Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-Scale Expression Analysis Reveals Distinct MicroRNA Profiles at Different Stages of Human Neurodevelopment

Background MicroRNAs (miRNAs) are short non-coding RNAs predicted to regulate one third of protein coding genes via mRNA targeting. In conjunction with key transcription factors, such as the repressor REST (RE1 silencing transcription factor), miRNAs play crucial roles in neurogenesis, which requires a highly orchestrated program of gene expression to ensure the appropriate development and function of diverse neural cell types. Whilst previous studies have highlighted select groups of miRNAs during neural development, there remains a need for amenable models in which miRNA expression and function can be analyzed over the duration of neurogenesis. Principal Findings We performed large-scale expression profiling of miRNAs in human NTera2/D1 (NT2) cells during retinoic acid (RA)-induced transition from progenitors to fully differentiated neural phenotypes. Our results revealed dynamic changes of miRNA patterns, resulting in distinct miRNA subsets that could be linked to specific neurodevelopmental stages. Moreover, the cell-type specific miRNA subsets were very similar in NT2-derived differentiated cells and human primary neurons and astrocytes. Further analysis identified miRNAs as putative regulators of REST, as well as candidate miRNAs targeted by REST. Finally, we confirmed the existence of two predicted miRNAs; pred-MIR191 and pred-MIR222 associated with SLAIN1 and FOXP2, respectively, and provided some evidence of their potential co-regulation. Conclusions In the present study, we demonstrate that regulation of miRNAs occurs in precise patterns indicative of their roles in cell fate commitment, progenitor expansion and differentiation into neurons and glia. Furthermore, the similarity between our NT2 system and primary human cells suggests their roles in molecular pathways critical for human in vivo neurogenesis.
Technical Report: Region and Effect Inference for Safe Parallelism

In this paper, we present the first full regionsand-effects inference algorithm for explicitly parallel fork-join programs. We infer annotations inspired by Deterministic Parallel Java (DPJ) for a type-safe subset of C++. We chose the DPJ annotations because they give the strongest safety guarantees of any existing concurrency-checking approach we know of, static or dynamic, and it is also the most expressive static checking system we know of that gives strong safety guarantees. This expressiveness, however, makes manual annotation difficult and tedious, which motivates the need for automatic inference, but it also makes the inference problem very challenging: the code may use region polymorphism, imperative updates with complex aliasing, arbitrary recursion, hierarchical region specifications, and wildcard elements to describe potentially infinite sets of regions. We express the inference as a constraint satisfaction problem and develop, implement, and evaluate an algorithm for solving it. The region and effect annotations inferred by the algorithm constitute a checkable proof of safe parallelism, and it can be recorded both for documentation and for fast and modular safety checking.
Optimization of Rules Selection for Robot Soccer Strategies

Mobile embedded systems belong among the typical applications of distributed systems control in real-time. An example of a mobile control system is a robotic system. The proposal and realization of such a distributed control system represents a demanding and complex task for real-time control. In the process of robot soccer game applications, extensive data is accumulated. The reduction of such data is a possible win in a game strategy. The main topic of this article is a description of an efficient method for rule selection from a strategy. The proposed algorithm is based on the geometric representation of rules. A described problem and a proposed solution can be applied to other areas dealing with effective searching of rules in structures that also represent coordinates of the real world. Because this construed strategy describes a real space and the stores physical coordinates of real objects, our method can be used in strategic planning in the real world where we know the geographical positions of objects.
A Quantum Genetic Algorithm for Operon Prediction

Operon is a fundamental unit of transcription which is usually used to understand gene regulations and functions in entire genomes. Detecting operon experimentally is difficult and time-consuming, thus many bioinformatics algorithms have been proposed to predict operon. In this paper, we use an improved discrete genetic algorithm based on quantum theory for operon prediction. It is simpler and more powerful than the algorithms available, and thus avoids local optima while searching for a better solution. We utilize intergenic distance, participation in the same metabolic pathway and cluster of orthologous groups (COG) gene functions to design fitness function base on reward and penalty (RP). The RP operation can improve fitness value of chromosome in proportion to the accuracy. Experimental results show that the detection accuracy of our method reached 0.872, 0.925, 0.943, 0.954 and 0.926 respectively for the E. coli, B. subtilis, P. aeruginosa PA01, S. aureus and M. tuberculosis genomes. Results demonstrate that our proposed method can predict operons with high accuracy.
A Probabilistic Description of Man-Machine Spoken Communication

