Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Analysis of Neuronal MicroRNAs in Caenorhabditis elegans Dauer Formation by Combinational Genetics and Neuronal miRISC Immunoprecipitation

Identifying the physiological functions of microRNAs (miRNAs) is often challenging because miRNAs commonly impact gene expression under specific physiological conditions through complex miRNA::mRNA interaction networks and in coordination with other means of gene regulation, such as transcriptional regulation and protein degradation. Such complexity creates difficulties in dissecting miRNA functions through traditional genetic methods using individual miRNA mutations. To investigate the physiological functions of miRNAs in neurons, we combined a genetic “enhancer” approach complemented by biochemical analysis of neuronal miRNA-induced silencing complexes (miRISCs) in C. elegans. Total miRNA function can be compromised by mutating one of the two GW182 proteins (AIN-1), an important component of miRISC. We found that combining an ain-1 mutation with a mutation in unc-3, a neuronal transcription factor, resulted in an inappropriate entrance into the stress-induced, alternative larval stage known as dauer, indicating a role of miRNAs in preventing aberrant dauer formation. Analysis of this genetic interaction suggests that neuronal miRNAs perform such a role partly by regulating endogenous cyclic guanosine monophosphate (cGMP) signaling, potentially influencing two other dauer-regulating pathways. Through tissue-specific immunoprecipitations of miRISC, we identified miRNAs and their likely target mRNAs within neuronal tissue. We verified the biological relevance of several of these miRNAs and found that many miRNAs likely regulate dauer formation through multiple dauer-related targets. Further analysis of target mRNAs suggests potential miRNA involvement in various neuronal processes, but the importance of these miRNA::mRNA interactions remains unclear. Finally, we found that neuronal genes may be more highly regulated by miRNAs than intestinal genes. Overall, our study identifies miRNAs and their targets, and a physiological function of these miRNAs in neurons. It also suggests that compromising other aspects of gene expression, along with miRISC, can be an effective approach to reveal miRNA functions in specific tissues under specific physiological conditions.
Map based road boundary estimation

Knowledge about the road shape is a key element for driver assistance systems which support the driver in complex scenarios like construction sites. Systems only using information derived from lane markings reach a limit here. The paper presents an approach to estimate road boundaries based on static objects bounding the road. A map based environment description and an interpretation algorithm identifying the road boundaries in the map are used. Two approaches are presented for estimating the map, one based on a radar sensor, one on a mono video camera. Besides that two fusion approaches are described. The estimated boundaries are independent of road markings and as such can be used as orthogonal information with respect to detected markings. Results of practical tests using the estimated road boundaries for a lane keeping system are presented.
Utilizing auditory masking in automatic speech recognition

A speech recognition system based on the psychoa-coustics of the masking property of the of human auditory system is proposed. The method utilizes several psychoacoustic properties of human perception to define perceptual speech excitation function (masking threshold) and perceptual noise. Based on the auditory masking threshold, a time-frequency noise spectral subtraction is implemented. For a human listener, the noise below the masking threshold is inaudible, and the objective is to minimize only the noise spectrum above the masking threshold. Additionally, we show that, for ASR applications, further improvements in recognition performance may be obtained by augmenting the masking of the noise by spectral subtraction in the masked region also. The strategy is to remove the masked noise from the ASR system, similar to the masking effect in the human auditory system. Based on the AMT, and the estimated perceptual noise, we have implemented two spectral subtraction algorithms: a straight-forward scheme of subtracting the total estimated perceptual noise from the noisy speech spectrum, and a spectral subtraction of the noise which lies below the masking threshold. It was observed that, both methods give significant improvements over the base PLP performance, with the latter method giving better recognition results.
An improved "soft" eFPGA design and implementation strategy

A recently proposed "soft" eFPGA methodology was used to create small amounts of programmable logic using the ASIC flow, but it incurs significant overhead. In this paper, it is shown that architecture-specific tactical standard cells can reduce the area and delay overhead of the previous approach by 58% and 40% respectively. It is also shown that by imposing a structured design and layout approach, the logic capacity and quality of standard-cell-based eFPGAs can be significantly improved. Finally, it is shown that our improved ASIC flow approach can create layouts that are competitive with another approach called GILES that uses custom FPGA CAD tools and nonstandard cells for tile layout purposes.
Cloud-based Predictive Modeling System and its Application to Asthma Readmission Prediction

