Miracle-dz/UniFAR-FTUs huggingface dataset:
WWN-9: Cross-domain synaptic maintenance and its application to object groups recognition

Where What Network 6 (WWN-6) has shown that its model of synaptic maintenance using neural transmitters acetylcholine (ACh) and norepinephrine (NE) enables each neuron to distinguish between neuronal input lines from its relatively stable object patch and those from irrelevant backgrounds. However, it is about only a single domain - sensory domain X. During development from conception through fetus and newborn, every brain neuron has three major domains of input, sensory X, lateral Y and motor Z. The single-domain model of WWN-6 is not directly applicable to multiple domains because different domains have very different dimension and signal variations that cannot be directly compared. We believe that cross-domain synaptic maintenance is a crucial mechanism to develop a shallow-and-deep processing hierarchy in the brain where each neuron autonomously select domains in the developing hierarchy, not necessarily directly connected to receptors in X and muscles in Z. In the new work here, we propose a biologically inspired model for cross-domain synaptic maintenance. We assume that the earlier connection guided by morphogen result in initial coarse connection, but cross-domain synaptic maintenance refine connections to enable each neuron to autonomously find its role. As concept patterns emerge in Z, neurons refine their connections, to differentiate their roles among sensory processing, motor processing, and a mixture of both. Experimentally, we show the effect of the new theory through learning of individual objects and object groups, where neurons initialized for object-group connections tend to find their receptor inputs from X are not as stable as inputs from motor Z, thus, gradually turn into "later" processing neurons - for "higher-level" object-based features and their invariances. In principle, WWN-9 tends to learn a new object group without repeating the learning of all instances of each individual object.
Early Warning of City-Scale Unusual Social Event on Public Transportation Smartcard Data

A sudden social crowd event is serious to public safety as it usually triggers huge number of people who are overwhelming to existing public facilities. Detection, early warning of such social crowd events is very important to the city administration but a very challenging problem in research. In this paper, we aim to solve this problem by using the non-sensitive data from public transportation smartcard. We make a detailed analysis of the traffic of people from smartcard data,, we find a 'two-peak' pattern of human flow before, after a social crowd event happening. Motivated by this finding, we propose a framework for early detection of unusual social crowd events by exploiting time series analysis, machine learning technology. We evaluate our model on the real world public transportation data of the biggest metropolitan of China, validate our model with social crowd event data retrieved from the Internet. The evaluation result shows the effectiveness of our model.
Not all signals are created equal : Dynamic objective auto-encoder for multivariate data

There is a representational capacity limit in a neural network defined by the number of hidden units. For multimodal time-series data, there could exist signals with various complexity and redundancy. One way of getting a higher representational capacity for such input data is to increase the number of units in the hidden layer. We propose a step towards dynamically change the number of units in the visible layer so that there is less focus on signals that are difficult to reconstruct and more focus on signals that are easier to reconstruct with the goal to improve classification accuracy and also better understand the data itself. A comparison with state-of-the-art architectures show that our model achieves a slightly better classification accuracy on the task of classifying various styles of human motion.
Comparative Study of Image Registration Methods

The main objective of image registration is to match two or more images captured at different times by different sensors or by different angles or from different viewpoints. Image registration has become a crucial step in most of the image processing tasks used in various areas. It is a key technology which is applied for computer vision, remote sensing, image processing, medical image analysis and other fields. Medical image registration is used to find a spatial transformation to match all the anatomical points and diagnostic points on the image. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transform modeling, and finally image resampling. The accuracy of a registration process is highly dependent to the feature extraction and matching methods. Cross Correlation is the basic statistical approach to image registration. It is used for template matching or pattern recognition. Template is considered as a sub-image from the reference image, and the image is considered as a sensed image. The objective is to establish the correspondence between the reference image and sensed image. It gives the measure of the degree of similarity between an image and template, which can be used for image registration. Normalized Cross Correlation (NCC) method is improved by using Feature Based Method. Image are effectively represented by any of its feature such as edges, points, curves etc. and these features are effectively used for image registration. Images are applied with the filters to extract edges. Post that NCC is done to find the sharp point on NCC plot. This method restricts us with only monomodal images. For multimodal images we have used Mutual Information as a measure of similarity. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. Mutual Information is effectively used as similarity measure between two images which can be monomodal or multimodal. Mutual information is consider as a measure of how well one image explains the other; it is maximized at the optimal alignment. To make this more effective Contourlet transform is used. Contourlet is a recent development of transform theory as an improvement of wavelets. It is a multiscale and multidirectional, two dimensional transform. It is a combination of Laplacian pyramid and directional filter bank. The discrete contourlet transform has a fast iterated filter bank algorithm that requires order N operations for N-pixel images. the contourlet transform effectively captures smooth contours that are the dominant feature in natural images. This leads us to more efficient image registration.
Transitive node similarity for link prediction in social networks with positive and negative links

