Miracle-dz/UniFAR-FTUs huggingface dataset:
Intermedin1–53 Protects Against Myocardial Fibrosis by Inhibiting Endoplasmic Reticulum Stress and Inflammation Induced by Homocysteine in Apolipoprotein E-Deficient Mice

Aim: Endoplasmic reticulum stress (ERS) and inflammation participate in cardiac fibrosis. Importantly, a novel paracrine/autocrine peptide intermedin1–53 (IMD1–53) in the heart inhibits myocardial fibrosis in rats. However, the mechanisms are yet to be fully elucidated. Methods: Myocardial fibrosis in apolipoprotein E-deficient (ApoE -/-) mice and neonatal rat cardiac fibroblasts (CFs) were induced using homocysteine (Hcy). Results: IMD1–53 inhibited myocardial fibrosis in vivo and in vitro. Picrosirius red staining showed that IMD1–53 reduced myocardial interstitial collagen deposition in ApoE-/- mice treated with Hcy and decreased the expression of myocardial collagen I and III, which was further verified in rat CFs. IMD1–53 attenuated myocardial hypertrophy, as shown by cardiomyocyte cross-sectional area, ratio of heart weight to body weight, and mRNA levels of atrial natriuretic peptide and brain natriuretic peptide. IMD1–53 inhibited the upregulation of ERS hallmarkers such as glucose-regulated protein 78 (GRP78), GRP94, activating transcription factor 6 (ATF6), ATF4, inositol-requiring enzyme 1α, spliced-X-box-binding protein-1, protein kinase receptor-like ER kinase, and eukaryotic translation initiation factor 2α in mouse myocardium and rat CFs treated with Hcy. In addition, IMD1–53 decreased the production of inflammatory factors such as tumor necrosis factor-α, monocyte chemotactic protein-1, interleukin-6 (IL-6), and IL-1β in the mouse myocardium and rat CFs treated with Hcy. Concurrently, IMD1–53 ameliorated the expression of nuclear factor-κB, transforming growth factor-β1, and c-Jun N-terminal kinase in the mouse myocardium and rat CFs treated with Hcy. Conclusions: IMD potentially protects against myocardial fibrosis induced by Hcy in ApoE-/- mice, possibly via attenuating myocardial ERS and inflammation.
Pseudo-random generation from one-way functions

We show that the existence of one-way functions is necessary and sufficient for the existence of pseudo-random generators in the following sense. Let ƒ be an easily computable function such that when <italic>x</italic> is chosen randomly: (1) from ƒ(<italic>x</italic>) it is hard to recover an <italic>x</italic><supscrpt>1</supscrpt> with ƒ(<italic>x</italic><supscrpt>1</supscrpt>) = ƒ(<italic>x</italic>) by a small circuit, or; (2) ƒ has small degeneracy and from ƒ(<italic>x</italic>) it is hard to recover <italic>x</italic> by a fast algorithm. From one-way functions of type (1) or (2) we show how to construct pseudo-random generators secure against small circuits or fast algorithms, respectively, and vice-versa. Previous results show how to construct pseudo-random generators from one-way functions that have special properties ([Blum, Micali 82], [Yao 82], [Levin 85], [Goldreich, Krawczyk, Luby 88]). We use the results of [Goldreich, Levin 89] in an essential way.
TerraSAR-X TOPS, ScanSAR and WideScanSAR interferometric processing

The German TerraSAR-X and TanDEM-X satellites are able to acquire images operationally in ScanSAR and WideScanSAR modes and experimentally in TOPS mode. This paper gives an overview of the interferometric processing steps of burst-mode acquisitions, emphasizing the importance of the co-registration stage. A co- registration approach based on incoherent cross-correlation is presented. Interferometric results, including differential interferogram, of TerraSAR-X repeat-pass images are provided.
Real-Time Traffic Simulation Using Cellular Automata

