Miracle-dz/UniFAR-FTUs huggingface dataset:
Prostate Cancer Cells Dependent Invasion by Metastatic − Metalloproteinase-1-Kinase in Integrin Fibronectin Receptor-Mediated , Matrix ′ Role of Focal Adhesion Kinase and Phosphatidylinositol 3

A5B1 Integrin interacts with the PHSRN sequence of plasma fibronectin, causing constitutive invasion by human prostate cancer cells. Inhibition of this process reduces tumorigenesis and prevents metastasis and recurrence. In this study, naturally serum-free basement membranes were used as in vitro invasion substrates. Immunoassays were employed to dissect the roles of focal adhesion kinase (FAK), phosphatidylinositol 3¶-kinase (PI3K), and protein kinase CD (PKCD) in A5B1-mediated, matrix metalloproteinase-1 (MMP-1)– dependent invasion by metastatic human DU 145 prostate cancer cells. We found that a peptide composed of the PHSRN sequence induced rapid FAK phosphorylation at Tyr (Y397), a site whose phosphorylation is associated with kinase activation. The technique of RNA silencing [small interfering RNA (siRNA)] confirmed the role of FAK in PHSRN-induced invasion. PHSRN also induced the association of the p85regulatory subunit of PI3K with FAK at a time corresponding to FAK phosphorylation and activation, and maximal PI3K activity occurred at this same time. The necessity of PI3K activity in both PHSRN-induced invasion and MMP-1 expression was confirmed by using specific PI3K inhibitors. By employing a specific inhibitor, Rottlerin, and by using siRNA, we also found that PKCD, a PI3K substrate found in focal adhesions, functions in PHSRN-induced invasion. In addition, the induction of MMP-1 in PHSRN-treated DU 145 cells was shown by immunoblotting, and the role of MMP-1 in PHSRNinduced invasion was confirmed by the use of blocking antiMMP-1 monoclonal antibody. Finally, a close temporal correspondence was observed between PHSRN-induced invasion and PHSRN-induced MMP-1 activity in DU 145 cells. (Cancer Res 2006; 66(16): 8091-9)
Trade-Off Lower Bounds for Stack Machines

A space bounded Stack Machine is a regular Turing Machine with a read-only input tape, several space bounded read-write work tapes, and an unbounded stack. Stack Machines with a logarithmic space bound have been connected to other classical models of computation, such as polynomial time Turing Machines (P) (Cook; 1971) and polynomial size, polylogarithmic depth, bounded fan-in circuits (NC) e.g., (Borodin et al.; 1989). In this paper, we give the first known lower bound for Stack Machines. This comes in the form of a trade-off lower bound between space and number of passes over the input tape. Specifically, we give an explicit permuted inner product function such that any Stack Machine computing this function requires either sublinear polynomial space or sublinear polynomial number of passes. In the case of logarithmic space Stack Machines, this yields an unconditional sublinear polynomial lower bound for the number of passes. To put this result in perspective, we note that Stack Machines with logarithmic space and a single pass over the input can compute Parity, Majority, as well as certain languages outside NC. The latter follows from (Allender; 1989), conditional on the widely believed complexity assumption that EXP is different from PSPACE. Our technique is a novel communication complexity reduction, thereby extending the already wide range of models of computation for which communication complexity can be used to obtain lower bounds. Informally, we show that a k-player number-in-hand communication protocol for a base function f can efficiently simulate a space- and pass-bounded Stack Machine for a related function F, which consists of several permuted instances of f, bundled together by a combining function h. Trade-off lower bounds for Stack Machines then follow from known communication complexity lower bounds. The framework for this reduction was given by (Beame and Huynh-Ngoc; 2008), who used it to obtain similar trade-off lower bounds for Turing Machines with a constant number of pass-bounded external tapes. We also prove that the latter cannot efficiently simulate Stack Machines, conditional on the complexity assumption that E is not a subset of PSPACE. It is the treatment of an unbounded stack which constitutes the main technical novelty in our communication complexity reduction.
A Co-evolution Based Method for Optimizing Crossing Waypoints Locations and Adjacency Relation in Air Route Network

