Miracle-dz/UniFAR-FTUs huggingface dataset:
Message Authentication Code over a wiretap channel

Message Authentication Code (MAC) is a keyed function fK such that when Alice, who shares the secret K with Bob, sends fK(M) to the latter, Bob will be assured of the integrity and authenticity of M. Traditionally, it is assumed that the channel is noiseless. Unfortunately, Maurer showed that in this case an attacker can succeed with probability equation after authenticating ∓ messages, where H(K) is the entropy of K. In this paper, we consider the setting where the channel is noisy. Specifically, Alice and Bob are connected by a discrete memoryless channel (DMC) W1 and a noiseless but insecure channel. In addition, there is a DMC W2 between Alice and attacker Oscar. We regard the noisy channel as an expensive resource and define the authentication rate ρauth as the ratio of message length to the number n of channel W1 uses. The security of this model depends on the channel coding for fK(M). A natural coding scheme is to use the secrecy capacity achieving code of Csiszár and Körner. Intuitively, this is also the optimal strategy. However, we propose a coding scheme that achieves a higher ρauth. Our crucial point is that under a secrecy capacity code, Bob can fully recover fK(M) while in our model this is not necessary as we only need to detect the existence of the modification. How to detect the malicious modification without recovering fK(M) is the main contribution of this work. We achieve this through random coding techniques.
The cognitive antecedents of empathic responding

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Nonstationary Motor Fault Detection Using Recent Quadratic Time–Frequency Representations

As the use of electric motors increases in the aerospace and transportation industries where operating conditions continuously change with time, fault detection in electric motors has been gaining importance. Motor diagnostics in a nonstationary environment is difficult and often needs sophisticated signal processing techniques. In recent times, a plethora of new time-frequency distributions has appeared, which are inherently suited to the analysis of nonstationary signals while offering superior frequency resolution characteristics. The Zhao-Atlas-Marks distribution is one such distribution. This paper proposes the use of these new time-frequency distributions to enhance nonstationary fault diagnostics in electric motors. One common myth has been that the quadratic time-frequency distributions are not suitable for commercial implementation. This paper also addresses this issue in detail. Optimal discrete-time implementations of some of these quadratic time-frequency distributions are explained. These time-frequency representations have been implemented on a digital signal processing platform to demonstrate that the proposed methods can be implemented commercially.
ON SUPERVISED DENSITY ESTIMATION TECHNIQUES AND THEIR APPLICATION TO CLUSTERING

The basic idea of traditional density estimation is to model the overall point density analytically as the sum of influence functions of the data points. However, traditional density estimation techniques only consider the location of a point. Supervised density estimation techniques, on the other hand, additionally consider a variable of interest that is associated with a point. Density in supervised density estimation is measured as the product of an influence function with the variable of interest. Based on this novel idea, a supervised density-based clustering named SCDE is introduced and discussed in detail. The SCDE algorithm forms clusters by associating data points with supervised density attractors which represent maxima and minima of a supervised density function. Results of experiments are presented that evaluate SCDE for hot spot discovery and co-location discovery in spatial datasets. Moreover, the benefits of the presented approach for generating thematic maps are briefly discussed.
Fractional programming through genetic algorithm

This paper intends to demonstrate use of Genetic Algorithm for solving fractional programming and which can be extended for DEA. Genetic Algorithm is one of the non-traditional algorithms for solving optimization problems. The multivariable fraction may have multiple optimum points. Genetic algorithm does not run the risk of getting trapped into the local minimum or maximum. The traditional optimization algorithms have difficulty in computing the derivatives and second order partial derivatives for fractional form. Though there are numerical algorithms but they become computationally intensive. The issues of discontinuity seriously affect traditional algorithms. The genetic algorithm may not be very efficient but a generalized way to find optimal points of multivariate fractional function. Two short and simple experiments have been conducted to illustrate the positions. In the second illustration effect of crossover position on the gain of objective function has been studied.
Towards a Logic-Based Unifying Framework for Computing