The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) data in restricted computational environments. To address this problem, we implemented a cloud-based predictive modeling system via a hybrid setup combining a secure private server with the Amazon Web Services (AWS) Elastic MapReduce platform. EHR data is preprocessed on a private server and the resulting de-identified event sequences are hosted on AWS. Based on user-specified modeling configurations, an on-demand web service launches a cluster of Elastic Compute 2 (EC2) instances on AWS to perform feature selection and classification algorithms in a distributed fashion. Afterwards, the secure private server aggregates results and displays them via interactive visualization. We tested the system on a pediatric asthma readmission task on a de-identified EHR dataset of 2,967 patients. We conduct a larger scale experiment on the CMS Linkable 2008-2010 Medicare Data Entrepreneurs' Synthetic Public Use File dataset of 2 million patients, which achieves over 25-fold speedup compared to sequential execution.
Time Changed Markov Processes in Unified Credit-Equity Modeling

This paper develops a novel class of hybrid credit-equity models with state-dependent jumps, local-stochastic volatility and default intensity based on time changes of Markov processes with killing. We model the defaultable stock price process as a time changed Markov diffusion process with state-dependent local volatility and killing rate (default intensity). When the time change is a Lévy subordinator, the stock price process exhibits jumps with state-dependent Lévy measure. When the time change is a time integral of an activity rate process, the stock price process has local-stochastic volatility and default intensity. When the time change process is a Lévy subordinator in turn time changed with a time integral of an activity rate process, the stock price process has state-dependent jumps, local-stochastic volatility and default intensity. We develop two analytical approaches to the pricing of credit and equity derivatives in this class of models. The two approaches are based on the Laplace transform inversion and the spectral expansion approach, respectively. If the resolvent (the Laplace transform of the transition semigroup) of the Markov process and the Laplace transform of the time change are both available in closed form, the expectation operator of the time changed process is expressed in closed form as a single integral in the complex plane. If the payoff is square-integrable, the complex integral is further reduced to a spectral expansion. To illustrate our general framework, we time change the jump-to-default extended CEV model (JDCEV) of Carr and Linetsky (2006) and obtain a rich class of analytically tractable models with jumps, local-stochastic volatility and default intensity. These models can be used to jointly price and hedge equity and credit derivatives.
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.
18 Inflectional morphology and related matters (

The first large-scale modern grammars of English were Quirk et al.’s A grammar of contemporary English (1972) and A comprehensive grammar of the English language (1985). It has taken 18 years for a major competitor to be published. Many linguists, especially those whose main focus is English, will have looked forward to the publication of the present book. The Cambridge grammar of the English language (henceforth CaGEL) is first and foremost the brainchild of Rodney Huddleston, whose 1984 Introduction to the grammar of English had already established itself as an important text. He was joined by Geoffrey Pullum and the other authors listed above at various points in time. CaGEL is an awe-inspiring tome which offers a comprehensive descriptive account of the grammar of English. It is based on recent descriptive and
Equity and transport policy reform 1 by

The paper assesses the marginal welfare and equity impacts of three transport instruments for a second-best economy in which the government has to use distortionary taxes for revenue-raising and distributional purposes. The assessment uses an applied general equilibrium model for Belgium. The model includes three types of external costs of transport: congestion, air pollution and accidents. The transport instruments are: peak road pricing, the fuel tax and subsidies to public transport. They are introduced in a revenue-neutral way with the labour income tax, the lump sum social security transfers and other transport instruments serving as revenue-preserving instruments. It is shown that the equity effects of the transport instruments depend to a large extent on how revenue-neutrality is ensured. The political acceptability of transport policy reforms can therefore be enhanced by a careful design of the revenue-preserving strategies. Moreover, it is argued that distributional considerations cannot be ignored in the double dividend discussion.
Detection of Salmonella spp in commercial eggs in Iran