In this paper, we present a method to simulate large-scale traffic networks, at real-time frame-rates. Our novel contributions include a method to automatically generate a road graph from real-life data, and our extension to a discrete traffic model, which we use to simulate traffic, demonstrating continuous vehicle motion between discrete locations. Given Ordnance Survey data, we automatically generate a road graph, identifying roads, junctions, and their connections. We distribute cells at regular intervals throughout the graph, which are used as discrete vehicle locations in our traffic model. Vehicle positions are then interpolated between cells to obtain continuous animation. We test the performance of our model using a 500 x 500m2 area of a real city, and demonstrate that our model can simulate over 600 vehicles at real-time frame-rates (greater than 80 percent network density).
Characterization and function of the bHLH-O protein XHes2: insight into the mechanisms controlling retinal cell fate decision

Neurons and glial cells differentiate from common multipotent precursors in the vertebrate retina. We have identified a novel member of the hairy/Enhancer of split [E(spl)] gene family in Xenopus, XHes2, as a regulator to bias retinal precursor cells towards a glial fate. XHes2 expression is predominantly restricted to sensory organ territories, including the retina. Using in vivo lipofection in the optic vesicle, we found that XHes2 overexpression dramatically increases gliogenesis at the expense of neurogenesis. This increase in glial cells correlates with a delayed cell cycle withdrawal of some retinal progenitors. In addition, birthdating experiments suggest that XHes2 deviates some early born cell types towards a glial fate that would normally have given rise to neurons. Conversely, a significant inhibition of glial differentiation is observed upon XHes2 loss of function. The gliogenic activity of XHes2 relies on its ability to inhibit neuronal differentiation by at least two distinct mechanisms: it not only negatively regulates XNgnr1 and NeuroD transcription, but it also physically interacts with a subset of proneural bHLH proteins.
Elemental Abundances in Two High Column Density Damped Lyα Systems at z < 1.5

We present Keck HIRES abundance measurements and metal-line kinematic profiles of the damped Lyα systems (DLAs) toward the quasars Q0933+733 (zabs = 1.479) and Q0948+433 (zabs = 1.233). These two DLAs have among the five highest H I column densities at any redshift: N(H ) = 4.2 × 1021 cm-2. The metal-line data, presented here for the first time, reveal that these DLAs are noteworthy for several other reasons as well. (1) The Q0933+733 DLA exhibits a simple kinematic structure with unusually narrow velocity widths as measured from its unsaturated metal lines (Δv = 16 km s-1). At 2.6% solar, it has the second lowest metallicity at z < 2. (2) The Q0948+433 DLA has among the strongest metal-line transitions of any known DLA. The saturated Si II λ1808 line implies a high metallicity ([Si/H] > -1) and a significant α-enhancement. The strong metal lines of this DLA have made possible the detection of Ti II λ1910, Co II λ2012, and Mg I λ2026. (3) We find that the relative gas-phase abundances of both DLAs follow the general trend seen at high redshift, e.g., enhanced Zn/Fe and Si/Fe and subsolar Mn/Fe, indicating that there is little evolution in the nucleosynthetic patterns of DLAs down to this epoch. (4) Their high H I column densities imply that these DLAs dominate the column density-weighted cosmic mean metallicity, 〈Z〉, of the universe at z < 1.5. Using the 15 DLAs with measured metallicities in the redshift interval 0.4 < z < 1.5, we find 〈Z〉 = -0.89, where the uncertainties are 95% confidence limits.
Two Roles for Attention in Shape Perception: A Structural Description Model of Visual Scrutiny

This paper presents MetriCat, a model of the human capacity to recognize objects both as members of a general class (e.g. “chair”) and as specific instances (“my office chair”), and of the role of visual attention in this capacity. MetriCat represents the attributes of an object's parts and their relations in a non-linear fashion that provides a natural basis for recognition at both the class and instance levels (Stankiewicz & Hummel, 1996). Like previous structural description models (e.g. Hummel & Biederman, 1992), MetriCat represents part attributes and relations independently, dynamically binding them into structural descriptions. The resulting representation suggests two roles forvisual attention in shape recognition: attention for binding and attention for signal-to-noise control. MetriCat implements both functions as special cases of a single mechanism for controlling the synchrony relations among units representing separate object parts. The model accounts for the time course of class- and instanc.
Reliability Adjustment Weighted Bit-Flipping Decoding for Low-Density Parity-Check Codes

