Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE METAL RICHNESS OF M DWARFS WITH PLANETS

Knowledge of the metallicities of M dwarfs rests predominantly on the photometric calibration of Bonfils and collaborators, which predicts that M dwarfs in the solar neighborhood, including those with known planets, are systematically metal poor compared to their higher-mass counterparts. We test this prediction using a volume-limited sample of low-mass stars, together with a subset of M dwarfs with high-metallicity, F, G, and K wide binary companions. We find that the Bonfils et al. photometric calibration systematically underestimates the metallicities of our high-metallicity M dwarfs by an average of 0.32 dex. We derive a new photometric metallicity calibration and show that M dwarfs with planets appear to be systematically metal rich, a result that is consistent with the metallicity distribution of FGK dwarfs with planets.
Building Injective Trapdoor Functions From Oblivious Transfer

Injective one-way trapdoor functions are one of the most fundamental cryptographic primitives. In this work we give a novel construction of injective trapdoor functions based on oblivious transfer for long strings. Our main result is to show that any 2-message statistically sender-private semihonest oblivious transfer (OT) for strings longer than the sender randomness implies the existence of injective one-way trapdoor functions. This is perhaps surprising given given the black box separation of injective one-way trapdoor functions from many common cryptographic protocols, e.g. IND-CCA encryption. As a tool for creating injective one-way trapdoor functions, we define a new notion of security for a public key encryption scheme called Randomness Dependent Message (RDM) security, and use it as a stepping stone for creating injective one-way trapdoor functions. Our main result has a number of interesting corollaries: • Applying the results of Mol and Yilek (PKC ’10), we also show that Lossy Encryption with long plaintexts implies correlated product secure functions and INDCCA secure encryption. • Lossy encryption with long plaintexts implies a weak form of RDM security. In addition, Hemenway, Libert, Ostrovsky and Vergnaud (ePrint ’09) showed that statistically-hiding 2-round Oblivious Transfer (OT) is equivalent to Lossy Encryption, where if OT uses randomness shorter than the message so does Lossy Encryption and vice versa. Thus, our main result also implies an injective one-way trapdoor function from any lossy encryption with short randomness. This is somewhat surprising since injective trapdoor functions are deterministic and, given the trapdoor, allow recovery of a complete inverse, while public-key encryptions are probabilistic and recover only the plaintext and not necessarily the randomness used in the encryption process. Our result corroborates the previous result of Bellare, Halevi, Sahai and Vadhan (CRYPTO ’98) showing that IND-CPA secure encryption implies injective one-way trapdoor permutations in the random oracle model. We stress that in our work we do not make use of a random oracle.
Normalized Cuts for Predominant Melodic Source Separation

The predominant melodic source, frequently the singing voice, is an important component of musical signals. In this paper, we describe a method for extracting the predominant source and corresponding melody from ldquoreal-worldrdquo polyphonic music. The proposed method is inspired by ideas from computational auditory scene analysis. We formulate predominant melodic source tracking and formation as a graph partitioning problem and solve it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. Sinusoidal modeling is used as the underlying representation. A novel harmonicity cue which we term harmonically wrapped peak similarity is introduced. Experimental results supporting the use of this cue are presented. In addition, we show results for automatic melody extraction using the proposed approach.
Monoclonal antibody-ricin A chain conjugate selectively cytotoxic for cells bearing the common acute lymphoblastic leukemia antigen.

The toxic subunit of ricin has been conjugated by a disulfide bound to a monoclonal murine antibody (J-5) specific for the common acute lymphoblastic leukemia antigen (CALLA) expressed on human lymphoblastic cells. Both the monoclonal antibody and ricin A chain retained their original biological activity after conjugation. The ricin A chain portion of this conjugate effectively inhibited protein synthesis in a cell-free rabbit reticulocyte lysate system. Its antibody-combining site attached to the membrane of CALLA-bearing cells, whereupon both components of the molecule, antibody and A chain, could be detected using fluorescein-labeled antibody probes directed against either mouse F(ab')2 or ricin A chain. This conjugate proved to be a potent cytotoxin for CALLA-positive Nalm-1 cells growing in vitro and produced 50% inhibition of proliferation at levels of 2 x 10(-10) M. In contrast, CALLA-negative cell lines were unaffected until levels of conjugate approached 10(-6) M. When tested for cytotoxic action on Nalm-1 cells, the composite conjugate molecule was at least 2000 times more effective than J-5 antibody alone, ricin A chain alone, or a noncovalent mixture of these components. Cytotoxicity of the conjugate could be blocked completely by addition of antibodies specific for either mouse immunoglobulin or ricin A chain. Direct comparison of the in vitro cytotoxicity of CALLA-directed univalent Fab versus bivalent F(ab')2 carriers of ricin A chain revealed 70-fold greater efficacy for the latter conjugate. This differential could not be accounted for by differences in A chain activity or binding affinity for the cell membrane but appeared related to the ability of the multivalent agent to induce modulation and internalization of CALLA determinants. No overt toxic effects were noted when the F(ab')2-A chain conjugate was administered i.v. to rabbits at levels as high as 20 mg/kg single injection or 5 mg/kg for 5 days. The unique specificity characteristics and high potency of this cytotoxic conjugate indicate that it has significant therapeutic potential for the treatment of CALLA-bearing human leukemias and lymphomas.
Toward a Cognitive Structure Conceptualization of Product Familiarity

