Specific Assumptions Research Articles

(Re-)defining “animal magnetism”: the mesmerism investigations of 1784

Une historiographie plus ancienne a appréhendé les enquêtes de 1784 sur le « magnétisme animal » comme le triomphe scientifique de la méthode expérimentale. Cet article va réévaluer la portée et l’impact de ces enquêtes et défendre l’idée que les membres des commissions ont altéré le sens de « magnétisme animal » pour convenir à leurs propres objectifs et intérêts. Plus spécifiquement, je vais montrer comment leurs expériences ont traduit leurs interprétations propres du sens, de la définition et de la fonction thérapeutique du magnétisme animal, et comment ces interprétations étaient constamment réévaluées et redéfinies. En premier lieu, je vais discuter de la manière dont les membres des commissions ont fait des sensations les effets visibles principaux du magnétisme. En deuxième lieu, je vais montrer comment les expériences ont appliqué des critères de démarcation sociale aux sensibilités des sujets. En troisième lieu, je vais présenter comment ils ont amélioré la définition de leurs protocoles pour s’assurer de la crédibilité de leur hypothèse selon laquelle l’imagination produisait les sensations magnétiques. Enfin, je vais expliquer comment ils ont redéfini en convulsions les « crises » magnétiques, afin de saper la légitimité de la thérapie.

CONHECIMENTO, CRIATIVIDADE E PRODUTIVIDADE SOB A PERSPECTIVA DA LINGUÍSTICA FUNCIONAL CENTRADA NO USO

ABSTRACT In this article, we discuss the notions of linguistic knowledge, creativity, and productivity as they are conceived in Functional Linguistics and in Construction Grammar, also reviewing, more briefly, these concepts in Generative Grammar. In this sense, we revisit the posture that different linguists adopt in relation to these notions, confronting them and highlighting their divergences. We relate each of the concepts to the specific theoretical assumptions of each model of analysis, also pointing out the evolution that such notions have gone through over time, which results from the development of Linguistics as a science. We characterize the construction and its properties, and under the framework of constructionist models, we relate creativity and productivity to the property of schematicity. Finally, based on Usage-based Functional Linguistics, we examine the transitive and the VAdjADV e VAdj-mente constructions of Brazilian Portuguese to illustrate the role of creativity and productivity and communicative and cognitive factors that motivate new uses of these constructions.

Generalized BSDE and reflected BSDE with random time horizon

Motivated by structural, reduced-form and hybrid models of the third party and counterparty credit risk, we study a generalized backward stochastic differential equations (BSDE) up to a random time horizon ϑ, which is not a stopping time with respect to a reference filtration. In contrast to the existing literature in the area of credit risk modeling, we do not impose specific assumptions on the random time ϑ and we study the existence of solutions to BSDE and reflected BSDE with a random time horizon through the method of reduction. For this purpose, we also examine BSDE and reflected BSDE with a làdlàg driver where the driver is allowed to have a finite number of jumps overlapping with jumps of the martingale part. Theoretical results are illustrated by particular instances of a random time and explicit BSDEs in either the Brownian or Brownian-Poisson filtration.

The notion of a person

The aim of this article is to clarify the content of the concept "person" as it figures in philosophical debates about personhood and personal identity. In order to do so, I will look at both specific philosophical problems that ask for a clear definition of this notion, as well as at the history of this concept's formation, and try to motivate the specific assumptions that are tightly connected to it.

Target Detection With Spatial-Spectral Adaptive Sample Generation and Deep Metric Learning for Hyperspectral Imagery

In hyperspectral target detection, the conventional metric learning-based algorithms provide unique advantages in detecting targets as they do not require specific assumptions and adapt to the condition of limited training samples. Nevertheless, they usually learn a linear transformation for metric space, which is unable to capture nonlinear mapping where the hyperspectral imageries possess, especially occurs in the spectra variability and nonlinear mixing problems. To alleviate this limitation, this study investigates a new spatial-spectral adaptive sample generation and deep metric learning-based method for hyperspectral target detection (denoted as DMLTD). The proposed DMLTD employs a spatial-spectral adaptive sample generation strategy and subpixel synthetic method for background sample generation and target sample augmentation, respectively. With sufficient samples, the proposed DMLTD trains a deep discriminative metric learning network to learn hierarchical nonlinear mappings, so that to address the spectra variability and nonlinear mixing problems, thus exploiting discriminative information between targets and backgrounds for detection. Experiments and analyses conducted on three real-world hyperspectral datasets indicate that our DMLTD yields competitive performance in hyperspectral image target detection.