In this paper, we propose reliability adjustment weighted bit-flipping (RA-WBF) decoding algorithm for low density parity-check (LDPC) codes. By adjusting the reliability of received symbols during iterative decoding, the proposed algorithm can offer better tradeoff between performance, convergence speed and computational complexity. Furthermore, this improvement can easily be extended to other variations of weighted bit-flipping decoding algorithm and obtain noticeable improvement in error performance. Simulation results are shown to verify our conclusions.
Growing fuzzy inference neural network applied to the NN 3 neural forecasting competition

Growing fuzzy inference neural system (GFINN) is a fuzzy–neural network model. Its functionality can be expressed in a form of fuzzy if–then rules. The skill of the GFINN model to grow allows it to change its size and structure according to the training data. The resulting structure allows for a simple input features selection — not all input features have to be used in every fuzzy rule. The new algorithm for the computation of output weights runs much faster than least mean squares estimate, while the experiments performed in past show nearly identical performace of both methods. Furthermore, the new training method guarantees the output weights to remain inside the output values interval. This makes the extracted rules reasonable, unlike with least mean squares estimate. This text describes the GFINN model and its application to the NN3 neural forecasting competition.
Toward a Code-Clone Search through the Entire Lifecycle of a Software Product

This paper presents a clone-detection method/tool currently under devel- opment. This tool is useful as a code-clone search through the entire lifecycle of a software product; The tool searches code examples and analyzes of code clones in both preventive and postmortem ways[LRHK10]. The approach is based on a sequence equivalence on execution paths[Kam13] and extends the equivalence to include gaps, thus type-3[BKA + 07] clone detection. Each of the detected clones is a sub-sequence of an execution path of a given program, in other words, a set of code fragments of multiple procedures (methods) which can be executed in a run of the program. The approach is relaxed in terms of adaptability to incomplete (not-yet- finished) code, but also makes use of concrete information such as types (including hierarchy) and dynamic dispatch when such information is available.
Improved MAP channel estimation algorithm for MIMO-OFDM systems under angle domain

This paper proposes an improved Maximum a posteriori probability (IMAP) algorithm for the estimation of frequency-selective channels in MIMO-OFDM systems. To further mean square error (MSE) performance and reduce the training overhead during MAP channel estimation, joint estimation is carried out over multiple OFDM symbols firstly. Then, according to the idea that MIMO channels in angle domain can be assumed to be independently. Most significant taps (MST) technique in angle domain is used to improve the MSE performance of MAP algorithm. Simulation results indicate the effectiveness of the proposed algorithm.
Human Activity Recognition : Accelerometers Unveil Your Actions

Wearable devices become more popular and are usually paired with smartphones. In this work, we explore approaches of recognizing human activities using accelerometer data from smartphones and wearable devices. The dataset we use records acceleration signals from four positions that are representable for smartphones and wearable devices. In real world situation, it is more likely that one only takes one smartphone and another wearable device with him/her. So our ultimate goal is to accurately recognize human actions using two accelerometers. We apply machine learning algorithms to train and infer human motion. The input of our algorithm is acceleration data from sensors. We then use GDA and SVM to output a predicted activity class. The experimental results demonstrate the effectiveness of our method.
On the role of object knowledge in reference production:  effects of color typicality on content determination

In two language production experiments, we investigated whether stored knowledge of the typical color of objects affects spoken reference. In experiment 1, human speakers referred to objects with colors ranging from very typical (e.g., red tomato) to very atypical (e.g., blue pepper). The probability that speakers redundantly include color in their descriptions was almost linearly predicted by the degree of atypicality. In experiment 2, we extended this finding to references to objects for which color is inherently a less salient property in stored knowledge (i.e., objects with a highly characteristic shape, making color less important for recognition). Following these findings that typicality affects reference production, we conclude that speakers utilize stored knowledge about everyday objects they refer to. We discuss the implications of our findings for artificial agents that generate natural language, arguing that computational models fall short in captur- ing general knowledge about typical properties of objects.
Global schedulability analysis of a synchronization protocol based on replenishment-bounded overrun for compositional real-time systems