With a rapid development of air transportation, the optimization of Air Route Network (ARN) becomes critical to improve both airspace safety and efficiency in air traffic management system. This paper proposes a bi-objective ARN optimization model by considering its two coupled sub-problems, i.e., The optimization of Crossing Waypoints (CWs) locations and the optimization of Adjacency Relation (AR). A co-evolution based method, termed as CoM-ARN, is developed for solving the ARN optimization problem. In CoM-ARN, the locations of CWs and the AR are evolved in a cycle and co-evolved way. In the evolutionary process, Multi-Objective Comprehensive Learning Particle Swarm Optimizer (MOCLPSO) is adopted to globally search the CWs locations. Besides, an AR generator is specifically designed to generate edges of ARN using Delaunay Triangulation (DT) based on the locations of CWs. In addition, in order to minimize the flight cost, a refinement operator is proposed to fine-tune the locations of CWs guided by a virtual attractive force applied by the pairs of origin-destination airports on the CWs in an ARN. Empirical studies using real data of China airspace demonstrate that our method shows great superiority compared with other methods using Multi-Objective Evolutionary Algorithms (MOEAs) on the ARN optimization problem.
Unconventional Algorithms: Complementarity of Axiomatics and Construction

In this paper, we analyze axiomatic and constructive issues of unconventional computations from a methodological and philosophical point of view. We explain how the new models of algorithms and unconventional computations change the algorithmic universe, making it open and allowing increased flexibility and expressive power that augment creativity. At the same time, the greater power of new types of algorithms also results in the greater complexity of the algorithmic universe, transforming it into the algorithmic multiverse and demanding new tools for its study. That is why we analyze new powerful tools brought forth by local mathematics, local logics, logical varieties and the axiomatic theory of algorithms, automata and computation. We demonstrate how these new tools allow efficient navigation in the algorithmic multiverse. Further work includes study of natural computation by unconventional algorithms and constructive approaches.
Accelerated Parallel Optimization Methods for Large Scale Machine Learning

The growing amount of high dimensional data in different machine learning applications requires more efficient and scalable optimization algorithms. In this work, we consider combining two techniques, parallelism and Nesterov's acceleration, to design faster algorithms for L1-regularized loss. We first simplify BOOM, a variant of gradient descent, and study it in a unified framework, which allows us to not only propose a refined measurement of sparsity to improve BOOM, but also show that BOOM is provably slower than FISTA. Moving on to parallel coordinate descent methods, we then propose an efficient accelerated version of Shotgun, improving the convergence rate from $O(1/t)$ to $O(1/t^2)$. Our algorithm enjoys a concise form and analysis compared to previous work, and also allows one to study several connected work in a unified way.
Delay robustness in cooperative control

