Miracle-dz/UniFAR-FTUs huggingface dataset:
LICS: Logic in Computer Security -- Some Attacker's Models and Related Decision Problems

Logic plays an important role in formal aspects of computer security, for instance in access control, security of communications or even intrusion detection. The peculiarity of security problems is the presence of an attacker, whose goal is to break the intended properties of a system/database/protocol. In this tutorial, we will consider several attacker's models and study how to find attacks (or to get security guarantees) on communication protocols in these different models.
CXC‐chemokine/CXCR2 biological axis promotes angiogenesis in vitro and in vivo in pancreatic cancer

Angiogenesis is essential for tumor growth and metastasis. Although ELR+‐CXC‐chemokines and their corresponding receptor, CXC‐receptor 2 (CXCR2), are known mediators of angiogenesis, little is known about their role in pancreatic cancer (PaCa). The aim of our study was to determine the role of ELR+‐CXC‐chemokine/CXCR2 biological axis in promoting PaCa angiogenesis. We prospectively collected secretin‐stimulated exocrine pancreatic secretions (SSEPS) from normal individuals (NP) and PaCa patients. We showed that summed concentrations of ELR+‐CXC‐chemokines in SSEPS from PaCa patients were significantly higher than in those from NP (p = 0.002). We measured ELR+‐CXC‐chemokine levels in supernatants from multiple PaCa cell lines and confirmed that BxPC‐3, Colo‐357 and Panc‐28 had significantly higher expression compared with an immortalized human pancreatic ductal epithelial (HPDE) cell line. After confirming lack of autocrine effects of ELR+‐CXC‐chemokines on PaCa cells (due to absence of CXCR2 expression), we investigated paracrine effects of these chemokines on human umbilical vein endothelial cells (HUVEC). Both recombinant ELR+‐CXC‐chemokines and co‐culturing with BxPC‐3 significantly enhanced proliferation, invasion, and tube formation of HUVEC (p < 0.05). These biological effects were significantly inhibited by treatment with a neutralizing antibody against CXCR2 (anti‐CXCR2 Ab) (p < 0.05). Finally, anti‐CXCR2 Ab significantly reduced tumor volume (p < 0.05), Ki‐67 proliferation index (p = 0.043) and Factor VIII+ microvessel density (p = 0.004) in an orthotopic nude mouse PaCa model. Our results show that ELR+‐CXC‐chemokines promote PaCa tumor‐associated angiogenesis through CXCR2, suggesting that CXCR2 is an anti‐angiogenic target in PaCa. © 2009 UICC
Measurement of total hepatic low density lipoprotein receptor levels in the hamster.

The ability to measure the total concentration of low density lipoprotein (LDL) receptors in hepatic tissues is of crucial importance to understanding changes in hepatic cholesterol metabolism. Such measurements can be made in conjunction with estimates of LDL receptor transcriptional activity, cell surface LDL receptor number, and rates of hepatic LDL uptake to evaluate the mechanisms controlling cellular LDL receptor expression. Current methods for assessing hepatic LDL receptor levels use microsomes as a source of LDL receptor, and thus rely on consistent contamination of the microsomal preparation with LDL receptor-containing plasma membranes, endocytic vesicles, and/or secretory vesicles. Because this contamination is variable, and may vary with alterations in either the distribution of LDL receptors among the various cellular membrane fractions or in the composition of the intracellular membranes, measurement of LDL receptor concentration in microsomal fractions may not accurately reflect the total compliment of LDL receptors within the cell. We have developed the methodology for isolating the full complement of hepatic LDL receptor containing membranes by discontinuous sucrose density gradient centrifugation, and for quantitating LDL receptor concentration using a Western immunoblotting procedure that uses an anti-C-terminal LDL receptor peptide polyclonal antiserum and assesses the intensity of color formation by reflectance densitometry. Using this methodology, we observed a 126 kDa immunoreactive band for the bovine adrenal cortex LDL receptor that also exhibited LDL binding activity as visualized by biotinylated LDL-ligand blotting, and a doublet of 140 kDa for the hamster liver LDL receptor. These bands were not observed when ligand blotting was conducted in the presence of either 10 mM EDTA or a 5-fold excess of unlabeled LDL, or when immunoblotting was conducted using either preimmune serum or antiserum that had been preabsorbed with LDL receptor peptide. The intensity of color formation was a linear function of the amount of membrane extract separated by electrophoresis. Intra-assay variation averaged 7%, and inter-animal variation averaged 20%. Cholestyramine, tiqueside, CP-88488, 17 alpha-ethinyl estradiol, mevinolin, and the combination of cholestyramine plus mevinolin, pharmacological interventions known to increase LDL receptor activity in experimental animals, produced the predicted increases in hamster total hepatic LDL receptor concentration that were highly correlated with concomitant increases in HMG-CoA reductase activity and reductions in serum cholesterol. (ABSTRACT TRUNCATED AT 400 WORDS)
Improving spectral bounds for clustering problems by Lagrangian relaxation