Advanced GNC-oriented modeling and simulation of Vertical Landing vehicles with fuel slosh dynamics

This paper introduces a flexible framework to enable high-fidelity simulations of the fuel slosh dynamics for verification of the Guidance, Navigation and Control (GNC) algorithms. The sloshing phenomenon must be tackled during the control system design phase and the GNC software run and validated within appropriate simulation frameworks. Equivalent mechanical models can well approximate sloshing under specific assumptions, allowing the derivation of the multibody vehicle’s equations of motion; however, while suitable for analysis and control synthesis purposes, this approach presents several limitations for implementation as a simulation model. In this work, the multibody plant embedding slosh dynamics is modeled by means of the DLR’s Vertical Landing Vehicles Library (VLVLib) written using the object-oriented Modelica modeling language. The advantages of using Modelica are explored throughout the paper. Given that control synthesis, analyses and simulation campaigns are usually performed in the Matlab/Simulink environment, a core point of the proposed solution is to achieve a good synergy within the latter and the Modelica plant model with the least readaptation burden for the developer. The strategies to achieve a full integration and verification tool chain are illustrated. The potential of this approach is demonstrated via two different test cases: a lunar landing scenario of the ALINA spacecraft developed by PTS, and CALLISTO reusable rocket demonstrator.

Remote Electronic Voting in Uncontrolled Environments: A Classifying Survey

Remote electronic voting, often called online or Internet voting, has been subject to research for the last four decades. It is regularly discussed in public debates, especially in the context of enabling voters to conveniently cast their ballot from home using their personal devices. Since these devices are not under the control of the electoral authority and could be potentially compromised, this setting is referred to as an “uncontrolled environment” for which special security assumptions have to be considered. This paper employs general election principles to derive cryptographic, technical, and organizational requirements for remote electronic voting. Based on these requirements, we have extended an existing methodology to assess online voting schemes and develop a corresponding reference attacker model to support the preparation of tailored protection profiles for different levels of elections. After presenting a broad survey of different voting schemes, we use this methodology to assess and classify those schemes comparatively by leveraging four election-specific attacker models.

A generalization of Bang’s lemma

We prove a common extension of Bang’s and Kadets’ lemmas for contact pairs, in the spirit of the Colourful Carathéodory Theorem. We also formulate a generalized version of the affine plank problem and prove it under special assumptions. In particular, we obtain a generalization of Kadets’ theorem. Finally, we give applications to problems regarding translative and homothetic coverings.

Finite element simulation of an upliftable rigid frame bridge under earthquakes: Experimental verification

Under strong earthquakes, the shallow foundation bridge would experience partial or temporary separation of foundation from the support, hereafter termed as uplift. The uplift can activate rigid-like body movements and cut off the energy transfer of earthquake motions to the upper structure. However, most of the previous studies focus on the theoretical analysis using over simplified rigid body models, and some analyses often have applicable limitations because of their specific assumptions. Experimental research using a shaking table is time-consuming and expensive. In this paper, a finite element model (FEM) of an upliftable rigid frame bridge is developed and verified by a series of shaking table tests. The experimental bridge model is designed according to a similarity of 1:10 based on a viaduct in Auckland. The model has two kinds of foundation forms: fixed foundation and upliftable foundation, the corresponding FEM of the bridges with a fixed and an upliftable foundation is established. In the numerical investigation, the behavior of rocking of the structure due to the uplift between the foundation and the support was considered by Winkler springs. The proposed FEM was verified through the shaking table test results. It shows that the FEM is consistent with the experimental results and can be used to evaluation the displacement, acceleration and bending moment of bridges with an upliftable foundation.