The robustness of various cooperative control schemes on large scale networked systems with respect to heterogeneous communication and coupling delays is investigated. The presented results provide delay-dependent and delay-independent conditions that guarantee consensus, rendezvous, flocking, and synchronization in different classes of multi-agent systems (MAS). All conditions are scalable to arbitrarily large multi-agent systems with non-identical agent dynamics. In particular, conditions for linear agents, for nonlinear agents with relative degree one, and for a class of nonlinear agents with relative degree two are presented. The interconnection topology between the agents is in most cases represented by an undirected graph. The results for nonlinear agents with relative degree one hold also for the more general case of directed graphs with switching topologies. Different delay configurations are investigated and compared. These configurations represent different ways how the delays affect the coupling between the agents. The presented robustness analysis considers constant, time-varying, and distributed delays in order to take different sources of delays into account. The results are applied to several typical applications and simulations illustrate the findings. The main contributions of this thesis include: (i) Consensus and rendezvous in single integrator MAS are robust to arbitrarily large delays even on switching topologies. However, the convergence rate of this MAS is delay-dependent and scalable convergence rate conditions are presented. (ii) Consensus and rendezvous in relative degree two MAS are robust to sufficiently small delays. Local, scalable conditions are derived for these MAS that guarantee consensus and rendezvous for bounded delays. (iii) Finally, the derived delay robustness analysis for general linear MAS allows for the first time to compare different delay configurations in a unifying framework. In dieser Dissertation wird die Robustheit von verschiedenen kooperierenden Regelungen bezuglich heterogener Kommunikations- und Kopplungsverzogerungen untersucht. Es werden skalierbare Analysemethoden fur Konsens, Rendezvous, Herdenverhalten (engl. flocking) und Synchronisation fur verschiedene Klassen von linearen und nichtlinearen Agentensystemen (engl. multi-agent systems) vorgestellt. Die Ergebnisse werden auf verschiedene typische Anwendungen ubertragen und durch Simulationen veranschaulicht.
The Vicissitudes of Positive Autobiographical Recollection as an Emotion Regulation Strategy in Depression

Recalling positive autobiographical memories is a powerful way to regulate emotion and repair low mood. However, depressed individuals often derive limited emotional benefit from such positive recollection. To identify the conditions under which positive memory recall is emotionally beneficial, we investigated whether memories perceived as discrepant from one’s current sense of self have a differential impact on low mood compared to memories perceived as concordant with the current self. Depressed participants (n = 39) recalled either a self-concordant or self-discrepant positive memory to alleviate sad mood and reported reduced sadness only when the memory was concordant with, but not discrepant from, their current sense of self. In contrast, never-depressed participants (n = 40) experienced mood improvement regardless of the type of memory recalled. These results suggest that the degree to which depressed individuals can identify with the self from a past positive event is an important determinate of the emotional consequences that follow.
Robust Structure and Motion Factorization of Non-Rigid Objects

Structure from motion is an import theme in computer vision. Although great progress has been made both in theory and applications, most of the algorithms only work for static scenes and rigid objects. In recent years, structure and motion recovery of nonrigid objects and dynamic scenes has received a lot of attention. In this paper, the state-of-the-art techniques for structure and motion factorization of nonrigid objects are reviewed and discussed. First, an introduction of the structure from motion problem is presented, followed by a general formulation of nonrigid structure from motion. Then, an augmented affined factorization framework, by using homogeneous representation, is presented to solve the registration issue in the presence of outlying and missing data. Third, based on the observation that the reprojection residuals of outliers are significantly larger than those of inliers, a robust factorization strategy with outlier rejection is proposed by means of the reprojection residuals, followed by some comparative experimental evaluations. Finally, some future research topics in nonrigid structure from motion are discussed.
Correlations between Different Heavy Metals in Diverse Body Fluids: Studies of Human Semen Quality

It has been hypothesized that exposure to heavy metals may impair male reproduction. To measure the effect produced by low doses of heavy metals on semen parameters, it is necessary to clarify in which body fluids those measurements must be performed. Sixty-one men attending infertility clinics participated in our study. Concentrations of lead, cadmium, and mercury were measured in whole blood, blood plasma, and seminal plasma using spectroanalytical and electrochemical methods. Semen analyses were performed according to World Health Organization criteria. For statistical analysis, Spearman's rank correlations, mean comparison tests, and discriminant analysis were calculated. Significant correlations between the measured concentrations of the three heavy metals in the same biological fluids were observed. However, no similar relationship was seen when comparing the concentrations in different body fluids of the same metal. According to our results and previous publications, seminal plasma might be the best body fluid for assessing impairment of human semen parameters.
SET MEMBERSHIP PARAMETER IDENTIFICATION WITH COMPLEX INTERVALS USING POLAR FORMS

Abstract This paper is dedicated to bounded error identification with complex valued non-linear models. Complex intervals are characterized by using polar forms and a new inclusion function is given for the addition of sectors. The latter is expressed as an optimization problem solved analytically. The new complex interval arithmetic is used with actual data and a complex valued non-linear model for the bounded error identification of the thermal properties of materials.
When does inter-cat communication become inter-cat aggression?