In this paper we propose a logic-based, framework inspired by artificial intelligence, but scaled down for practical database and programming applications. Computation in the framework is viewed as the task of generating a sequence of state transitions, with the purpose of making an agent's goals all true. States are represented by sets of atomic sentences (or facts), representing the values of program variables, tuples in a coordination language, facts in relational databases, or Herbrand models. In the model-theoretic semantics, the entire sequence of states and events are combined into a single model-theoretic structure, by associating timestamps with facts and events. But in the operational semantics, facts are updated destructively, without timestamps. We show that the model generated by destructive updates is identical to the model generated by reasoning with facts containing timestamps. We also extend the model with intentional predicates and composite event predicates defined by logic programs containing conditions in first-order logic, which query the current state.
RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments

MOTIVATION During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts. RESULTS In this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis. AVAILABILITY AND IMPLEMENTATION RNASurface C source code is available from http://bioinf.fbb. msu.ru/RNASurface/downloads.html.
Low-Phase-Noise 54-GHz Transformer-Coupled Quadrature VCO and 76-/90-GHz VCOs in 65-nm CMOS

This paper presents new circuit topologies and design techniques for low-phase-noise (PN) complementary metal-oxide-semiconductor (CMOS) millimeter-wave quadrature voltage-controlled oscillator (QVCO) and VCOs. A transformer-coupled QVCO topology with extra phase shift is proposed to replace the coupling transistors, which eliminates coupling transistors' noise, decouples the tradeoff between PN and phase error, and improves the PN performance. This technique is demonstrated in a millimeter-wave QVCO with a measured PN of -119.2 dBc/Hz at 10-MHz offset of a 56.2-GHz carrier and a tuning range of 9.1%. In addition, an inductive-divider-feedback technique is proposed in an LC VCO design to improve the transconductance linearity, resulting in a larger signal swing and lower PN compared with the conventional LC VCOs. The effectiveness of this approach is demonstrated in a 76and a 90-GHz VCO design, both fabricated in a 65-nm CMOS process, with an FOMT of 173.6 and 173.1 dBc/Hz, respectively.
Model validation of discretized spatial closed elastica

A closed elastica, which is an elastic rod whose both ends are fixed to prescribed two places, can be found in various situations in robotics. In this paper, we investigate validity of the discretized spatial model of a closed elastica. The discretized closed elastica model can be regarded as a series of rigid bodies connected with passive elastic joints physically, and represented by a set of nonlinear equations derived by discretizing differential equations with boundary conditions as well as Euler equations expressing local minimum elastic energy. The validity of the considered model was studied by comparing the measured shape and the calculated shape from the model. Experimental results using a laser position sensor for shape measurement show that good correspondence between measured and calculated shapes, which supports the validity of the considered model. The constrained force and torque at the tip of an elastica, which can be obtained simultaneously associated with shape calculation, are also verified from measurement by a 6-axes force/torque sensor.
SPITZER SAGE INFRARED PHOTOMETRY OF MASSIVE STARS IN THE LARGE MAGELLANIC CLOUD

We present a catalog of 1750 massive stars in the Large Magellanic Cloud (LMC), with accurate spectral types compiled from the literature, and a photometric catalog for a subset of 1268 of these stars, with the goal of exploring their infrared properties. The photometric catalog consists of stars with infrared counterparts in the Spitzer SAGE survey database, for which we present uniform photometry from 0.3 to 24 μm in the UBVIJHKs+IRAC+MIPS24 bands. The resulting infrared color–magnitude diagrams illustrate that the supergiant B[e], red supergiant, and luminous blue variable (LBV) stars are among the brightest infrared point sources in the LMC, due to their intrinsic brightness, and at longer wavelengths, due to dust. We detect infrared excesses due to free–free emission among ∼900 OB stars, which correlate with luminosity class. We confirm the presence of dust around 10 supergiant B[e] stars, finding the shape of their spectral energy distributions (SEDs) to be very similar, in contrast to the variety of SED shapes among the spectrally variable LBVs. The similar luminosities of B[e] supergiants (log L/L☉ ⩾ 4) and the rare, dusty progenitors of the new class of optical transients (e.g., SN 2008S and NGC 300 OT), plus the fact that dust is present in both types of objects, suggests a common origin for them. We find the infrared colors for Wolf–Rayet stars to be independent of spectral type and their SEDs to be flatter than what models predict. The results of this study provide the first comprehensive roadmap for interpreting luminous, massive, resolved stellar populations in nearby galaxies at infrared wavelengths.
Recursive restartability: turning the reboot sledgehammer into a scalpel