SMEFTs living on the edge: determining the UV theories from positivity and extremality

We study the “inverse problem” in the context of the Standard Model Effective Field Theory (SMEFT): how and to what extend can one reconstruct the UV theory, given the measured values of the operator coefficients in the IR? The main obstacle of this problem is the degeneracies in the space of coefficients: a given SMEFT truncated at a finite dimension can be mapped to infinitely many UV theories. We discuss these degeneracies at the dimension-8 level, and show that positivity bounds play a crucial role in the inverse problem. In particular, the degeneracies either vanish or become significantly limited for SMEFTs that live on or close to the positivity bounds. The UV particles of these SMEFTs, and their properties such as spin, charge, other quantum numbers, and interactions with the SM particles, can often be uniquely determined, assuming dimension-8 coefficients are measured. The allowed region for SMEFTs, which forms a convex cone, can be systematically constructed by enumerating its generators. We show that a geometric notion, extremality, conveniently connects the positivity problem with the inverse problem. We discuss the implications of a SMEFT living on an extremal ray, on a k-face, and on the vertex of the positive cone. We also show that the information of the dimension-8 coefficients can be used to set exclusion limits on all individual UV states that interact with the SM, independent of specific model assumptions. Our results indicate that the dimension-8 operators encode much more information about the UV than one would naively expect, which can be used to reverse engineer the UV physics from the SMEFT.

Device Selection and Beamforming Optimization in Large-Scale mmWave IoT Networks

The joint provision of higher data rates and massive Internet of Things (IoT) connectivity has been identified as one of the key milestones toward beyond 5G (B5G). To this end, we investigate the issue of device selection and beamforming (BF) optimization assuming a large-scale IoT network using mmWaves. We formulate the considered problem as a network sum-rate maximization problem under Access Points’ load constraints, and where the BF parameters belong to discrete sets, as in practical cases. First, we mathematically prove the submodularity of the objective function, under specific yet reasonable assumptions. Based on the identified features of the problem at hand, we propose three different approaches to tackle this intricate optimization problem: 1) a Branch-and-Bound-based; 2) a Lagrangian Relaxation-based; and 3) a Greedy-based approach inspired by the submodular objective. The numerical results validate the three approaches, as they achieve a near-optimal sum rate in small network cases, and largely outperform benchmark schemes in terms of sum rate and individual rates. Among them, the proposed Greedy-based approach achieves the best sum rate with very low complexity, thereby providing excellent scalability.

Analysis of Existing Physical Theories Explaining Aerodynamic Lift Production by an Airplane

Since the first successful airplane, invented by The Wright brothers in the early 1900s, air travel has become one of the most common modes of transportation. While airplane production and use rapidly increase, explanations regarding the physics of flight remain elusive due to major shortcomings. With all the research available, this paper aims to explore how accurately current theories have explained lift production by an airplane's wing. The contents of this paper will touch on the subject of physics, more specifically fluid dynamics and aerodynamics, and even more specifically, the aerodynamic force known as lift. Theories analyzed in this paper include the "Equal Transit", "Skipping Stone", "Venturi", Momentum-Based, Bernoulli-Based, and Circulation theory. Overall, limitations leading to the inaccuracy of these explanations include not providing physical explanations for specific assumptions, misusing and misinterpreting principles and theorems, simplifying explanations thus not accounting for all necessary factors, proposing one-way causation relationships, confusing mathematical theories for qualitative physical theories, and conflicting experimental data. Because of this, an additional purpose of this paper is to provide a more accurate and comprehensive explanation for lift.

Об истоках множественности логик

The article presents a brief analysis of how the existence of various logics became possi­ble. This is shown on the example of such well-known logical theories as syllogistics, temporal, multivalued, intuitionistic, paraconsistent and quantum logics. Each of them arose not on someone’s whim, but to solve specific problems. They are based on the most general ontological assumptions about the subject area under study. In formal logic onto­logical assumptions are refined in the concept of a model structure. Since it is impossible to talk about logic in isolation from the language used, the most general epistemic as­sumptions about the nature of the relationship of linguistic expressions to those objects of extralinguistic reality that they represent are also accepted. One of the most important of these relationships is the concept of the truth of sentences, which was first formulated by Plato and Aristotle. Taking certain ontological and epistemic assumptions depending on the problem being solved, we obtain different logics. Process logic is primarily char­acterized by special ontological assumptions that are fundamentally different from the as­sumptions of other currently existing logics. The ontology of processes is an ontology of developing processes, not things. Historically, it was most vividly described in the writings of Heraclitus. In the overwhelming majority of modern approaches to the de­scription of processes, we see attempts to reduce them to sequences of states, which de­values the very concept of a process, just as a cinematic picture of the flow of time deval­ues the concept of time. Since logics are built on the basis of various ontological and epistemic assumptions, they are inherently theories of these accepted assumptions, and not universal reasoning tools that don’t depend on the characteristics of the study area and the categories of linguistic expressions. Universal logic is possible if one rises from the level of specific languages to a higher level of sign theory.