Cats are rapidly catching up with dogs as the UK's favourite companion animal, yet the majority of cat owners continue to be unaware of the cat's welfare needs. This lack of owner understanding can lead to owners assuming that the cat's needs — particularly for social companionship — are similar to their own, leading owners to seek feline companions for their cats. This article considers the cat's capacity for intra-specific relationships and gives an over-view of the purpose of the cat's communication system and the significance of, and subtlety of, aggression within the cat's communication repertoire.
DIFOJO: a Java fork/join framework for heterogeneous networks

Many object-oriented frameworks have focused on exploiting parallelism on either shared memory or distributed memory machines. This paper presents the design, implementation and preliminary evaluation of a Java-based framework that exploits both forms of parallel machine connected together to form heterogeneous environments. The framework readily supports implementation of problems that can be solved in parallel by a divide-and-conquer approach.
Role of oxygen radicals in induction of DNA damage by metabolites of benzene.

Benzene is strongly suspected of being an animal and human carcinogen, but the mechanisms by which benzene induces tumors of lymphoid and hematopoietic organs are unknown. Binding studies in vivo suggest a very low level of covalent binding to the DNA of bone marrow elements. Since several metabolites of benzene have the potential to undergo autooxidation and thereby generate reactive oxygen intermediates, we have tested the hypothesis that benzene metabolites can induce DNA damage through the generation of oxygen radicals. Hydroquinone (HQ), benzoquinone (BQ), catechol, and 1,2,4-benzenetriol (BT) were first tested for their ability to generate O2-. at a physiological pH. BT, and to a lesser extent HQ, were autooxidized and produced significant quantities of O2-. No detectable O2-. was produced by catechol or BQ. Similarly, BT was very efficient at degrading DNA, and this degradation was inhibited by scavengers of O2-. , H2O2 and . OH. HQ did not degrade DNA but did induce single- and double-strand breaks. In contrast to the action of BT, the breakage of DNA by HQ was not inhibited by scavengers of reactive oxygen intermediates. The metabolites which did not produce O2-. (catechol and BQ) did not induce significant breakage of DNA. Taken together, the data support the hypothesis that certain benzene metabolites can induce DNA damage through the production of oxygen radicals; they further suggest that other metabolites may act, via another mechanism, to damage DNA.
Assessing Deviations of Empirical Measures for Temporal Network Anomaly Detection : An Exercise

The Internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continually, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, anomaly-based network detection techniques are a valuable technology to protect target systems and networks against malicious activities. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. This paper begins with an exercise of the most well-known anomaly-based detection techniques. Then, available platforms, systems under development and research projects in the area are presented. Finally, we outline the main challenges to be dealt with for the wide scale deployment of anomaly-based detectors, with special emphasis on assessment issues. Network anomaly detection is a vibrant research area. Researchers have approached this problem using various techniques such as artificial intelligence, machine learning, and state machine modeling. In this paper we introduce an internet traffic anomaly detection mechanism based on large deviations results for empirical measures. Using past traffic traces we characterize network traffic during various time-of-day intervals, assuming that it is anomaly-free. We present two different approaches to characterize traffic: (i)A modelfree approach based on the method of types and Sanov’s theorem, and (ii) A model-based approach modeling traffic using a Markov modulated process. Using these characterizations as a reference we continuously monitor traffic and employ large deviations and decision theory results to compare the empirical measure of the monitored traffic with the corresponding reference characterization, thus , identifying traffic anomalies in real-time. Our experimental results shows that applying our methodology (even shortlived) anomalies are identified with in a small number of observations. Through out, we compare the two approaches presenting their advantages and disadvantages to identify and classify temporal network anomalies. We also demonstrate how our framework can be used to monitor traffic from multiple network elements in order to identify both spatial and temporal anomalies. We validate our techniques by analyzing real traffic traces with time-stamped anomalies.
Extending Game Technologies with Virtual Reality and Multi-Agent Systems