Even after decades of software engineering research, complex computer systems still fail, primarily due to nondeterministic bugs that are typically resolved by rebooting. Conceding that Heisenbugs will remain a fact of life, we propose a systematic investigation of restarts as "high availability medicine. " In this paper we show how recursive restartability (RR) - the ability of a system to gracefully tolerate restarts at multiple levels improves fault tolerance, reduces time-to-repair and enables system designers to build flexible, highly available software infrastructures. Using several examples of widely deployed software systems, we identify properties that are required of RR systems and outline an agenda for turning the recursive restartability philosophy into a practical software structuring tool. Finally, we describe infrastructural support for RR systems, along with initial ideas on how to analyze and benchmark such systems.
Modelling of a retinal ganglion cell with simple spiking models

Modelling aspects of the human vision system, including the retina, is difficult due to insufficient knowledge about the internal components, organisation and complexity of the interactions within the system. Retinal ganglion cells are considered a core component of the human visual system as they convey the accumulated data as action potentials onto the optic nerve. Current techniques capable of mapping this input-output response involve computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. This paper aims to model a retinal ganglion cell with a simple spiking neuron combined with a pre-processing method, which accounts for the preceding retinal neural structure. Performance of the models is compared with the spike responses obtained in the electrophysiological recordings from a mammalian retina subjected to visual stimulation.
Prenatal mercury exposure and infant birth weight in the Norwegian Mother and Child Cohort Study

Abstract Objective To examine the association between calculated maternal dietary exposure to Hg in pregnancy and infant birth weight in the Norwegian Mother and Child Cohort Study (MoBa). Design Exposure was calculated with use of a constructed database of Hg in food items and reported dietary intake during pregnancy. Multivariable regression models were used to explore the association between maternal Hg exposure and infant birth weight, and to model associations with small-for-gestational-age offspring. Setting The study is based on data from MoBa. Subjects The study sample consisted of 62 941 women who answered a validated FFQ which covered the habitual diet during the first five months of pregnancy. Results Median exposure to Hg was 0·15 μg/kg body weight per week and the contribution from seafood intake was 88 % of total Hg exposure. Women in the highest quintile compared with the lowest quintile of Hg exposure delivered offspring with 34 g lower birth weight (95 % CI −46 g, −22 g) and had an increased risk of giving birth to small-for-gestational-age offspring, adjusted OR = 1·19 (95 % CI 1·08, 1·30). Although seafood intake was positively associated with increased birth weight, stratified analyses showed negative associations between Hg exposure and birth weight within strata of seafood intake. Conclusions Although seafood intake in pregnancy is positively associated with birth weight, Hg exposure is negatively associated with birth weight. Seafood consumption during pregnancy should not be avoided, but clarification is needed to identify at what level of Hg exposure this risk might exceed the benefits of seafood.
Using Distributional Semantics for Automatic Taxonomy Induction

Semantic taxonomies are powerful tools that provide structured knowledge to Natural Language Processing (NLP), Information Retreval (IR), and general Artificial Intelligence (AI) systems. These taxonomies are extensively used for solving knowledge rich problems such as textual entailment and question answering. In this paper, we present a taxonomy induction system and evaluate it using the benchmarks provided in the Taxonomy Extraction Evaluation (TExEval2) Task. The task is to identify hyponym-hypernym relations and to construct a taxonomy from a given domain specific list. Our approach is based on a word embedding, trained from a large corpus and string-matching approaches. The overall approach is semi-supervised. We propose a generic algorithm that utilizes the vectors from the embedding effectively, to identify hyponym-hypernym relations and to induce the taxonomy. The system generated taxonomies on English language for three different domains (environment, food and science) which are evaluated against gold standard taxonomies. The system achieved good results for hyponym-hypernym identification and taxonomy induction, especially when compared to other tools using similar background knowledge.
Recognition and Verification of Design Patterns

In this paper we consider the automatic discovery of design (programming) patterns. While patterns have surfaced as an effective mechanism for authoring and understanding compelx software, popular languages lack facilities for direct specification of patterns or verification of pattern usage in program specifications. Static analysis for patterns is provably undecidable; we focus on discovery and verification of patterns by analyzing dynamic sequences of method calls on object. We show a proof-of-concept of our approach by presenting the results of analyzing a Java program for Iterator patterns.
New Approach to Financial Time Series Forecasting - Quantum Minimization Regularizing BWGC and NGARCH Composite Model

A hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) is used for resolving the overshooting phenomenon significantly; however, it may lose the localization once volatility clustering occurs. Thus, we propose a compensation to deal with the time-varying variance in the residual errors, that is, incorporating a non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC, and quantum minimization (QM) is employed to regularize the smoothing coefficients for both BWGC and NGARCH to effectively improve model’s robustness as well as to highly balance the generalization and the localization.
Getting real with real options

We apply a utility-based method to obtain the value of a finite-time investment opportunity when the underlying real asset is not perfectly correlated to a traded financial asset. Using a discrete-time algorithm to calculate the indifference price for this type of real option, we present numerical examples for the corresponding investment thresholds, in particular highlighting their dependence with respect to correlation and risk aversion.
CHAPTER 5 Sorafenib in Non-Small Cell Lung Cancer : results of clinical trials

Non-small lung cancer (NSCLC) patients with metastatic disease have a poor prognosis and platinum-based chemotherapy is the only treatment option in the majority of these patients. There is a great need for new targeted therapies in this complex and heterogeneous disease. Sorafenib is a multi-target tyrosine kinase inhibitor (TKI), which inhibits proteins involved in proliferation and angiogenesis and could be an attractive agent for patients with NSCLC. Sorafenib is studied in firstand second-line treatment as mono and combination therapy. In phase III trials, sorafenib failed to show improvement in survival as first-line treatment in addition to chemotherapy and as monotherapy in third or fourth line. Even detrimental effects were observed in patients with squamous histology. Still, a subgroup of patients derive long-term benefit from treatment with sorafenib. To identify these patients, biomarker studies have been conducted, but no predictive biomarker has yet been found. Combination therapy of sorafenib with other targeted agents is currently being studied.
Prosodic features for automatic text-independent evaluation of degree of nativeness for language learners

Predicting the degree of nativeness of a student utterance is an important issue in computer-aided language learning. This task has been addressed by many studies focusing on the segmental assessment of the speech signal. To achieve improved correlations between human and automatic nativeness scores, other aspects of speech should also be considered, such as prosody. The goal of this study is to evaluate the use of prosodic information to help predict the degree of nativeness of pronunciation, independent of the text. A supervised strategy based on human grades is used in an attempt to select promising features for this task. Preliminary results show improvements in the correlation between human and automatic scores.
A Dynamic Model of the French Economy, with Rational Expectations, Monopolistic Competition and Labour Market Bargaining

This paper describes a small dynamic model of the French economy in a situation of imperfect competition on all markets, with some nominal rigidity and part of households facing a liquidity constraint. Economic agents optimise over time. One of the features of this model is that the labour market is modeled in some detail. Dynamic and long-term multipliers are computed, under rational expectations, for changes in fiscal, para fiscal and budgetary policy, for alterations to the environment of France, and for structural shocks. These multipliers are given economic interpretation.
Model Theory with Applications to Algebra and Analysis: Model theory and Kähler geometry

We survey and explain some recent work at the intersection of model theory and bimeromorphic geometry (classification of compact complex manifolds). Included here are the essential saturation of the many-sorted structure C of Kähler manifolds, the conjectural role of hyperkähler manifolds in the description of strongly minimal sets in C, and Campana’s work on the isotriviality of hyperkähler families and its connection with the nonmultidimensionality conjecture.
Collaborative filtering recommendation algorithm based on both user and item

Based on the analysis of the sparse problem and the cold start problem in the traditional collaborative filtering recommendation, a new collaborative filtering recommendation algorithm based on adaptive nearest neighbor selection is proposed. The algorithm considers the influence factors of user characteristics and item attributes, and then calculates the nearest neighbor sets of target users and target projects by using the score similarity model. According to the situation of the sparse score data, the similarity measurement results of two aspects are handled by the adaptive coordination factors, so as to get the final project forecast score. Experiments show that the proposed algorithm can effectively balance the instability effects based on the user group score and the recommendation based on the item group, and effectively alleviate the problems caused by the data sparsity. The experimental results show our method can increase the data density and achieve lower MAE (Mean Absolute Error), that is to say, the proposed approach can efficiently improve recommendation quality.