Background and Objective: Salmonellosis can be acquired through consumption of infected raw or undercooked eggs. The aim of this study was isolation and identification of Salmonella spp from the eggshells and the egg contents samples of Tabriz retails. Methods: A total number of 150 samples of eggs were analyzed for the presence of Salmonella spp. using conventional culture method and multiplex-PCR. Results: Two (1.33%) out of 150 samples from eggshells were determined as contaminated with Salmonella spp. Salmonella spp was not isolated from the egg contents. Salmonella serovar was determined as enteritidis and typhimurium. Conclusion: The results of the present study provide the recent dataset of the prevalence of S. enteritidis and S. typhimurium in eggs at retail shops in the northwest of Iran. It is important to remember that control is required at all levels in the food chain and by separating cooked and raw.
A new road friction coefficient estimation method based on SVM

In this paper, a new road friction coefficient estimation method based on Support Vector Machines (SVM) was proposed in the process of steering driving under different road conditions. First a 2 ideal DOF vehicle model was built in Matlab/simulink to analyzing the wheel cornering characteristics. And then an algorithm based on SVM was performed to estimate difference road adhesion coefficient. Simulation results showed that the algorithm can effectively identify the friction coefficient, which had a significant meaning for controlling the stability of vehicle in process of over steering and under steering.
Visualizing Semantic Table Annotations with TableMiner+

This paper describes an extension of the TableMiner+ sys- tem, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and e�fficient approach. It adds a graphical user interface to TableMiner+, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner+ an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web.
A Hybrid Speech Emotion Perception Method of VQ-based Feature Processing and ANN Recognition

This paper constructs a VQ/ANN (vector quantization/artificial neural network) based speech emotion recognition system. The system first extracts the basic prosodic parameters and Mel-frequency cepstral coefficients (MFCC) frame by frame. Recent researches reveal that MFCC convey detailed emotional relevant information of syllable. However, the statistic measures of MFCC confuse the information at sentence level. Therefore, this paper proposes a VQ-based method different to statistic method to generate measures of MFCC. Then the combination of VQ-based MFCC measures and the statistic measures of prosodic parameters is used as input feature vector. The ANN is performed to process the combination features and the statistic measures of all extracted parameters respectively. The experiment results reveal that the combination features outperform the statistic measures. More detailed analysis indicates that the combination features could characterize the emotion space better than the statistic features. Besides, the rationality of VQ/ANN based framework is also demonstrated.
Stable cluster head selection in LEACH protocol: a cross-layer approach

Optimal cluster head count plays a key role in determining the efficiency of a cluster based routing protocol. If the cluster head count will be more than the optimal value, aggregation advantage cannot be taken fully. If clusters are less, then there will be more aggregation load on the cluster head and communication load on the cluster members as they have to communicate with cluster head over long distances. LEACH (Low Energy Clustering Adaptive Hierarchy) protocol, a popular clustering protocol, suffers from the cluster head variability problem. In this paper, we propose an approach which uses cross layer information to overcome this problem. NS-2 simulations show that improved LEACH increased the network lifetime i.e FND (First Node Death) time by 22%, HNA (Half Nodes Alive) by 24% and LND (Last Node Death) time by 12% respectively.
An improved web information summarization method using Sentence Similarity-Based Soft clustering

For the explosion of information in the World Wide Web, this paper proposed a new method of web news summarization via soft clustering algorithm. It used search engine to extract relevant documents, and mixed query sentence into sentences set which segmented from multi-document set, then this paper adopted efficient soft cluster algorithm SSSC (Sentence Similarity-Based Soft Clustering) to cluster all the sentences. Experimental result shows that the proposed summarization method can improve the performance of summary, soft clustering algorithm is efficient.
Learning Interpretable Models Using an Oracle

