Miracle-dz/UniFAR-FTUs huggingface dataset:
Upregulation of long noncoding RNA TUG1 promotes cervical cancer cell proliferation and migration

Long noncoding RNAs (lncRNAs), a novel class of transcripts that have critical roles in carcinogenesis and progression, have emerged as important gene expression modulators. Recent evidence indicates that lncRNA taurine‐upregulated gene 1 (TUG1) functions as an oncogene in numerous types of human cancers. However, its function in the development of cervical cancer remains unknown. The aim of this research was to investigate the clinical significance and biological functions of TUG1 in cervical cancer. TUG1 was found to be significantly upregulated in cervical cancer tissues and four cervical cancer cell lines by quantitative real‐time polymerase chain reaction (qRT‐PCR). Elevated TUG1 expression was correlated with larger tumor size, advanced international federation of gynecology and obstetrics (FIGO) stage, poor differentiation, and lymph node metastasis. Furthermore, knockdown of TUG1 suppressed cell proliferation with activation of apoptosis, in part by regulating the expression of Bcl‐2 and caspase‐3. Silencing of TUG1 inhibited cell migration and invasion via the progression of epithelial–mesenchymal transition (EMT). Taken together, our findings indicate that TUG1 acts as an oncogene in cervical cancer and may represent a novel therapeutic target.
¿Cómo percibimos la publicidad?: Antecedentes de la actitud hacia la publicidad e implicaciones en la intención de compra

An empirical analysis of the dimensions that explain how consumers perceive advertising and the consequences of this process is presented. By means of a questionnaire, it was possible to question 220 Portuguese consumers on the advertising of the leading telecommunications brand in Portugal. Data analysis revealed four dimensions as present in the perception of advertising: entertainment, relevance, empathy and repulsion. It was concluded that entertainment and relevance (both positively) and repulsion (negatively) predict the "attitudes on brand publicity" and that the latter partially explains the "purchase intention".
Constant resolution omnidirectional cameras

In this paper we present a general methodology for designing mirrors of catadioptric onmidirectional sensors encompassing linear projection properties, the so called constant resolution cameras. The linearity is stated between 3D distances (or angles) and pixel coordinates. We include three practical cases of interest of linear constraints for both standard (cartesian pixel distribution) and log-polar cameras: constant vertical, horizontal and angular resolution. Finally, the formulation is applied in designing a camera combining some of the presented practical cases. Resulting images show that the design was successful as the desired linear properties were obtained.
Modeling and simulation of integrated CUK converter for grid connected PV system with EPP MPPT hybridization

This paper presents the modeling and simulation of integrated CUK converter with EPP MPPT for grid connected photovoltaic system. Integrated CUK converter is presented for the use as a high gain boost (step- up) converter whose switch operates in continuous conduction mode. MPPT controllers are used in PV system to track the maximum power due to its poor efficiency. The system is designed in MATLAB/SIMULINK and the simulation results shows the comparative analysis of high gain integrated CUK converter with conventional CUK converter for grid connected photovoltaic system.
A Critical Role for Syk in Signal Transduction and Phagocytosis Mediated by Fcγ Receptors on Macrophages

Receptors on macrophages for the Fc region of IgG (FcγR) mediate a number of responses important for host immunity. Signaling events necessary for these responses are likely initiated by the activation of Src-family and Syk-family tyrosine kinases after FcγR cross-linking. Macrophages derived from Syk-deficient (Syk−) mice were defective in phagocytosis of particles bound by FcγRs, as well as in many FcγR-induced signaling events, including tyrosine phosphorylation of a number of cellular substrates and activation of MAP kinases. In contrast, Syk− macrophages exhibited normal responses to another potent macrophage stimulus, lipopolysaccharide. Phagocytosis of latex beads and Escherichia coli bacteria was also not affected. Syk− macrophages exhibited formation of polymerized actin structures opposing particles bound to the cells by FcγRs (actin cups), but failed to proceed to internalization. Interestingly, inhibitors of phosphatidylinositol 3-kinase also blocked FcγR-mediated phagocytosis at this stage. Thus, PI 3-kinase may participate in a Syk-dependent signaling pathway critical for FcγR-mediated phagocytosis. Macrophages derived from mice deficient for the three members of the Src-family of kinases expressed in these cells, Hck, Fgr, and Lyn, exhibited poor Syk activation upon FcγR engagement, accompanied by a delay in FcγR-mediated phagocytosis. These observations demonstrate that Syk is critical for FcγR-mediated phagocytosis, as well as for signal transduction in macrophages. Additionally, our findings provide evidence to support a model of sequential tyrosine kinase activation by FcγR's analogous to models of signaling by the B and T cell antigen receptors.
How Morphological Constraints Affect Axonal Polarity in Mouse Neurons