Speech enabled interfaces and spoken dialog systems are mostly based on statistical speech and language processing modules. Their behavior is therefore not deterministic and hardly predictable. This makes the simulation and the optimization of such systems performances difficult, as well as the reuse of previous work to build new systems. In the aim of a partially automated optimization of such systems, this paper presents a formalism attempt for the description of man-machine spoken communication in the framework of spoken dialog systems. This formalization is partly based on a probabilistic description of the information processing occurring in each module composing a spoken dialog system but also on a stochastic user modeling. Eventually, some possible applications of this theoretic framework are proposed
Use of two-dimensional matched filters for estimating a length of blood vessels newly created in angiogenesis process

The complex algorithm for estimating a length of blood vessels’ newly created in angiogenesis process has been presented. A filtering method using two-dimensional matched filter is a fundamental process in the presented algorithm. For proper extraction of blood vessels’ network more image processing techniques has been used including spatial low-pass filtering, binarisation, skeletonisation, and, developed by the author, algorithms for cleaning vessel network obtained in this process and for removing vessels false detected in the previous steps. The presented algorithm can be used for fully automatic vessels’ length estimating in medical images and can be helpful for angiogenesis process’s research.
Location Of Microearthquakes Induced By Hydraulic Fracturing

This paper examines the problem of locating microearthquakes induced by hydraulic fracturing using seismic arrival time data. In addition to the use of absolute arrival times measured for individual events, we consider the use of differential arrival times amongst a set of two or more seismic events as a means of constralning their locations relative to one another. Differential arrival times can be measured very accurately using cross-correlation techniques and are less sensitive than absolute arrival times to subsurface velocity structure. We have developed an algorithm which combines relative event location techniques with conventional absolute location techniques and applied it to a set of 19 microearthquakes recorded during a hydraulic fracturing experiment conducted as part of the Los Alamos Hot Dry Rock project. We find that the events, except for a few outliers, delineate a planar zone 30 meters in dimension, presumably a fracture plane. This example shows that the use of differential arrival times improves the accuracy of locating microearthquake clusters and that the relative locations of events within the cluster are better determined than their absolute locations. The results also suggest the need for directional data from three-component stations or better station geometry to further improve location accuracy.
Parallel Remote Rendering of Large Point-based Models using Multicore Processors

In this paper, an efficient framework is proposed for the remote rendering of large point-based models represented by QSplat Level-Of-Detail (LOD). This framework renders the scenes of the 3D models on smart mobiles via the effective utilization of multicore server(s). The high-level requirements that guided the formulation of the proposed framework are (a) the problem domain is highly irregular, motivating the use of low-overhead dynamic load-balancing to effectively utilize the multicore processors and (b) the hidden delays encountered with multicore processors, e.g. to maintain cache consistency. The challenge is to conceive a design and implementation that meets both of these complex requirements. In this manner, various dynamic load balancing schemes were proposed and evaluated through the processing of several models with different sizes. In addition, the hidden delays encountered with the multicore processors were well studied. Experiments using the proposed framework exhibited remarkable efficiency of more than 85% for large and sophisticated models.
Tumor necrosis factor-alpha in normal and diseased brain: Conflicting effects via intraneuronal receptor crosstalk?

Tumor necrosis factor-alpha (TNF-α) is pleiotropic mediator of a diverse array of physiological and neurological functions, including both normal regulatory functions and immune responses to infectious agents. Its role in the nervous system is prominent but paradoxical. Studies on uninflamed or “normal” brain have generally attributed TNF-α a neuromodulatory effect. In contrast, in inflamed or diseased brain, the abundance of evidence suggests that TNF-α has an overall neurotoxic effect, which may be particularly pronounced for virally mediated neurological disease. Still others have found TNF-α to be protective under some conditions of neurological insult. It is still uncertain exactly how TNF-α is able to induce these opposing effects through receptor activation of only a limited set of cell signaling pathways. In this paper, we provide support from the literature to advance our hypothesis that one mechanism by which TNF-α can exert its paradoxical effects in the brain is via crosstalk with signaling pathways of growth factors or other cytokines.
ABOUT THE OVERLAP DISTRIBUTION IN MEAN FIELD SPIN GLASS MODELS