Product familiarity is conceptualized in terms of the cognitive structures of knowledge concerning the product that are stored in memory. Such knowledge could have been derived from direct or indirect experiences with the product in question (e.g., product use vs. advertisements or word-of-mouth). Different amounts and types of product experience should be reflected by variation in cognitive structure. Theoretically, differences in cognitive structures should influence the cognitive processes and outcomes that involve those cognitive structures and, thereby, should also affect overt behavior. Data from an existing study are used to illustrate these ideas. Suggestions for future research are offered.
Peptide-templating dye-sensitized solar cells

A hollow TiO2 nanoribbon network electrode for dye-sensitized solar cells (DSSC) was fabricated by a biotemplating process combining peptide self-assembly and atomic layer deposition (ALD). An aromatic peptide of diphenylalanine was assembled into a three-dimensional network consisting of highly entangled nanoribbons. A thin TiO2 layer was deposited at the surface of the peptide template via the ALD process. After the pyrolysis of the peptide template, a highly entangled nanotubular TiO2 framework was successfully prepared. Evolution of the crystal phase and crystallite size of the TiO2 nanostructure was exploited by controlling the calcination temperature. Finally, the hollow TiO2 nanoribbon network electrode was integrated into DSSC devices and their photochemical performances were investigated. Hollow TiO2 nanoribbon-based DSSCs exhibited a power conversion efficiency of 3.8%, which is comparable to the conventional TiO2 nanoparticle-based DSSCs (3.5%). Our approach offers a novel pathway for DSSCs consisting of TiO2 electrodes via biotemplating.
Association between Pregnancy Loss and Urinary Phthalate Levels around the Time of Conception

Background: Animal studies indicate that some phthalate metabolites may harm female reproductive function. Objectives: We assessed the associations between exposure to phthalate metabolites and pregnancy loss. Methods: Using a previously established cohort of couples planning their first pregnancy, we analyzed four primary and two oxidized secondary phthalate metabolites in urine samples collected on day 10 after the first day of the last menstrual period before conception occurred (n = 128) and during the previous cycle (if any, n = 111). Subclinical embryonal loss was identified by repeated measurement of urinary human chorionic gonadotropin, and information on clinical spontaneous abortions was obtained by telephone interview with the mother. Results: Pregnancy loss (n = 48) was increased among women with urinary concentration of monoethylhexyl phthalate (MEHP) in the upper tertile in the conception sample compared with women in the lowest tertile [adjusted odds ratio (OR) = 2.9; 95% confidence interval (CI): 1.1, 7.6]. The corresponding OR for subclinical embryonal loss (n = 32) was 40.7 (95% CI: 4.5, 369.5). Conclusions: The phthalate metabolite MEHP was associated with higher occurrence of pregnancy loss. Because this is the first human study to show this association and the sample size is small, the findings need to be corroborated in independent studies.
Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.
Le travail intellectuel qualifié entre incorporation et autonomie : Mesure, évaluation, contestation

