.0%, 50.5%, and 30.7%, respectively (P<0.05). Albumin levels of the patients who survived were higher than those of the patients who died. In the low albumin group, the number of individual-event-critical NCIS cases and the frequency of multiple organs injuries were 63.8% and 28.3%, respectively, and were higher than in the 2 other groups. Mortality was higher in patients with sepsis. Hypoalbuminemia was associated with severe adverse outcomes (odds ratio=6.3, 95% confidence interval: 3.7–10.9, P<0.001). Conclusions Hypoalbuminemia was frequent among neonates with sepsis. Lower albumin levels might be associated with a poorer prognosis. Albumin levels could be appropriate for the diagnosis and prognosis of late preterm neonates with infections.
Towards an explanatory-combinatorial dictionary of Japanese

The Meaning-Text Theory is a formal, dependency-based linguistic theory in which the lexicon plays a central role. Within this theory, the vocabulary of a language is described by an Explanatory-Combinatorial Dictionary [ECD]. Unlike most existing dictionaries, an ECD is oriented towards language generation rather than analysis. This orientation makes it especially useful for language learners or computers that need to produce nativequality (i.e. highly idiomatic) texts. An ECD of a language aims at describing exhaustively all three components of its lexical units: their meaning, their form and their combinatorics. While it is common for current dictionaries to describe words’ meaning and form, their combinatorics is often left undescribed, so that the user has no information on how to actually use them in a sentence. An ECD, on the contrary, describes not only the syntactic and morphological behavior of words, but also their restricted lexical cooccurrence. The description of the restricted lexical cooccurrence of words constitutes the heart of an ECD. It is done using a formal language known as lexical functions. Lexical functions offer an elegant and powerful way of encoding common semantico-syntactic patterns of lexical cooccurrence. Until now, most of the work in explanatory-combinatorial lexicography has been done on French and Russian. The main goal of this paper is to show that it would be possible and useful to develop an ECD of Japanese, and to discuss potential problems inherent to this task and propose some leads towards a solution.
Tackling the bottleneck of delay tables in 3D ultrasound imaging

3D ultrasound imaging is quickly becoming a reference technique for high-quality, accurate, expressive diagnostic medical imaging. Unfortunately, its computation requirements are huge and, today, demand expensive, power-hungry, bulky processing resources. A key bottleneck is the receive beamforming operation, which requires the application of many permutations of fine-grained delays among the digitized received echoes. To apply these delays in the digital domain, in principle large tables (billions of coefficients) are needed, and the access bandwidth to these tables can reach multiple TB/s, meaning that their storage both on-chip and off-chip is impractical. However, smarter implementations of the delay generation function, including forgoing the tables altogether, are possible. In this paper we explore efficient strategies to compute the delay function that controls the reconstruction of the image, and present a feasibility analysis for an FPGA platform.
The Impact of Social Media Use Motives on Psychological and Sociocultural Adjustment of International Students

Social media exerts tremendous influence especially on the young generation. Previous research has investigated the important function social media plays in the process of intercultural adjustment. Unfortunately, little attention has been paid to the impact of social media use motivation. While numerous studies of media use motivation centers around mass media, very few examine the impact of social media use motives on intercultural adjustment. To extend this line of research, this study investigates the relationship between social media use motives and intercultural adjustment of international students in the United States. Findings from t-test, correlation, and regression analyses are reported. Directions for future research are also discussed.
User-specific Score Normalization and Fusion for Biometric Person Recognition

Every person is unique. This uniqueness is not only prevalent in his/her biometric traits, but also in the way he/she interacts with a biometric device. A recent trend in tailoring a biometric system to each user (client) is by normalizing the match score for each claimed identity. This technique is called user(or client-) specific score normalization. This concept can naturally be extended to the multimodal biometrics where several biometric devices and/or traits are involved. This chapter gives a survey on user-specific score normalization as well as compares several representative techniques in this research direction. It also shows how this technique can be used for designing an effective user-specific fusion classifier. The advantage of this approach, compared to the direct design of such a fusion classifier, is that much less genuine data is needed. Several potential research directions are also outlined.
Investigation on Multi-Grain Parallelism in Chip Multiprocessor for Multimedia Application

with the advent of chip multiprocessor (CMP) architecture, programmer must tune the program to the architecture in order to fully utilize the hardware resource. How to parallel program multimedia application in the CMP is a big obstacle. In this paper, we introduce the potential parallelism in the multimedia application and the multi-grain parallelism architecture in the CMP; also we make a systematic analysis on how to exploit multi-grain parallelism to accelerate the multimedia application computing. A method based on the multi- grain parallelism in the CMP is proposed to instruct the programmer to effectively layout the multimedia-related program. The method exploits the parallelisms from data-level, instruction-level, thread-level, and memory-transfer level. The Experimental results indicate that the method is practical and effective.
Many Sources Asymptotics for a Feedforward Network with Small Buffers ∗

In this paper we obtain the logarithmic asymptotics in a network of small buffers when the resources are accessed by a large number of stationary independent sources. Under the assumption that the network is feedforward with respect to source-destination pairs we identify the precise large deviations rate functions at each node in terms of the external input characteristics. Using these results we then show that the asymptotic admissible region when each type of source requires the same QoS in terms of loss is the same as that which is obtained by assuming that flows pass through each node unchanged.