Online social networks (OSNs) like Facebook, and Myspace recommend new friends to registered users based on local features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features. We also exploit global graph features introducing transitive node similarity. Moreover, we derive variants of our method that apply in signed networks. We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. We show that a significant accuracy improvement can be gained by using information about both positive and negative edges.
Detecting Corner Using Fuzzy Similarity Matrix

In real-time video stream application, a feature point detector is very necessary. Stable feature points are useful in machine vision application. Feature point detectors such as SIFT, Harris and SUSAN are good methods which extracted high quality features, but neither the Harris detector nor the detection stage of SIFT can operate at real-time frame rate. In this paper, we present a new corner detection algorithm which uses fuzzy similarity matrix to calculate average similarity of contiguous pixels. It can be used in real-time applications of any complexity. In our experiments to evaluate our corner detector, difference image transformations are computed and the repeatability of feature points between a reference image and each of the transformed images are computed. Experiment results show our corner detector could give good results.
The Fungal Pathogen Moniliophthora perniciosa Has Genes Similar to Plant PR-1 That Are Highly Expressed during Its Interaction with Cacao

The widespread SCP/TAPS superfamily (SCP/Tpx-1/Ag5/PR-1/Sc7) has multiple biological functions, including roles in the immune response of plants and animals, development of male reproductive tract in mammals, venom activity in insects and reptiles and host invasion by parasitic worms. Plant Pathogenesis Related 1 (PR-1) proteins belong to this superfamily and have been characterized as markers of induced defense against pathogens. This work presents the characterization of eleven genes homologous to plant PR-1 genes, designated as MpPR-1, which were identified in the genome of Moniliophthora perniciosa, a basidiomycete fungus responsible for causing the devastating witches' broom disease in cacao. We describe gene structure, protein alignment and modeling analyses of the MpPR-1 family. Additionally, the expression profiles of MpPR-1 genes were assessed by qPCR in different stages throughout the fungal life cycle. A specific expression pattern was verified for each member of the MpPR-1 family in the conditions analyzed. Interestingly, some of them were highly and specifically expressed during the interaction of the fungus with cacao, suggesting a role for the MpPR-1 proteins in the infective process of this pathogen. Hypothetical functions assigned to members of the MpPR-1 family include neutralization of plant defenses, antimicrobial activity to avoid competitors and fruiting body physiology. This study provides strong evidence on the importance of PR-1-like genes for fungal virulence on plants.
Finite–dimensional Markovian realizations for stochastic volatility forward–rate models

We consider forward–rate models of Heath–Jarrow–Morton type, as well as more general infinite–dimensional stochastic differential equations, where the volatility–diffusion term is stochastic in the sense of being driven by a separate hidden Markov process. Within this framework, we use the previously developed Hilbert–space–realization theory in order to provide general necessary and sufficient conditions for the existence of a finite–dimensional Markovian realization for the stochastic volatility models. We illustrate the theory by analysing a number of concrete examples.
A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons

Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta‐analysis, comparing only two treatments), and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast‐based and arm‐based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., missing completely at random, missing at random, and missing not at random) providing evidence that our models outperform existing models in terms of bias, mean squared error, and coverage probability then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development. Copyright © 2015 John Wiley & Sons, Ltd.
Fuzzy Algorithms for Maximum Lifetime Routing in Wireless Sensor Networks