Clustering is one of the most important issues in data mining, image segmentation, VLSI design, parallel computing and many other areas. We consider the general problem of partitioning n points into k clusters by maximizing the affinity measure of the points into the clusters. This objective function, referred to as Ratio Association, generalizes the classical (Minimum) Sum-of-Squares clustering problem, where the affinity is measured as closeness in the Euclidean space. This generalized version has emerged in the context of the approximation of chemical conformations for molecules, and in explaining transportation phenomena in dynamical systems, especially in dynamical astronomy. In particular, we refer to the dynamical systems application in the paper. Although successful heuristics have been developed to approximately solve the problem, the conventional spectral bounds proposed in the literature are not tight enough for “large” instances to assert the quality of those heuristics or to allow solving the problem exactly. In this paper, we investigate how to tighten the spectral bounds by using Lagrangian relaxation and Subgradient optimization methods.
Functional imaging of the auditory processing applied to speech sounds

In this paper, we describe domain-general auditory processes that we believe are prerequisite to the linguistic analysis of speech. We discuss biological evidence for these processes and how they might relate to processes that are specific to human speech and language. We begin with a brief review of (i) the anatomy of the auditory system and (ii) the essential properties of speech sounds. Section 4 describes the general auditory mechanisms that we believe are applied to all communication sounds, and how functional neuroimaging is being used to map the brain networks associated with domain-general auditory processing. Section 5 discusses recent neuroimaging studies that explore where such general processes give way to those that are specific to human speech and language.
A Model of Plausibility

Plausibility has been implicated as playing a critical role in many cognitive phenomena from comprehension to problem solving. Yet, across cognitive science, plausibility is usually treated as an operationalized variable or metric rather than being explained or studied in itself. This article describes a new cognitive model of plausibility, the Plausibility Analysis Model (PAM), which is aimed at modeling human plausibility judgment. This model uses commonsense knowledge of concept-coherence to determine the degree of plausibility of a target scenario. In essence, a highly plausible scenario is one that fits prior knowledge well: with many different sources of corroboration, without complexity of explanation, and with minimal conjecture. A detailed simulation of empirical plausibility findings is reported, which shows a close correspondence between the model and human judgments. In addition, a sensitivity analysis demonstrates that PAM is robust in its operations.
Model Checking Social Network Models

A social network service is a platform to build social relations among people sharing similar interests and activities. The underlying structure of a social networks service is the social graph, where nodes represent users and the arcs represent the users' social links and other kind of connections. One important concern in social networks is privacy: what others are (not) allowed to know about us. The "logic of knowledge" (epistemic logic) is thus a good formalism to define, and reason about, privacy policies. In this paper we consider the problem of verifying knowledge properties over social network models (SNMs), that is social graphs enriched with knowledge bases containing the information that the users know. More concretely, our contributions are: i) We prove that the model checking problem for epistemic properties over SNMs is decidable; ii) We prove that a number of properties of knowledge that are sound w.r. t. Kripke models are also sound w.r. t. SNMs; iii) We give a satisfaction-preserving encoding of SNMs into canonical Kripke models, and we also characterise which Kripke models may be translated into SNMs; iv) We show that, for SNMs, the model checking problem is cheaper than the one based on standard Kripke models. Finally, we have developed a proof-of-concept implementation of the model-checking algorithm for SNMs.
Death Pathways Mediate Suppression via Programmed Cell Cutting Edge: Regulatory T Cells Do Not