La notion de mesure suppose un accord univoque sur les evaluations sociales (et donc un systeme de classification) (Hacking, 1999). La notion d’evaluation repose sur un point de vue. Elle doit admettre le desaccord, la difference de point de vue entre employe et employeur (Marx, Boltanski). Ces distinctions analytiques concernent l’evolution du travail dans les grandes organisations vers une individualisation de la tâche et des groupes de projet (Bercot, Pichaut). Ces distinctions concernent la pression a l’evaluation et a la valorisation des professions qualifiees (ie enseignants, urbanistes, concepteurs de reseau. Il s’agit de mettre en exergue, les conflits d’evaluation, les conflits de perception et de logiques sociales, les conflits sur les systemes d’evaluation et modernisation. Il s’agit de voir comment se dessinent ex post des accords et des evolutions sur les systemes de classification du travail intellectuel (rente aristocratique, rente publique, conception de projet, conception de service)
University of Huddersfield Repository Portfolio-based Planning: State of the Art, Common Practice and Open Challenges

In recent years the ﬁeld of automated planning has signiﬁcantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of conﬁguring and exploiting a portfolio of planners to perform better than any individual planner: some recent planning systems based on this idea achieved signiﬁcantly good results in experimental analysis and International Planning Competitions. Such results let us suppose that future challenges of the Automated Planning community will converge on designing different approaches for combining existing planning algorithms. This paper reviews existing techniques and provides an exhaustive guide to portfolio-based planning. In addition, the paper outlines open issues of existing approaches and highlights possible future evolution of these techniques.
Seismic Data Processing Using Nonlinear Prediction and Neural Networks

In this paper a simplified but formalized version of the “human interpreter approach” for model driven seismic interpretative processing is proposed. We consider the task of recognizing as such various seismic events and accurately timing their arrival. The novelty of our approach consists in relating nonlinear prediction and neural networks for analyzing real data sets. A seismic event being inherently unpredictable, the performance of a sensible predictor will be perturbed and this is what we will count on. Based on the generalization power of a neural network, the goal is to allow a certain degree of automatic traceability of the criteria used for the identification and timing of seismic events.
Cooperative maneuvring in close environments among cybercars and dual-mode cars

—This paper describes the results of vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) experiments implementing cooperative maneuvering for three different vehicles driving automatically. The cars used were cybercars from the Institut National de Recherche en Informatique et Automatique (INRIA), (France), which are fully automated road vehicles, and two mass-produced cars—one a Smart Fortwo car from TNO (Netherlands) equipped with additional actuators and sensors and the other a convertible Citroën C3 from IAI (Spain) that uses sensorial information to manage the actuators. The cars communicate by a wireless mesh network over Wi-Fi using the optimized link state routing (OLSR) ad-hoc protocol. The entire communication task is embedded in a small MIPS Linux Box (4G System Cube) that is transparent for the cars. A standard framework was deﬁned with the parameters needed to perform adaptive cruise control (ACC) and intersection maneuvers among the cars, as well as emergency stops via a signal sent by the infrastructure. The experiments were carried out in La Rochelle (France) during the ﬁnal demonstration of the European Union (EU) Cybercars-2 Project.
Materialized view maintenance and integrity constraint checking: trading space for time

We investigate the problem of incremental maintenance of an SQL view in the face of database updates, and show that it is possible to reduce the total time cost of view maintenance by materializing (and maintaining) additional views. We formulate the problem of determining the optimal set of additional views to materialize as an optimization problem over the space of possible view sets (which includes the empty set). The optimization problem is harder than query optimization since it has to deal with multiple view sets, updates of multiple relations, and multiple ways of maintaining each view set for each updated relation.We develop a memoing solution for the problem; the solution can be implemented using the expression DAG representation used in rule-based optimizers such as Volcano. We demonstrate that global optimization cannot, in general, be achieved by locally optimizing each materialized subview, because common subexpressions between different materialized subviews can allow nonoptimal local plans to be combined into an optimal global plan. We identify conditions on materialized subviews in the expression DAG when local optimization is possible. Finally, we suggest heuristics that can be used to efficiently determine a useful set of additional views to materialize.Our results are particularly important for the efficient checking of assertions (complex integrity constraints) in the SQL-92 standard, since the incremental checking of such integrity constraints is known to be essentially equivalent to the view maintenance problem.
Research on Fault Diagnosis in Analog Circuit Based on Wavelet-Neural Network

The fault diagnosis method based on wavelet-neural network in analog circuit was presented. The problems on feature extraction in analog circuit, data pre-processing, training and testing of network, fault pattern classification were all discussed here. Fault features were extracted by circuit simulation, after data preprocessed by wavelet-neural network, which can be constructed into samples aggregation, the samples for training and testing were used to train and test neural network respectively, the uniform pattern samples were used to diagnose faults, the fault diagnosis in analogy circuit was realized effectively. An illustration validated this method
Sensitivity Analysis of the Early Exercise Boundary for American Style of Asian Options