Hierarchical scheduling frameworks (HSFs) provide means for composing complex real-time systems from well-defined independently developed and analyzed subsystems. To support shared logical resources requiring mutual exclusive access in two-level HSFs, overrun without payback has been proposed as a mechanism to prevent budget depletion during resource access arbitrated by the stack resource policy (SRP). The same mechanism can be applied to support scheduling techniques, such as fixed-priority scheduling with deferred preemption (FPDS), that aim at a reduction of the architecture-related preemption costs and may improve the feasibility of a system. Whereas the blocking times and overrun budgets for shared logical resources will typically be much smaller than the normal budget, these values may significantly increase for scheduling techniques such as FPDS. In this paper, we therefor consider replenishment-bounded overrun, i.e. the overrun ends upon a replenishment, because the normal budget becomes available again, which allows for larger overrun budgets. We show that the global schedulability analysis for this special kind of overrun has a number of anomalies: (i) the usual theorem for critical instant does not hold, (ii) maximal blocking does not necessarily lead to a maximal response time, and (iii) it is not sufficient to analyse a fixed amount of time (say, a number of hyperperiods). We present analysis for two subsystems.
ScriptCOM: An extension of COM for the dynamic adaptation

The work presented in this paper is articulated around COM, a components model which represents the Microsoft standard for the development of reusable and communicating components. COM establishes a binary standard of writing of components and standardizes the communication between objects. However, it is not appropriate for the description of the applications to high availability like the banking applications, and the services of telecommunication which require an adaptation which must be carried out by disturbing the least possible application. We propose in this article ScriptCOM, a model of adaptable components COM. The originality of this model is to be extensible towards the model COM while respecting our needs. To validate our proposal, we used ScriptCOM for the development of a financial business application of a store of sale and maintenance of computer equipment.
Recognition of familiar food activates feeding via an endocrine serotonin signal in Caenorhabditis elegans

Familiarity discrimination has a significant impact on the pattern of food intake across species. However, the mechanism by which the recognition memory controls feeding is unclear. Here, we show that the nematode Caenorhabditis elegans forms a memory of particular foods after experience and displays behavioral plasticity, increasing the feeding response when they subsequently recognize the familiar food. We found that recognition of familiar food activates the pair of ADF chemosensory neurons, which subsequently increase serotonin release. The released serotonin activates the feeding response mainly by acting humorally and directly activates SER-7, a type 7 serotonin receptor, in MC motor neurons in the feeding organ. Our data suggest that worms sense the taste and/or smell of novel bacteria, which overrides the stimulatory effect of familiar bacteria on feeding by suppressing the activity of ADF or its upstream neurons. Our study provides insight into the mechanism by which familiarity discrimination alters behavior. DOI: http://dx.doi. org/10.7554/eLife.00329. 001
Are Classical Experiments Needed for Manpower Policy

The critical element that distinguishes classical experiments from all other modes of analysis is the random assignment of treatment to enrollees in a study. This paper examines the major methodological advantages of random assignment for the purpose of estimating the effectiveness of current manpower policy. It also reviews the claimed methodological and ethical objections to experiments. The main valid objection to an experiment is neither methodological nor ethical, it is the experiment's cost in relation to that of nonexperimental methods of analysis. The authors argue that the offsetting gain from experimentation is the inherent reliability of experimental estimates of treatment effects. The paper offers a simple framework for deciding whether the improved reliability of treatment-effect estimates is worth the added cost of experimentation. It concludes with an assessment of the actual value of experiments for evaluating current manpower policies.
Regulatory Reform in Mexico's Natural Gas Industry: Liberalization in the Context of a Dominant Upstream Incumbent