Regulatory T cells (Tregs) play a critical role in the immune system to regulate peripheral tolerance and pre-vent autoimmunity. However, the relative importance of different mechanisms of Treg function remains ob-scure. In this article, we reveal a limited role for programmed cell death pathways in mediating Treg suppression of conventional T cells. We show that Tregs are able to suppress the proliferation of conventional T cells that are resistant to apoptosis (Bim 2 / 2 , Bim 2 / 2 Puma 2 / 2 , Bcl-2 transgenic) or receptor-in-teracting serine-threonine kinase-dependent necrosis (also referred to as regulated necrosis or necroptosis) (Ripk3 2 / 2 ) in several in vitro and in vivo assays. These data suggest that programmed cell death pathways, such as apoptosis and receptor-interacting serine-threonine kinase-dependent necrosis, are not required for Treg-mediated suppression. The Journal of Immunology , 2011, 187: 000–000.
Cloning and Characterization of Human Siglec-11

Siglecs are sialic acid-recognizing animal lectins of the immunoglobulin superfamily. We have cloned and characterized a novel human molecule, Siglec-11, that belongs to the subgroup of CD33/Siglec-3-related Siglecs. As with others in this subgroup, the cytosolic domain of Siglec-11 is phosphorylated at tyrosine residue(s) upon pervanadate treatment of cells and then recruits the protein-tyrosine phosphatases SHP-1 and SHP-2. However, Siglec-11 has several novel features relative to the other CD33/Siglec-3-related Siglecs. First, it binds specifically to α2–8-linked sialic acids. Second, unlike other CD33/Siglec-3-related Siglecs, Siglec-11 was not found on peripheral blood leukocytes. Instead, we observed its expression on macrophages in various tissues, such as liver Kupffer cells. Third, it was also expressed on brain microglia, thus becoming the second Siglec to be found in the nervous system. Fourth, whereas the Siglec-11 gene is on human chromosome 19, it lies outside the previously described CD33/Siglec-3-related Siglec cluster on this chromosome. Fifth, analyses of genome data bases indicate that Siglec-11 has no mouse ortholog and that it is likely to be the last canonical human Siglec to be reported. Finally, although Siglec-11 shows marked sequence similarity to human Siglec-10 in its extracellular domain, the cytosolic tail appears only distantly related. Analysis of genomic regions surrounding the Siglec-11 gene suggests that it is actually a chimeric molecule that arose from relatively recent gene duplication and recombination events, involving the extracellular domain of a closely related ancestral Siglec gene (which subsequently became a pseudogene) and a transmembrane and cytosolic tail derived from another ancestral Siglec.
Leveraging geometric correlation for input-adaptive facial landmark regression

Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions on controlled facial images can successfully capture the appearance variations, the performance of these appearance-based models are tightly related to the quantity and quality of the training data. In this paper, we develop a novel framework, named geometric correlated landmark regression (GCLR), to inherit the advantages but overcome limitations of these two categories of methods. Specifically, we first establish a landmark-to-landmark regression to estimate the geometry of facial images. By further incorporating a sparse coding term into the regression framework, we can successfully leverage the geometric correlations between the test image and the shape dictionary, thus significantly enhance the geometry regression performance. Experimental results on various challenging facial data sets verify the effectiveness and efficiency of GCLR.
Time-varying market price of risk in the CAPM: Approaches, empirical evidence and implications

Time-varying risk premia traditionally have been associated with the empirical fact that conditional second moments are time-varying. This paper additionally examines another possible source for time-varying risk premia, namely the market price of risk (lambda). For utility functions that do not imply constant risk aversion measures, the market price of risk will in general change over time. We provide empirical evidence for the German stock market in a bivariate GARCH-M framework using alternative specifications for lambda. The results indicate that a model with lambda being a function of typical volatility measures performs best for most series. To facilitate the interpretation of the results, we plot impulse response functions of the risk premia.
THE COURSE OF MULTIPLE SCLEROSIS