Neuronal differentiation is under the tight control of both biochemical and physical information arising from neighboring cells and micro-environment. Here we wished to assay how external geometrical constraints applied to the cell body and/or the neurites of hippocampal neurons may modulate axonal polarization in vitro. Through the use of a panel of non-specific poly-L-lysine micropatterns, we manipulated the neuronal shape. By applying geometrical constraints on the cell body we provided evidence that centrosome location was not predictive of axonal polarization but rather follows axonal fate. When the geometrical constraints were applied to the neurites trajectories we demonstrated that axonal specification was inhibited by curved lines. Altogether these results indicated that intrinsic mechanical tensions occur during neuritic growth and that maximal tension was developed by the axon and expressed on straight trajectories. The strong inhibitory effect of curved lines on axon specification was further demonstrated by their ability to prevent formation of multiple axons normally induced by cytochalasin or taxol treatments. Finally we provided evidence that microtubules were involved in the tension-mediated axonal polarization, acting as curvature sensors during neuronal differentiation. Thus, biomechanics coupled to physical constraints might be the first level of regulation during neuronal development, primary to biochemical and guidance regulations.
Performance evaluation of typical unsupervised feature learning algorithms for visual object recognition

Many kinds of feature learning algorithms have been proposed and have continually refreshed the state-of-the-art performance in speech recognition, visual object recognition et al. However, most of them are complicated and hard to train, which limits more widely application. In this paper, we present the usability performance evaluation of six typical unsupervised feature learning algorithms from aspects of accuracy, time cost, and hyper-parameters. A common patch based framework [1] with mediocre parameters is adopted to highlight the difference between algorithms. The experiments confirm that sparse coding can attain consistent performance across different datasets. Moreover, random patches with soft threshold function and K-means combining with triangle coding achieve comparable performance with sparse coding, and even faster and easier to train, the results suggest they are good choices to build an application system in practice.
The Emerging Financial Pre-warning Systems

The exact prediction of financial crises is an essential research task for decision makers. In recent years, data mining techniques have been used to tackle the related problems and perform a satisfactory job in various domains. However, in the information age, utilizing straightforward data mining techniques to predict financial crises has many shortcomings and limitations. Thus, this investigation utilized the random forest (RF) technique as a pre-processing procedure to determine the most representative features. Then, the selected features were fed into rough set theory to yield interpretable information for decision makers, who can use it to make suitable judgments in a turbulent economic climate. The proposed model is a promising alternative for predicting financial crisis, and it can assist in regard to both taxation and financial institutions.
The impulse RETINA: A smart velocity sensor

The model Retina developed by the laboratory LACOS (University of le Havre, France) in collaboration with the A.B. Kogan research institute for neurocybernetics (Rostov-state University, Russia) is a retinal neural network permitting a pre-processing of foveal vision. This model is adaptative and its multi-resolution allows it to detect a large scale of velocities. We use the Retina to detect the motion and extract the velocity vector of a time sequence image. From impulse output signals of Retina we extract the pertinent parameters which encode the motion by time frequency analysis. The final aim is to implement this sensor on a mobile robot.
Sentence Embedding Evaluation Using Pyramid Annotation

Word embedding vectors are used as input for a variety of tasks. Choosing the right model and features for producing such vectors is not a trivial task and different embedding methods can greatly affect results. In this paper we repurpose the "Pyramid Method" annotations used for evaluating automatic summarization to create a benchmark for comparing embedding models when identifying paraphrases of text snippets containing a single clause. We present a method of converting pyramid annotation files into two distinct sentence embedding tests. We show that our method can produce a good amount of testing data, analyze the quality of the testing data, perform test on several leading embedding methods, and finally explain the downstream usages of our task and its significance.
Generation of Broadband THz Airy Beams Applying 3D Printing Technique

Non-diffraction beams like Airy beams have been widely explored in various disciplines owing to its fascinating features. A broadband generator for THz Airy beam operating from 90 to 110 GHz is investigated in this work. It is composed of three lenses and fabricated by 3D printing technique using polymer. Experimental characterization is conducted to validate the effectiveness of the fabricated Airy beam generator. Obvious curved propagation trajectory of the Airy beam is observed in the operation bandwidth, implying successful generation of the broadband Airy beam. Potential applications like broadband THz wave manipulation and imaging can be developed based on the proposed technique.
Strategies for Analysis against Redundant Reused Codes in Safety-Critical Software