The natural gas industry combines activities with natural monopoly characterisitics with those that are potentially competitive. Pipeline transport and distribution, which have natural monopoly characterisitcs, require regulation of price and non-price behavior. Production is a contestable activity, but in a few countries (including Mexico) it remains a state monopoly. Gas marketing is also contestable, but the presence of a dominant, upstream, vertically integrated incumbent may pose significant barriers to entry. Market architecture decisions--such as horizontal structure, regional development, and the degree of vertical integration--are also crucial. The authors report that Mexico has undertaken structural reform in the energy sector more slowly than many other countries, but it has introduced changes to attract private investment in natural gas transport and distribution. These changes were a response to the rapid growth in demand for natural gas (about 10 percent a year) in Mexico, which was in turn a response to economic development and the enforcement of environmental regulations. The new regulatory framework provides incentives for firms to invest and operate efficiently and to bear much much of the risk associated with new projects. It also protects captive consumers and improves general economic welfare. The continued vertical integration of the state-owned company Pemex and its statutory monopoly in domestic production posed a challenge to regulators. Their response in liberalizing trade, setting first-hand sales prices, and regulating natural gas distribution makes the Mexican case an interesting example of regulatory design. As the first phase of investment mobilization and competition for the market in Mexican distribution project concludes, remaining challenges include consistently and transparently enforcing regulations, coordinating tasks among government agencies, and ensuring expansion of gas transport services and domestic production. A key challenge in the near term will be fostering competition in the market. In strengthening the role of market forces, one issue is Pemex's discretionary discounts on domestic gas and access to transport services, made possible by its monopoly in domestic production and marketing activities and its overwhelming dominance in transport. The main instrument available to the regulator is proscribing Pemex contract pricing, but more durable and tractable instruments should be considered.
Classification of hyperspectral image based on deep belief networks

Generally, dimensionality reduction methods, such as Principle Component Analysis (PCA) and Negative Matrix Factorization (NMF), are always applied as the preprocessing part in hyperspectral image classification so as to classify the constituent elements of every pixel in the scene efficiently. The results, however, would suffer the loss of detailed information inevitably. In this paper, deep learning frameworks, restricted Boltzmann machine (RBM) model and its deep structure deep belief networks (DBN), are introduced in hyperspectral image processing as the feature extraction and classification approach. The experiments are conducted on an airborne hyperspectral image. Further in the experiments, spatial-spectral classification is also practiced. Meanwhile, SVM with and without some classical feature extraction methods adopting before classification are employed as comparison. The results show the superior performance of the proposed approach.
A Hybrid PSO Approach to Automate Test Data Generation for Data Flow Coverage with Dominance Concepts

This paper presents a technique that based on a combination of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called GPSCA (Genetic-Particle Swarm Combined Algorithm) which is used to generate automatic test data for data flow coverage with using dominance concept between two nodes. The performance of the proposed approach is analyzed on a number of programs having different size and complexity. Finally, the performance of GPSCA is compared to both GA and PSO for generation of automatic test cases to demonstrate its superiority.
A comparative study on the bug-proneness of different types of code clones

Code clones are defined to be the exactly or nearly similar code fragments in a software system's code-base. The existing clone related studies reveal that code clones are likely to introduce bugs and inconsistencies in the code-base. However, although there are different types of clones, it is still unknown which types of clones have a higher likeliness of introducing bugs to the software systems and so, should be considered more important for managing with techniques such as refactoring or tracking. With this focus, we performed a study that compared the bug-proneness of the major clone-types: Type 1, Type 2, and Type 3. According to our experimental results on thousands of revisions of seven diverse subject systems, Type 3 clones exhibit the highest bug-proneness among the three clone-types. The bug-proneness of Type 1 clones is the lowest. Also, Type 3 clones have the highest likeliness of being co-changed consistently while experiencing bug-fixing changes. Moreover, the Type 3 clones that experience bug-fixes have a higher possibility of evolving following a Similarity Preserving Change Pattern (SPCP) compared to the bug-fix clones of the other two clone-types. From the experimental results it is clear that Type 3 clones should be given a higher priority than the other two clone-types when making clone management decisions. We believe that our study provides useful implications for ranking clones for refactoring and tracking.