As Machine Learning (ML) becomes pervasive in various real world systems, the need for models to be understandable has increased. We focus on interpretability, noting that models often need to be constrained in size for them to be considered interpretable, e.g., a decision tree of depth 5 is easier to interpret than one of depth 50. But smaller models also tend to have high bias. This suggests a trade-off between interpretability and accuracy. We propose a model agnostic technique to minimize this trade-off. Our strategy is to first learn a powerful, possibly black-box, probabilistic model - referred to as the oracle - on the training data. Uncertainty in the oracle's predictions are used to learn a sampling distribution for the training data. The interpretable model is trained on a sample obtained using this distribution. We demonstrate that such a model often is significantly more accurate than one trained on the original data. Determining the sampling strategy is formulated as an optimization problem. Our solution to this problem possesses the following key favorable properties: (1) the number of optimization variables is independent of the dimensionality of the data: a fixed number of seven variables are used (2) our technique is model agnostic - in that both the interpretable model and the oracle may belong to arbitrary model families. Results using multiple real world datasets, using Linear Probability Models and Decision Trees as interpretable models, with Gradient Boosted Model and Random Forest as oracles, are presented. We observe significant relative improvements in the F1-score in most cases, occasionally seeing improvements greater than 100%. Additionally, we discuss an interesting application of our technique where a Gated Recurrent Unit network is used to improve the sequence classification accuracy of a Decision Tree that uses character n-grams as features.
Interaction System of Treadmill Games based on depth maps and CAM-Shift

Because of the non-contact and substantivity, recognition from images have become a hotspot in somatic technology. Especially, recognition with non-markers lets human body become remote controller without any accessories and attracts widespread attention. This paper chose CAM-Shift algorithm to track bare hands for its calculate speed and stability. This article introduced depth information to traditional CAM-Shift and developed and extended the algorithm in foreground segmentation, and auto-initialization. This paper mainly segmented depth map to get the mask off code of the foreground, and made use of the 3D information of the depth maps to initialize the searching window. Then, the paper built up Interaction System of Treadmill Games based on depth maps and CAM-Shift. The system realized the man-machine interaction with non-contact and no-markers by getting player's depth maps and color images with a attachment of Xbox — Kinect from Microsoft, 3D tracking the player's bare hands and analyzing and recognizing the movements of the hands. Then the experiments testified this system satisfies the real time request, and the reaction is acute and correct.
Experiences of vulnerability due to loss of support by aged parents of emigrated children: a hermeneutic literature review

ABSTRACT In disrupted families due to migration individual members support each other through transnational care. The care is often reciprocal as the members who are left behind support the members who emigrated and in return receive care from the emigrated family members. Aged parents who get left behind, however, often become vulnerable. The hermeneutic literature review shows that social, psychological or emotional and economic vulnerability are experienced. They have to deal with cultural challenges as their children form part of a new culture in their receiving country. Strong feelings of loss, helplessness and loneliness are experienced. The emigration of their children may also contribute to the financial vulnerability of the elderly.
Content, structure, and dynamics of personal reputation: The role of trust and status potential within social networks

In this paper we examined the content, structure, and dynamics of reputation, a person’s agreed-upon character that is constructed within social groups. In Study 1, we examined longitudinally the content and structure of an individual’s reputation as distributed across a newly forming group. In Study 2, we examined how the dynamics of reputation shape gossip, a form of reputational discourse. In keeping with theoretical claims about the function of reputation, trustworthiness and status potential proved to be central to reputation content that is shared across a social network and emerged over the course of a year (Study 1). Gossip, a form of reputational discourse, was found to focus upon individuals who are untrustworthy and of questionable and undeserved status (Study 2). We discuss how the results from these studies shed light on how reputation is essential to cooperation and cohesion within groups.
Baseline Profile Stability for Network Anomaly Detection

Network attacks are commonplace in the Internet. One of the defense mechanisms against the network attacks is using a baseline profile established during normal operation to detect the traffic that deviates from the baseline profile. However, this approach works only if there is a stable base profile representing the legitimate network traffic. Although there has been some preliminary research, the details of profiling, such as the profile format, its size and the traffic stability by site or time, have not been widely available. In this study, we analyze actual traffic traces from two Internet traffic archives and verify the traffic stability by various aspects. The analysis shows that there are significant differences in the traffic patterns among different sites. In addition, there are some differences between different time of day or different days, even within a site, suggesting that different profiles are needed for different times. The result of this study can be used practically to anomaly-based IDS for determining the stability of the traffic for a particular site, and the number of required traffic profiles based on the traffic patterns