Event-triggered model-free adaptive control for unknown multiple input and multiple output nonlinear system under denial of service attacks

The event-triggered model-free adaptive control problem for a class of multiple input and multiple output (MIMO) nonlinear system under denial of service (DoS) attacks is investigated in this paper. To economize the limited bandwidth resources, an event-triggered mechanism is designed between the sensor and controller. First, intermittent denial of service attack is modeled, which is described as limited by frequency and duration, without more specific assumptions about the structure of attacks strategies. Then, with the help of the dynamic linearization method, a novel event-triggered model-free adaptive control algorithm is designed. The predictive output increments in the algorithm are used to compensate for the influence of DoS attacks on the system. Subsequently, the stability of the system is analyzed by using the Lyapunov method to determine the convergence of the tracking error. Finally, two simulation examples illustrate the validity of the design.

A public resource of baseline data from the Alzheimer's Prevention Initiative Autosomal-Dominant Alzheimer's Disease Trial.

The Alzheimer's Prevention Initiative Autosomal-Dominant Alzheimer's Disease (API ADAD) Trial evaluated the anti-oligomeric amyloid beta (Aβ) antibody therapy crenezumab in cognitively unimpaired members of the Colombian presenilin 1 (PSEN1) E280A kindred. We report availability, methods employed to protect confidentiality and anonymity of participants, and process for requesting and accessing baseline data. We developed mechanisms to share baseline data from the API ADAD Trial in consultation with experts and other groups sharing data from Alzheimer's disease (AD) prevention trials, balancing the need to protect anonymity and trial integrity with making data broadly available to accelerate progress in the field. We pressure-tested deliberate and inadvertent potential threats under specific assumptions, employed a system to suppress or mask both direct and indirect identifying variables, limited and firewalled data managers, and put forth specific principles requisite to receive data. Baseline demographic, PSEN1 E280A and apolipoprotein E genotypes, florbetapir and fluorodeoxyglucose positron emission tomography, magnetic resonance imaging, clinical, and cognitive data can now be requested by interested researchers. Baseline data are publicly available; treatment data and biological samples, including baseline and treatment-related blood-based biomarker data will become available in accordance with our original trial agreement and subsequently developed Collaboration for Alzheimer's Prevention principles. Sharing of these data will allow exploration of important questions including the differential effects of initiating an investigational AD prevention therapy both before as well as after measurable Aβ plaque deposition.

Direct collapse of exceptionally heavy black holes in the merger-driven scenario

ABSTRACT We revisit the conditions present in supermassive discs (SMDs) formed by the merger of gas-rich, metal-enriched galaxies at redshift z ∼ 10. We find that SMDs naturally form hydrostatic cores which go through a rapidly accreting supermassive star phase, before directly collapsing into massive black holes via the general relativistic instability. The growth and collapse of the cores occurs within ∼5 × 105 yr from the formation of the SMD, producing bright electromagnetic, neutrino and gravitational wave transients with a typical duration of a few minutes and, respectively, a typical flux and a typical strain amplitude at Earth of ∼10−8 erg s−1 cm−2 and ∼4 × 10−21. We provide a simple fitting formula for the resulting black hole masses, which range from a few 106 to 108 M⊙ depending on the initial SMD configuration. Crucially, our analysis does not require any specific assumption on the thermal properties of the gas, nor on the angular momentum loss mechanisms within the SMD. Led by these findings, we argue that the merger-driven scenario provides a robust pathway for the rapid formation of supermassive black holes at z > 6. It provides an explanation for the origin of the brightest and oldest quasars without the need of a sustained growth phase from a much smaller seed. Its smoking gun signatures can be tested directly via multimessenger observations.

Evolvability and constraint in the evolution of three-dimensional flower morphology.