In this paper we analyze American style of floating strike Asian call options belonging to the class of financial derivatives whose payoff diagram depends not only on the underlying asset price but also on the path average of underlying asset prices over some predetermined time interval. The mathematical model for the option price leads to a free boundary problem for a parabolic partial differential equation. Applying fixed domain transformation and transformation of variables we develop an efficient numerical algorithm based on a solution to a non-local parabolic partial differential equation for the transformed variable representing the synthesized portfolio. For various types of averaging methods we investigate the dependence of the early exercise boundary on model parameters.
Chromatin Structure in Situ: The Contribution of DNA Ultrastructural Cytochemistry

Ultrastructural studies conducted in situ using conventional transmission electron microscopy have had relatively little impact on defining the structural organization of chromatin. This is due to the fact that in routine transmission electron microscopy, together with the deoxyribonucleoprotein, many different intermingled substances are contrasted, masking the ultrastructure of chromatin. By selective staining of DNA in thin sections, using the Feulgen-like osmium-ammine reaction, these drawbacks have been overcome and worthwhile data have been obtained both on the gross morphology and the ultrastructural-functional organization of chromatin in situ. In the present study these results are reviewed and discussed in light of recent achievements in both interphase nuclear chromatin compartmentalization in interphase nuclei and in the structural organization of chromatin fibers in transcriptionally active and inactive chromatin.
Pump-priming PASCAL proposal : Large Margin Algorithms and Kernel Methods for Speech Applications

Research on large margin algorithms in conjunctions with kernels methods has been both exciting and successful. While there have been quite a few preliminary successes in applying kernel methods for speech applications, most the research efforts have focused on non-temporal problems such as text classification and optical character recognition (OCR). We propose to design, analyze, and implement learning algorithms and kernels for hierarchical-temporal speech utterances. Our first and primary end-goal is to build and test thoroughly a full-blown speech phoneme classifier that will be trained on millions of examples and will achieve the best results in this domain. We also plan to apply and test the resulting algorithms and kernels to other supervised problems in spoken language such as language identification, word spotting from phonemes, and speaker verification. 1 Background and Motivation We propose an algorithmic research framework for supervised speech analysis problems that builds on recent advances in kernel methods and large margin classifiers. Specific applications include (but are not limited to) speech phoneme classification, spoken language identification, closed vocabulary word recognition/spotting, and speaker verification. Recent work on large margin methods such as support vector machines and boosting algorithms has shown to be effective in many decision tasks. While there has been some work on large margin methods for complex decision problems, most of the research focus still revolves around the less complex structures. Speech signals however exhibit a complex temporal structure in both the input space (acoustic signal) and the target space (e.g. phonetic transcription and word transcription). For instance, speech signals exhibit multi-scale temporal behavior and the set of speech phonemes is organized in an hierarchical structure. The current state-of-the-art models to handle such speech signals are based on generative models that capture some temporal dependencies such as Hidden Markov Models (HMMs). (See for instance [9] and the many references therein.) Numerous reasons have underscored HMMs as the tool of choice in speech signal processing, such as their ability to constrain the space of possible solutions to legal and probable sequences of phonemes/words, through the design of speech-specific Markovian state topologies, as well as their insensitivity to large unbalanced datasets due to their generative nature.
Power gating applied to MP-SoCs for standby-mode power management

Complex SoCs from servers to intelligent sensors are increasingly built up from heterogeneous IP cores and subsystems. Accelerator blocks or additional processor cores support both general purpose and graphics optimized processing in mobile SoCs, but the number of cores that may be simultaneously active is typically restricted for both battery life and thermal package limits. Power gating is the primary approach to cutting the leakage power for inactive blocks, while state retention and standby voltage scaling can be valuable enhancements for improving energy and latency costs for such leakage mitigation schemes. This paper describes work on techniques that look promising to build on current multivoltage EDA tools and power intent, without the costs of resorting to full-custom design techniques.
An LTE module for the ns-3 network simulator

In this paper, we present a novel module developed for the simulation of the LTE technology with the ns-3 simulator. It focuses mainly on modeling the E-UTRA part of the system, with a particular attention on the aspects related to the channel, PHY and MAC layers. First we discuss the overall modeling assumptions according to which the module was designed. Subsequently, we describe in detail the architecture of the module and its components. Finally, we discuss an example simulation scenario.