A review of 73 cases of multiple sclerosis (Table 1) followed during years by the same examiner is presented. The selection of patients is based only upon the readiness of the patient to continhe to co-operate. The duration of observation time is from 3 to 15 years, making in all 515 patient-years 7 years on an average. The patient was examined every 2. to 4. months, a complete neurological examination being performed and the patient asked for changes in the subjective state and the environment.
A ruthenium antimetastasis agent forms specific histone protein adducts in the nucleosome core.

Keywords: bioinorganic chemistry ; cancer ; histone binding ; nucleosomes ; Rapta-C ; ruthenium ; Mass-Spectrometry ; Anticancer Metallodrugs ; Complexes ; Chromatin ; Binding ; Dna ; Particle ; Chemotherapy ; Selectivity ; Inhibition Reference EPFL-ARTICLE-171621doi:10.1002/chem.201100298View record in Web of Science Record created on 2011-12-16, modified on 2017-05-12
Testing for Speculative Behavior in US Corn Ethanol Investments

Crude oil price speculation during 2000s could have increased installed capacity in corn ethanol plants beyond what was warranted by the market factors. We use Muth’s commodity pricing model and Flood and Garber’s tests to test for speculative investment in US corn ethanol industry. The ethanol price expectations are derived using a system of supply-demand-inventory describing US ethanol markets under rational expectations (perfect foresight). These price expectations can help differentiate the installed capacity into two: capacity supported by the market fundamentals and the probable capacity that is installed based on speculation. Econometric estimation procedures and functional form approximations are discussed.
FPGA-Based Fault Injection into Synthesizable Verilog HDL Models

This paper presents an FPGA-based fault injection tool, called FITO that supports several synthesizable fault models for dependability analysis of digital systems modeled by Verilog HDL. Using the FITO, experiments can be performed in real-time with good controllability and observability. As a case study, an OpenRISC 1200 microprocessor was evaluated using an FPGA circuit. About 4000 permanent, transient, and SEU faults were injected into this microprocessor. The results show that the FITO tool is more than 79 times faster than a pure simulation-based fault injection with only 2.5% FPGA area overhead.
Data-Driven Contextual Valence Shifter Quantification for Multi-Theme Sentiment Analysis

Users often write reviews on different themes involving linguistic structures with complex sentiments. The sentiment polarity of a word can be different across themes. Moreover, contextual valence shifters may change sentiment polarity depending on the contexts that they appear in. Both challenges cannot be modeled effectively and explicitly in traditional sentiment analysis. Studying both phenomena requires multi-theme sentiment analysis at the word level, which is very interesting but significantly more challenging than overall polarity classification. To simultaneously resolve the multi-theme and sentiment shifting problems, we propose a data-driven framework to enable both capabilities: (1) polarity predictions of the same word in reviews of different themes, and (2) discovery and quantification of contextual valence shifters. The framework formulates multi-theme sentiment by factorizing the review sentiments with theme/word embeddings and then derives the shifter effect learning problem as a logistic regression. The improvement of sentiment polarity classification accuracy demonstrates not only the importance of multi-theme and sentiment shifting, but also effectiveness of our framework. Human evaluations and case studies further show the success of multi-theme word sentiment predictions and automatic effect quantification of contextual valence shifters.
Crystal structure of tris(1,10-phenanthroline-κ2 N,N′)iron(II) bis[bis(trifluoromethylsulfonyl)imide] monohydrate