Software that applied in the industrial safety-critical systems are usually developed from reused components. The redundant parts of the safety-critical software introduced by such approach shall therefore be considered from the perspective of avoidance and protection from systematic failure. The large scale and high complexity of modern safety-critical software increase the difficulty on conducting safety analysis against it. A series of strategy for safety analysis against the redundant reused codes with detailed procedures are proposed. It is based on the application of systematic safety analysis approach on the activation model of redundant reused codes modeled by the patterns that such codes take effect in the system. The proposed approach provides methodological reference for the developer and analyzer in the fields of industrial safety-critical software/system on conducting highly focused safety analysis.
Limit theorems for continuous-time random walks with infinite mean waiting times

A continuous-time random walk is a simple random walk subordinated to a renewal process used in physics to model anomalous diffusion. In this paper we show that, when the time between renewals has infinite mean, the scaling limit is an operator Lévy motion subordinated to the hitting time process of a classical stable subordinator. Density functions for the limit process solve a fractional Cauchy problem, the generalization of a fractional partial differential equation for Hamiltonian chaos. We also establish a functional limit theorem for random walks with jumps in the strict generalized domain of attraction of a full operator stable law, which is of some independent interest.
Time-lag method for detecting following and leadership behavior of pedestrians from mobile sensing data

The vast availability of mobile phones with built-in movement and location sensors enable the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper we propose methods for detecting two common pedestrian movement patterns, namely individual following relations and group leadership. The proposed methods for identifying following patterns employ machine learning on features derived using similarity analysis on time lagged sequences of WiFi measurements containing either raw signal strength values or derived locations. To detect leadership we combine the individual following relations into directed graphs and detect leadership within groups by graph link analysis. Methods for detecting these movement patterns open up new possibilities in - amongst others - computational social science, reality mining, marketing research and location-based gaming. We provide evaluation results that show error rates down to 7%, improving over state of the art methods with up to eleven percentage points for following patterns and up to twenty percentage points for leadership patterns. Our method is, contrary to state of the art, also applicable in challenging indoor environments, e.g., multi-story buildings. This implies that even quite small samples allow us to detect information such as how events and campaigns in multistory shopping malls may trigger following in small groups, or which group members typically take the lead when triggered by e.g. commercials, or how rescue or police forces act during training exercises.
Parallelizing FIB Lookup in Content Centric Networking

As a promising future Internet architecture, CCN(Content Centric Networking) is a pragmatic shift from host-to-host to named content model. Providing each piece of content with a specified name, CCN can either directly route a user's Interest for a specific piece of content to the content server, or simply pick the right piece of content from the in network cache and send it back to the user. The combination of content name and in-network cache makes CCN suitable for content distribution. Name based FIB (Forwarding Information Base) table lookup thus plays an important role in CCN routing, as FIB records the forwarding face set for different name prefixes. This paper leverages multi-core platform to accelerate the FIB lookup in CCN router. Based on TILEPro64 platform, which has 64 identical tiles, two parallel zed lookup algorithms, one is based on hash table and the other is based on Bloom filter, are implemented and evaluated. To better evaluate the performance, the size of FIB is estimated and the statistics of URLs are measured to simulate the generation of practical Interests packet traffic. Through theoretical evaluation and experiment, it shows that the parallelization can improve the FIB lookup performance efficiently.
Implementing a classroom management package in an urban middle school: A case study

Abstract Previous research has shown that a classroom management package can be used to prevent challenging behaviors and promote positive student behavior. Replicating and extending previous research, the purpose of this study was to investigate if a package of precorrection, explicit timing, and active supervision can have a positive effect across content areas and grade levels within an urban middle school. Utilizing 3 single-case AB designs, the effects of a classroom management intervention in 3 classrooms were observed on decreasing student disruptions and teacher redirections. Results indicated that the rates of student disruptions and teacher redirections decreased by more than 50% across the 3 classrooms. To evaluate consumer satisfaction within the intervention, we collected social validity data.
A textual domain specific language for requirement modelling

Requirement specification is usually done with a combination of Natural Language (NL) and informal diagrams. Modeling approaches to support requirement engineering activities have involved a combination of text and graphical models. In this work, a textual domain specific modelling notation for requirement specification is presented. How certain requirement quality attributes are addressed using this notation is also demonstrated.
LLPA: Logic State Based Leakage Power Analysis

Numerous side-channel attacks on integrated circuit implementations of cryptographic systems have been demonstrated in literature. Insecure implementations can reveal secret information through data dependencies in dynamic and leakage power profiles. Side-channel resistant logic styles are effective against dynamic power analysis attacks, but are suggested to exhibit weaknesses against the less common Leakage Power Analysis (LPA) attacks. We present a novel LPA attack that uses knowledge of a circuit's internal structure to mount a stronger attack via the leakage power side-channel, and show that even dual-rail side-channel resistant logic styles are susceptible to these LPA attacks. Our proposed LPA attack can successfullyextract secret key information from S-boxes even in the presenceof large amounts of random on-chip noise, and in scenarioswhere Hamming-weight based techniques are unsuitable. We alsoevaluate the impact of process variations on our scheme, andpropose strategies for mitigating this impact.
Concept-based Text Clustering