Flower phenotypes evolve to attract pollinators and to ensure efficient pollen transfer to and from the bodies of pollinators or, in self-compatible bisexual flowers, between anthers and stigmas. If functionally interacting traits are genetically correlated, response to selection may be subject to genetic constraints. Genetic constraints can be assessed by quantifying standing genetic variation in (multivariate) phenotypic traits and by asking how much the available variation is reduced under specific assumptions about phenotypic selection on functionally interacting and genetically correlated traits. We evaluated multivariate evolvability and potential genetic constraints underlying the evolution of the three-dimensional structure of Dalechampia blossoms. First, we used data from a greenhouse crossing design to estimate the G matrix for traits representing the relative positions of male and female sexual organs (anthers and stigmas) and used the G matrix to ask how genetic variation is distributed in multivariate space. To assess the evolutionary importance of genetic constraints, we related standing genetic variation across phenotypic space to evolutionary divergence of population and species in the same phenotypic directions. Evolvabilities varied substantially across phenotype space, suggesting that certain traits or trait combinations may be subject to strong genetic constraint. Traits involved functionally in flower-pollinator fit and autonomous selfing exhibited considerable independent evolutionary potential, but population and species divergence tended to occur in phenotypic directions associated with greater-than-average evolvability. These results are consistent with the hypothesis that genetic constraints can hamper joint trait evolution towards optimum flower-pollinator fit and optimum self-pollination rates.

On the relation of GNSS phase center offsets and the terrestrial reference frame scale: a semi-analytical analysis

Phase center offsets (PCOs) of global navigation satellites systems (GNSS) transmit antennas along the boresight axis introduce line-of-sight-dependent range changes in the modeling of GNSS observations that are strongly correlated with the estimated station heights. As a consequence, changes in the adopted PCOs impact the scale of GNSS-based realizations of the terrestrial reference frame (TRF). Vice versa, changes in the adopted TRF scale require corrections to the GNSS transmit antenna PCOs for consistent observation modeling. Early studies have determined an approximate value of alpha =-0.050 for the ratio of station height changes and satellite PCO changes in GPS orbit determination and phase center adjustment. However, this is mainly an empirical value and limited information is available on the actual PCO-scale relation and how it is influenced by other factors. In view of the recurring need to adjust the IGS antenna models to new ITRF scales, a semi-analytical model is developed to determine values of alpha for the four current GNSSs from first principles without a need for actual network data processing. Given the close coupling of satellite boresight angle and station zenith angle, satellite PCO changes are essentially compensated by a combination of station height, zenith troposphere delay, and receiver clock offset. As such, the value of alpha depends not only on the orbital altitude of the considered GNSS but also on the elevation-dependent distribution of GNSS observations and their weighting, as well as the elevation mask angle and the tropospheric mapping function. Based on the model, representative values of alpha _text {GPS}=-0.051, alpha _text {GLO}=-0.055, alpha _text {GAL}=-0.041, and alpha _text {BDS-3}=-0.046 are derived for GPS, GLONASS, Galileo, and BeiDou-3 at a 10^circ elevation cutoff angle. These values may vary by varDelta alpha approx 0.003 depending on the specific model assumptions and data processing parameters in a precise orbit determination or precise point positioning. Likewise changes of about pm 0.003 can be observed when varying the cutoff angle between 5^circ and 15^circ .

Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting.

Semisupervised learning (SSL) has been extensively studied in related literature. Despite its success, many existing learning algorithms for semisupervised problems require specific distributional assumptions, such as "cluster assumption" and "low-density assumption," and thus, it is often hard to verify them in practice. We are interested in quantifying the effect of SSL based on kernel methods under a misspecified setting. The misspecified setting means that the target function is not contained in a hypothesis space under which some specific learning algorithm works. Practically, this assumption is mild and standard for various kernel-based approaches. Under this misspecified setting, this article makes an attempt to provide a theoretical justification on when and how the unlabeled data can be exploited to improve inference of a learning task. Our theoretical justification is indicated from the viewpoint of the asymptotic variance of our proposed two-step estimation. It is shown that the proposed pointwise nonparametric estimator has a smaller asymptotic variance than the supervised estimator using the labeled data alone. Several simulated experiments are implemented to support our theoretical results.

안전하고 효과적인 자율주행을 위한 불확실성 순차 모델링

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.