The crystal structure of the title complex, [Fe(C12H8N2)3][(CF3SO2)2N]2·H2O, is constructed by one octa-hedral [Fe(phen)3](2+) (phen = 1,10-phenanthroline) cation (point group symmetry 2), two Tf2N(-) [bis-(tri-fluoromethyl-sulfon-yl)imide] anions, and one water mol-ecule of crystallization (point group 2). The Fe-N bond lengths are indicative of a d (6) low-spin state for the Fe(II) ion in the complex. The dihedral angle between the phen ligands in the cation is 87.64 (6)°. The Tf2N(-) counter-anion is non-coordinating, with the -CF3 groups arranged in a trans fashion with respect to each other, leading to an anti,anti conformation of the -CF3 groups and -SO2N- moieties relative to the S-C bonds. The water mol-ecule of crystallization connects two O atoms of the Tf2N(-) anions through weak hydrogen bonds. C-H⋯O hydrogen-bonding inter-actions are also observed, consolidating the packing of the mol-ecules into a three-dimensional network structure.
Impact of Allowance Submissions in European Carbon Emission Markets

This paper studies the impact of the April allowance submissions mandate under the European Union emission trading scheme (EU ETS) in carbon emission markets. Using intraday order flow data, we test for the cross-market efficiency of spot-futures dynamics and find that the equilibrium level, adjustment speed and no-arbitrage boundaries of the spot and futures relationship shift subsequent to the submission date. In addition, our results show that the allowance submissions affect the price discovery process, with the carbon spot market providing stronger price leadership in the periods before the submission date and the futures market playing the predominant informational role thereafter. Using the heterogeneous autoregressive realized volatility (HAR-RV) model, we also find a change in volatility spillovers before the submission date, particularly from the spot to the futures market. Overall, the results suggest that the April allowance submissions have significant impact on the time series dynamics of spot and futures carbon emission markets.
uDirect: A novel approach for pervasive observation of user direction with mobile phones

In this paper we present the uDirect algorithm as a novel approach for mobile phone centric observation of a user's facing direction, through which the device and user orientations relative to earth coordinate are estimated. While the device orientation estimation is based on accelerometer and magnetometer measurements in standing mode, the unique behavior of measured acceleration during stance phase of a human's walking cycle is used for detecting user direction. Furthermore, the algorithm is independent of initial orientation of the device which gives the user higher space of freedom for long term observations. As the algorithm only relies on embedded accelerometer and magnetometer sensors of the mobile phone, it is not susceptible to shadowing effect as GPS. In addition, by performing independent estimations during each step of walking the model is robust to error accumulation. Evaluating the algorithm with 180 data samples from 10 participates has empirically confirmed the assumptions of our analytical model about the unique characteristics of the human stance phase for direction estimation. Moreover, our initial inspection has shown a system based on our algorithm outperforms conventional use of GPS and PCA analysis based techniques for walking distances more than 2 steps.
Probabilistic Attributed Hashing

Due to the simplicity and efficiency, many hashing methods have recently been developed for large-scale similarity search. Most of the existing hashing methods focus on mapping low-level features to binary codes, but neglect attributes that are commonly associated with data samples. Attribute data, such as image tag, product brand, and user profile, can represent human recognition better than low-level features. However, attributes have specific characteristics, including high-dimensional, sparse and categorical properties, which is hardly leveraged into the existing hashing learning frameworks. In this paper, we propose a hashing learning framework, Probabilistic Attributed Hashing (PAH), to integrate attributes with low-level features. The connections between attributes and low-level features are built through sharing a common set of latent binary variables, i.e. hash codes, through which attributes and features can complement each other. Finally, we develop an efficient iterative learning algorithm, which is generally feasible for large-scale applications. Extensive experiments and comparison study are conducted on two public datasets, i.e., DBLP and NUS-WIDE. The results clearly demonstrate that the proposed PAH method substantially outperforms the peer methods.
Searching for primitive roots in finite fields

Let GF(pn) be the finite field with pn elements, where p is prime. We consider the problem of how to deterministically generate in polynomial time a subset of GF(pn) that contains a primitive root, i.e., an element that generates the multiplicative group of nonzero elements in GF(pn) . We present three results. First, we present a solution to this problem for the case where p is small, i.e., p = n-d) . Second, we present a solution to this problem under the assumption of the Extended Riemann Hypothesis (ERH) for the case where p is large and n = 2. Third, we give a quantitative improvement of a theorem of Wang on the least primitive root for GF(p), assuming the ERH.