Thematic organization of text is a natural practice of humans and a crucial task for today’s vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that facilitates exploration, search and interpretation of current texts, as well as organization of future ones. Automatic clustering is usually based on words. Text is represented by the words it mentions, and thematic similarity is based on the proportion of words that texts have in common. The resulting bag-of-words model is semantically ambiguous and undesirably orthogonal—it ignores the connections between words. This thesis claims that using concepts as the basis of clustering can significantly improve effectiveness. Concepts are defined as units of knowledge. When organized according to the relations among them, they form a concept system. Two concept systems are used here: WordNet, which focuses on word knowledge, and Wikipedia, which encompasses world knowledge. We investigate a clustering procedure with three components: using concepts to represent text; taking the semantic relations among them into account during clustering; and learning a text similarity measure from concepts and their relations. First, we demonstrate that concepts provide a succinct and informative representation of the themes in text, exemplifying this with the two concept systems. Second, we define methods for utilizing concept relations to enhance clustering by making the representation models more discriminative and extending thematic similarity beyond surface overlap. Third, we present a similarity measure based on concepts and their relations that is learned from a small number of examples, and show that it both predicts similarity consistently with human judgement and improves clustering. The thesis provides strong support for the use of concept-based representations instead of the classic bag-of-words model.
Source apportionment of size and time resolved trace elements and organic aerosols from an urban courtyard site in Switzerland

Abstract. Time and size resolved data of trace elements were obtained from measurements with a rotating drum impactor (RDI) and subsequent X-ray fluorescence spectrometry. Trace elements can act as indicators for the identification of sources of particulate matter 10 ) in ambient air. Receptor modeling was performed with positive matrix factorization (PMF) for trace element data from an urban background site in Zurich, Switzerland. Eight different sources were identified for the three examined size ranges (PM 1−0.1 , PM 2.5−1 and PM 10−2.5 ): secondary sulfate, wood combustion, fire works, road traffic, mineral dust, de-icing salt, industrial and local anthropogenic activities. The major component was secondary sulfate for the smallest size range; the road traffic factor was found in all three size ranges. This trace element analysis is complemented with data from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (AMS), assessing the PM 1 fraction of organic aerosols. A separate PMF analysis revealed three factors related to three of the sources found with the RDI: oxygenated organic aerosol (OOA, related to inorganic secondary sulfate), hydrocarbon-like organic aerosol (HOA, related to road traffic) and biomass burning organic aerosol (BBOA), explaining 60 %, 22 % and 17 % of total measured organics, respectively. Since different compounds are used for the source classification, a higher percentage of the ambient PM 10 mass concentration can be apportioned to sources by the combination of both methods.
Smoking Related Cancers and Loci at Chromosomes 15q25, 5p15, 6p22.1 and 6p21.33 in the Polish Population

Genetic factors associated with the risk of smoking related cancers have until recently remained elusive. Since the publication of a genome-wide association study (GWAS) on lung cancer new genetic loci have been identified that appear to be associated with disease risk. In this replication study we genotyped 14 single nucleotide polymorphisms (SNPs) located at the 5p12.3-p15.33, 6p21.3-p22.1, 6q23-q27 and 15q25.1 loci in 874 lung, 450 bladder, 418 laryngeal cancer cases and cancer-free controls, matched by year of birth and sex to the cases. Our results revealed that loci in the chromosome region 15q25.1 (rs16969968[A], rs8034191[G]) and 5p15 (rs402710[T]) are associated with lung cancer risk in the Polish population (smoking status adjusted OR = 1.45, 1.35, 0.77; p≤0.0001, 0.0005, 0.002; 95%CI 1.23–1.72, 1.14–1.59, 0.66–0.91 respectively). None of the other regions analyzed herein were implicated in the risk of lung, bladder or laryngeal cancer. This study supports previous findings on lung cancer but fails to show association of SNPs located in 15q25.1 and 5p15 region with other smoking related cancers like bladder and laryngeal cancer.
Innovative Retail Laboratory

The Innovative Retail Laboratory (IRL) is an applicationoriented research laboratory of the German Research Center for Artificial Intelligence (DFKI) run in collaboration with the German retailer GLOBUS SB-Warenhaus Holding in St. Wendel. In this living lab, we conduct research in a wide range of different domains, mostly related to intelligent shopping assistance. Our demonstrators range from an instrumented shopping cart employing indoor navigation to several intelligent shopping consultants, ambient information services and an automated checkout system.