We continue our presentation of mathematically rigorous results about the Sherrington-Kirkpatrick mean field spin glass model. Here we establish some properties of the distribution of overlaps between real replicas. They are in full agreement with the Parisi accepted picture of spontaneous replica symmetry breaking. As a byproduct, we show that the self-averaging of the Edwards-Anderson fluctuating order parameter, with respect to the external quenched noise, implies that the overlap distribution is given by the Sherrington-Kirkpatrick replica symmetric Ansatz. This extends previous results of Pastur and Scherbina. Finally, we show how to generalize our results to realistic short range spin glass models.
Graph Grammar Based Web Data Extraction

Web data extraction becomes a hot topic after the invention of World Wide Web, because the large amount of information on the Web makes it challenging to retrieve useful information. Due to the diverse designs and presentations of information on different Web sites, it is hard to implement a general solution to extract data across different Web sites. This paper presents a novel method based on graph grammar to extract the same type of information from different Web sites without the need of training or adjustment. Our approach formalizes a common Web pattern as a graph grammar. Then, based on the visual layout and HTML DOM structure, a Web page is abstracted as a spatial graph that highlights the essential spatial relations between information objects. According to the defined graph grammar, a spatial parsing is performed on the spatial graph to extract structured records. We have evaluated our approach on twenty one different Web sites, and achieved the F1-score as 97.49% which shows promising performance.
Adaptive object tracking using bayesian network and memory

This paper presents an adaptive object tracking method that integrates the cues from color likelihood and edge likelihood, and that adapts itself to abrupt appearance changing objects. We use a Bayesian network based multi-modal fusion method of color and edge information. To handle the cases of sudden appearance changes, occlusion, disappearance and reappearance of tracked objects, a memory model is also introduced. The proposed tracker has the following characteristics. First, multiple modalities are integrated in the Bayesian network to evaluate the posterior of each feature. Secondly, context factors are computed in order to select best object state. Finally, a memory-based appearance model is introduced to handle abrupt appearance changes. Our method is robust and versatile for a modest computational cost. Desirable tracking results are obtained.
A Stable Estimator for the Information Matrix under EM

This article develops a new and stable estimator for information matrix when the EM algorithm is used in maximum likelihood estimation. This estimator is constructed using the smoothed individual completedata scores that are readily available from running the EM algorithm. In comparison to the approach of Louis (1982), this new estimator is more stable and easier to implement. Both real and simulated data are used to demonstrate the use of this new estimator.
NeuroHub: a research information environment for neuroscientists

The NeuroHub project aims to develop a research information system for neuroscientists at three different partner institutions: Oxford, Reading and Southampton. Each research group has different working practices, research methodologies and user requirements, which have lead to the development of a system that supports a wide variety of tasks in the neuroscience research life cycle. In this paper, we present how these user requirements have been translated in a research information environment that supports a community of over 70 researchers using the system for day-to-day research tasks.
Cluster-Based Language Model for Sentence Retrieval in Chinese Question Answering

Sentence retrieval plays a very important role in question answering system. In this paper, we present a novel cluster-based language model for sentence retrieval in Chinese question answering which is motivated in part by sentence clustering and language model. Sentence clustering is used to group sentences into clusters. Language model is used to properly represent sentences, which is combined with sentences model, cluster/topic model and collection model. For sentence clustering, we propose two approaches that are OneSentence-Multi-Topics and OneSentence-One-Topic respectively. From the experimental results on 807 Chinese testing questions, we can conclude that the proposed cluster-based language model outperforms over the standard language model for sentence retrieval in Chinese question answering.
Decidability of the interval temporal logic AA*BB* over the rationals

The classification of the fragments of Halpern and Shoham's logic with respect to decidability/undecidability of the satisfiability problem is now very close to the end. We settle one of the few remaining questions concerning the fragment AA*BB*, which comprises Allen's interval relations "meets" and "begins" and their symmetric versions. We already proved that AA*BB* B is decidable over the class of all finite linear orders and undecidable over ordered domains isomorphic to N. In this paper, we first show that AA*BB* is undecidable over R and over the class of all Dedekind-complete linear orders. We then prove that the logic is decidable over Q and over the class of all linear orders.
New Faint Optical Spectrophotometric Standards. Hot White Dwarfs from the Sloan Digital Sky Survey