We address the maximum lifetime routing problem in wireless sensor networks (WSNs) and propose two online routing algorithms based on fuzzy logic, namely fuzzy maximum lifetime algorithm and fuzzy multiobjective algorithm. The former attempts to maximize the WSN lifetime objective, whereas the latter strives to simultaneously optimize the lifetime as well as the energy consumption objectives. The distinguishing aspect of this work is the novel use of fuzzy membership functions and rules in the design of cost functions for the routing objectives considered in this work. A range of simulation results obtained under various network scenarios show that the proposed approach is superior to a number of other well-known online routing heuristics, both in terms of the obtained network lifetime as well as the average energy consumption.
Pairing Organizational and Individual Factors to Improve Employees’ Risk Responsiveness

Scholars and practitioners have established various mechanisms and processes that help safeguard employee health and safety. Extant literature emphasizes the importance of training workers to respond to workplace hazards but often overlooks the influence of organizational factors on employees’ safety behavior. This study surveyed employees at a U.S. oil refinery to assess the influence of both individual and organizational factors on risk responsiveness—behaviors such as having safety conversations at work, possessing self-efficacy, and being willing to respond appropriately to risks. Drawing from prior health communication models, this study develops an integrated model of risk responsiveness. Results suggest that a combination of individual factors and perceptions of organizational information-seeking processes are better predictors of (a) risk-information-seeking behaviors, (b) safety self-efficacy, and (c) risk knowledge than individual-level factors or organizational processes alone. Furthermore, prior near prior near misses or injuries at work had no impact on any of the outcome variables.
A reservoir of Moraxella catarrhalis in human pharyngeal lymphoid tissue.

BACKGROUND Early exposure of infants and long-term immunity suggest that colonization with Moraxella catarrhalis is more frequent than is determined by routine culture. We characterized a reservoir of M. catarrhalis in pharyngeal lymphoid tissue. METHODS Tissue from 40 patients (median age, 7.1 years) undergoing elective tonsillectomy and/or adenoidectomy was analyzed for the presence of M. catarrhalis by culture, real-time DNA and RNA polymerase chain reaction (PCR), immunohistochemical analysis (IHC), and fluorescent in situ hybridization (FISH). Histologic sections were double stained for M. catarrhalis and immune cell markers, to characterize the tissue distribution of the organism. Intracellular bacteria were identified using confocal laser scanning microscopy (CLSM). RESULTS Twenty-nine (91%) of 32 adenoids and 17 (85%) of 20 tonsils were colonized with M. catarrhalis. Detection rates for culture, DNA PCR, RNA PCR, IHC, and FISH were 7 (13%) of 52, 10 (19%) of 52, 21 (41%) of 51, 30 (61%) of 49, and 42 (88%) of 48, respectively (P<.001). Histologic analysis identified M. catarrhalis in crypts, intraepithelially, subepithelially, and (using CLSM) intracellularly. M. catarrhalis colocalized with macrophages and B cells in lymphoid follicles. CONCLUSIONS Colonization by M. catarrhalis is more frequent than is determined by surface culture, because the organism resides both within and beneath the epithelium and invades host cells.
Religion, spirituality, coping, and resilience among African Americans with diabetes

ABSTRACT Study aims were to explore how religion and spirituality impacted attitudes about self-management practices among African Americans with homelessness histories and to understand resilience in diabetes care practices. Qualitative semistructured face-to-face interviews were conducted with 42 African Americans older than 18 years. All audio-taped interviews lasted between 1–1.5 hr, transcribed verbatim, and analyzed using ATLAS.ti (version 7.0). Five resilience themes emerged. While participants recognized diabetes as an illness requiring professional treatment, the context of balancing treatment with religion and spiritual practices mattered. The study findings highlight the importance of spirituality, religious beliefs, and coping strategies in diabetes self-care activities.
On the Fidelity of Single-Machine Network Emulation in Linux

Network emulation strikes the balance between using real machines on full-fledged networks and running software models of applications and networks in simulation environments. Advanced Linux features make it possible to emulate entire networks on a single machine, enabling experiments that are much easier to run and repeat. However, some of these features were not designed with the primary purpose of supporting emulation frameworks: if not carefully configured, they can lead to erroneous results. In this paper we note a few properties of Linux-based network emulation that researchers should consider while planning their experiments. We also recommend a particular configuration that improves the fidelity of emulation results.
Urban population and amenities