Abstract Nowadays, videogames, computer simulations andtraining are very complex applications because usersrequire higher and higher levels of quality. Artiﬁ-cialIntelligenceplaysnowadaysafundamentalroleinvideogames and virtual reality applications. Agentsinteracting with humans must have a credible visualappearance and must maintain user immersion. Themost important issues of user’s interest are both re-alistic visualization and convincing intelligence. Thispaper presents a system that allows a full integrationoftwoareaswhicharebecomingincreasinglyimportantrecently: virtual reality (visualization) and intelligentmulti-agent systems (artiﬁcial intelligence), in a ﬂexi-ble and easily scalable way. This system extends thetechnologicalpossibilitiesofthevideogames. Introduction Computergamedevelopmentcoversmanydiﬀerentar-eas,suchasgraphics,artiﬁcialintelligenceandnetworkcommunications. Nowadays, players demand moresophisticated and credible computer-controlled oppo-nents, but results are sometimes unsatisfactory. Thisisduetotheneedofreal-timeprocessingconstraintstoreach an acceptable feeling of immersion for the user.Sometimes, these high computational costs are solvedby using dedicated hardware, such as graphic cards.However, in the case of artiﬁcial intelligence, there isno speciﬁc hardware developed to help programmersandusers,andoutcomesaresometimespoor. Thede-velopment of systems, tools and development environ-mentscanplayanimportantroleintheprogressofthisﬁeld. Thus, the combination of artiﬁcial intelligencetechniquesandvirtualreality(orvirtualenvironments)has given birth to the ﬁeld of intelligent virtual envi-ronments (IVEs). An IVE is a virtual environmentsimulatingaphysical(orreal)world,inhabitedbyau-tonomousintelligententities(Luck 2000). Theseenti-tieshavetointeractin/withthevirtualenvironmentas if they were real entities in the real world. In ad-dition,entitiesandthevirtualenvironmenthavetobeshowntousersinanappropriateway. Therearesome
The zebrafish dystrophic mutant softy maintains muscle fibre viability despite basement membrane rupture and muscle detachment

The skeletal muscle basement membrane fulfils several crucial functions during development and in the mature myotome and defects in its composition underlie certain forms of muscular dystrophy. A major component of this extracellular structure is the laminin polymer, which assembles into a resilient meshwork that protects the sarcolemma during contraction. Here we describe a zebrafish mutant, softy, which displays severe embryonic muscle degeneration as a result of initial basement membrane failure. The softy phenotype is caused by a mutation in the lamb2 gene, identifying laminin β2 as an essential component of this basement membrane. Uniquely, softy homozygotes are able to recover and survive to adulthood despite the loss of myofibre adhesion. We identify the formation of ectopic, stable basement membrane attachments as a novel means by which detached fibres are able to maintain viability. This demonstration of a muscular dystrophy model possessing innate fibre viability following muscle detachment suggests basement membrane augmentation as a therapeutic strategy to inhibit myofibre loss.
Support Vector Machine and Generalization Takio Kurita

The Support Vector Machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick [1– 3]. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. The SVM uses linear threshold elements to build up two-classes classifier. It learns linear threshold element parameters based on “margin maximization” from training samples. This paper reviews how to enhance generalization in learning classifiers. The SVM is introduced, then multiple regression analysis (MRA) and logistic regression analysis (LRA) are explained as the statistical methods for building up a classifier with a structure similar to that for the SVM. The same method as used for the SVM can be introduced in both MRA and LRA to enhance performance for unlearned samples. This paper reviews how to enhance generalization in classifier learning and compares the SVM with these methods at the criterion function level[4].