The spectral energy distributions for pure-hydrogen (DA) hot white dwarfs can be accurately predicted by model atmospheres. This makes it possible to define spectrophotometric calibrators by scaling the theoretical spectral shapes with broad-band photometric observations – a strategy successfully exploited for the spectrographs onboard the Hubble Space Telescope (HST) using three primary DA standards. Absolute fluxes for non-DA secondary standards, introduced to increase the density of calibrators in the sky, need to be referred to the primary standards, but a far better solution would be to employ a network of DA stars scattered throughout the sky. We search for blue objects in the sixth data release of the Sloan Digital Sky Survey (SDSS) and fit DA model fluxes to identify suitable candidates. Reddening needs to be considered in the analysis of many of these stars. We propose a list of nine pure-hydrogen white dwarfs with absolute fluxes with estimated uncertainties below 3 per cent, including four objects with estimated errors <2 per cent, as candidates for spectrophotometric standards in the range 14 < g < 18, and provide model-based fluxes scaled to match the SDSS broad-band fluxes for each. We apply the same method to the three HST DA standards, linking the zero point of their absolute fluxes to ugr magnitudes transformed from photometry obtained with the US Naval Observatory 1-m telescope. For these stars, we estimate uncertainties of <1 per cent in the optical, finding good consistency with the fluxes adopted for HST calibration.
XML representation languages as a way of interconnecting TTS modules

The present paper reports on a novel way of increasing the modularity and pluggability of text-to-speech (TTS) architectures. In a proof-of-concept study, two current TTS systems, both using XML-based languages for internal data representation, are plugged together using XSLT transforms as a means of translating from one system’s internal representation to the other’s. This method allows one system to use modules from the other system. The potential and the limitations of the approach are discussed.
Ontology based Web Page Topic Identification

the emergence of the web, lots of research efforts are made in the area of Web Mining. This paper proposes an automatic approach for automatic topic identification from the web pages. The contribution of this research is in the approach of automatic topic identification of web pages that can provide better results. The topic of the web documents is identified through ontological approach. Keywords are extracted from the basic HTML tags and co-occurrence of words in the text instead of calculating the frequency of each term exits in a web page. Domain ontology is developed to map topics of the documents. Keywords are mapped to the ontology with a Levenshtein Edit Distance to extract topic of the web page. The result could give benefit to the search engines for faster tagging of web pages.
The elicitation of time preferences

We compare three methods for the elicitation of time preferences in an experimental setting: the Becker-DeGroot-Marschak (BDM) procedure (BDM), a second price auction and the multiple price list format. The first two methods have been used rarely to elicit time preferences. Although all methods used are broadly strategically equivalent, and should induce the same 'truthful' revelation, we find that the methods do differ: the money discount rates elicited with the multiple price list tend to be higher than those elicited with the other two methods. Furthermore, there are no significant differences between the rates elicited with the BDM and the auction elicitation procedure.
Compilers and Interpreters

Most programs a written in high-level programming languages that are not directly implemented by any real hardware machine. So it is not possible to simply dump the source code onto a computer and expect it to run. There are two basic approaches to bridge the gap between high-level programming languages and machine code: compilation and interpretation. Traditional Compilation. With compilation, some source program P written in some source language L is translated into an equivalent program P in some target language L. The program that does this translation is called a compiler. Typically, the source language L is some high-level programming language (e.g., C), and the target language L is the machine code of some hardware machine. The output program P would then be an executable that can run directly on the hardware. In this way, P is both the output of the compiler program and a program itself. After compilation P can be executed (independently of the compiler) to process input and produce output: Machine Code Program (P') Source Program (P) Input Output Traditional Compiler Interpretation. An alternative approach to program execution is interpretation. An interpreter is a program that executes another program P written in some programming language L by simulating L and directly performing the tasks required by the statements in P while P is running. With an interpreter, there is no independent executable output P. The interpreter itself is the only program that runs directly on L. It knows how to simulate the execution each kind of statement in L, and so it can be used to perform the tasks for each of the statements in P as they would be performed on L.
Classification Models for Neurocognitive Impairment in HIV Infection Based on Demographic and Clinical Variables

Objective We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection. Methods The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to obtain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients. Results The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes). Conclusion Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.