We use a frictionless neoclassical general-equilibrium model to explain cross-metro variation in population density based on three broad amenity types: quality of life, productivity in tradables, and productivity in non-tradables. Analytically, we demonstrate the dependence of quantities on amenities through substitution possibilities in consumption and production. Our calibrated model predicts large elasticities, consistent with variation in U.S. data, and estimates of local labor supply and demand. From only differences in wages and housing costs, we explain half of the variation in density, especially through quality of life amenities. We also show that density information can provide or refine measures of land value and local productivity. Our approach can be used to study a wide variety of urban quantities.
Spatial histogram equalization of complex-valued acoustic spectra in modulation domain for noise-robust speech recognition

This paper proposes to enhance the complex-valued acoustic spectrograms of speech signals via the technique of histogram equalization (HEQ) to produce noise-robust features for recognition. The presented method extends our previous work in the task of spectrogram enhancement and has two significant aspects. First, we process the real and imaginary parts of acoustic spectrograms separately, and therefore both of the corresponding magnitude and phase components can be enhanced implicitly. Second, we apply FIR filters to the intra-frame acoustic spectra to acquire the respective local structural statistics, which are subsequently employed to perform various types of HEQ on the acoustic spectrograms for robustifying the resulting speech features. All experiments were carried out on the Aurora-2 database and task. The performance of the presented methods was thoroughly tested and verified by comparisons with other well-known robustness methods, which reveals the capability of our methods in promoting the noise robustness of speech features.
A double dissociation between sensorimotor impairments and reading disability: A comparison of autistic and dyslexic children

Does sensorimotor dysfunction underlie reading impairment? To investigate this question, a battery of literacy, phonology, auditory, visual, and motor tests were administered to age- and ability-matched groups of dyslexic, autistic, and control children. As in previous studies, only a subset of the dyslexic children had sensory and/or motor impairments, whilst some dyslexics were entirely spared, suggesting that sensorimotor impairments are not necessary to cause reading disability. A subset of autistic children was also found to have sensorimotor impairments; however, some of these children did not have reading problems, suggesting that sensorimotor impairments are not sufficient to cause reading disability. We conclude that sensorimotor and reading impairments are doubly dissociable. Sensorimotor impairments do not seem to be the cause of reading disability, but can be seen as nonspecific markers for developmental disorder.
Contextuality and Beyond: Investigating an Online Diary Corpus

Heylighen and Dewaele’s (2002) F-score, a measure of formality developed based on categorical frequencies of word types, is used as a starting point for an investigation of an online diary corpus. Comparisons are made between results in the main corpus of diary entries, a smaller corpus of diary comments, and with previously calculated F-scores for similar types of data (Nowson, Oberlander & Gill, 2005). While the overall F-score is similar in these two corpora, results show that internal make-up of the categories upon which the calculation is based can differ. This suggests that while the F-score is a good measure of formality/contextuality and is useful in distinguishing between genres on a large scale, more detailed analyses are required to more completely describe and situate genres with respect to one another.
Broadcast scheduling for information distribution

Broadcast data delivery is encountered in many applications where there is a need to disseminate information to a large user community in a wireless asymmetric communication environment. In this paper, we consider the problem of scheduling the data broadcast such that average response time experienced by the users is low. In a push-based system, where the users cannot place requests directly to the server and the broadcast schedule should be determined based solely on the access probabilities, we formulate a deterministic dynamic optimization problem, the solution of which provides the optimal broadcast schedule. Properties of the optimal solution are obtained and then we propose a suboptimal dynamic policy which achieves average response time close to the lower bound. The policy has low complexity, it is adaptive to changing access statistics, and is easily generalizable to multiple broadcast channels. In a pull-based system where the users may place requests about information items directly to the server, the scheduling can be based on the number of pending requests for each item. Suboptimal policies with good performance are obtained in this case as well. Finally, it is demonstrated by a numerical study that as the request generation rate increases, the achievable performance of the pull- and push-based systems becomes almost identical.
A Necessary and Sufficient Condition for Stability of Arbitrarily Switched Second-Order LTI System: Marginally Stable Case

In this paper, the stability analysis for a class of second order switched LTI systems with possible marginally stable subsystems is investigated. The main contribution here is the derivation of a necessary and sufficient condition for stability of such a switched system under arbitrary switching. The condition can be easily checked which is illustrated by an example.