Mechanical Properties of Thermoelectric Materials for Practical Applications

Thermoelectric (TE) direct conversion of thermal energy into electricity is a novel renewable energy conversion method currently at a technological readiness level of 3–5 approaching laboratory prototypes. While approaching practical thermoelectric devices, an increase in the thermoelectric element’s efficiency is needed at the entire service temperature range. Yet, the main focus of research was concentrated on the electronic properties of the materials, while research on the mechanical properties was left behind. As it is shown in this chapter, knowing and controlling the mechanical properties of TE materials are paramount necessities for approaching practical TEGs. The material’s elastic constants, strength and fracture toughness are the most crucial parameters for designing of practical devices. The elastic constants provide understanding about the material’s stiffness, while strength provides the loading conditions in which the material will keep its original shape. Knowing the fracture toughness provides the stress envelope in which the material could operate and its susceptibility to inherent fabrication faults. The characterization methods of these properties are varied and may be physical or pure mechanical in nature. It is the authors opinion to prefer the mechanical methods, so the results obtained will describe more accurately the material’s response to mechanical loading

Adolescents’ and young adults’ online risk taking : the role of gist and verbatim representations

Young people are exposed to and engage in online risky activities, such as disclosing personal information and making unknown friends online. Little research has examined the psychological mechanisms underlying young people’s online risk taking. Drawing on Fuzzy Trace Theory, we examined developmental differences in adolescents’ and young adults’ online risk taking and assessed whether differential reliance on gist representations (based on vague, intuitive knowledge) or verbatim representations (based on specific, factual knowledge) could explain online risk taking. One hundred and twenty two adolescents (ages 13-17) and 172 young adults (ages 18-24) were asked about their past online risk taking behaviour, intentions to engage in future risky online behaviour, and gist and verbatim representations. Adolescents had significantly higher intentions to take online risks than young adults. Past risky online behaviours were positively associated with future intentions to take online risks for adolescents and negatively for young adults. Gist representations about risk negatively correlated with intentions to take risks online in both age groups, while verbatim representations positively correlated with online risk intentions, particularly among adolescents. Our results provide novel insights about the underlying mechanisms involved in adolescent and young adults’ online risk taking, suggesting the need to tailor the representation of online risk information to different age groups

Scaling and Universality of the Complexity of Analog Computation

We apply a probabilistic approach to study the computational complexity of analog computers which solve linear programming problems. We analyze numerically various ensembles of linear programming problems and obtain, for each of these ensembles, the probability distribution functions of certain quantities which measure the computational complexity, known as the convergence rate, the barrier and the computation time. We find that in the limit of very large problems these probability distributions are universal scaling functions. In other words, the probability distribution function for each of these three quantities becomes, in the limit of large problem size, a function of a single scaling variable, which is a certain composition of the quantity in question and the size of the system. Moreover, various ensembles studied seem to lead essentially to the same scaling functions, which depend only on the variance of the ensemble. These results extend analytical and numerical results obtained recently for the Gaussian ensemble, and support the conjecture that these scaling functions are universal.Comment: 22 pages, latex, 12 eps fig

Modulated Floquet Topological Insulators

Floquet topological insulators are topological phases of matter generated by the application of time-periodic perturbations on otherwise conventional insulators. We demonstrate that spatial variations in the time-periodic potential lead to localized quasi-stationary states in two-dimensional systems. These states include one-dimensional interface modes at the nodes of the external potential, and fractionalized excitations at vortices of the external potential. We also propose a setup by which light can induce currents in these systems. We explain these results by showing a close analogy to px+ipy superconductors

Physical Passive Patch Adversarial Attacks on Visual Odometry Systems

Deep neural networks are known to be susceptible to adversarial perturbations -- small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model's output on a set of inputs. Universal perturbations present a more realistic case of adversarial attacks, as awareness of the model's exact input is not required. In addition, the universal attack setting raises the subject of generalization to unseen data, where given a set of inputs, the universal perturbations aim to alter the model's output on out-of-sample data. In this work, we study physical passive patch adversarial attacks on visual odometry-based autonomous navigation systems. A visual odometry system aims to infer the relative camera motion between two corresponding viewpoints, and is frequently used by vision-based autonomous navigation systems to estimate their state. For such navigation systems, a patch adversarial perturbation poses a severe security issue, as it can be used to mislead a system onto some collision course. To the best of our knowledge, we show for the first time that the error margin of a visual odometry model can be significantly increased by deploying patch adversarial attacks in the scene. We provide evaluation on synthetic closed-loop drone navigation data and demonstrate that a comparable vulnerability exists in real data. A reference implementation of the proposed method and the reported experiments is provided at https://github.com/patchadversarialattacks/patchadversarialattacks.Comment: Accepted to ACCV 202

Learning with a Drifting Target Concept

We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution. We also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates

Ground-based observations of the relations between lightning charge-moment-change and the physical and optical properties of column sprites

Optical observations of 66 sprites, using a calibrated commercial CCD camera, were conducted in 2009-2010 and 2010-2011 winter seasons as part of the ILAN (Imaging of Lightning And Nocturnal flashes) campaign in the vicinity of Israel and the eastern Mediterranean. We looked for correlations between the properties of parent lightning (specifically, the charge moment change; CMC) to the properties of column sprites, such as the measured radiance, the length and the number of column elements in each sprite event. The brightness of sprites positively correlates with the CMC (0.7) and so does the length of sprite elements (0.83). These results are in agreement with previous studies, and support the QE model of sprite generation. © 2013 Elsevier Ltd

Fast Nonlinear Vector Quantile Regression

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable $\mathrm{Y}$ given explanatory features $\boldsymbol{\mathrm{X}}$. A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard definition for multivariate distributions. Recently, vector quantile regression (VQR) was proposed as an extension of QR for vector-valued target variables, thanks to a meaningful generalization of the notion of quantiles to multivariate distributions via optimal transport. Despite its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target $\boldsymbol{\mathrm{Y}}$ given the features $\boldsymbol{\mathrm{X}}$; (ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles; (iii) no fast or scalable solvers for VQR currently exist. In this work we fully address these limitations, namely: (i) We extend VQR to the non-linear case, showing substantial improvement over linear VQR; (ii) We propose {vector monotone rearrangement}, a method which ensures the quantile functions estimated by VQR are monotone functions; (iii) We provide fast, GPU-accelerated solvers for linear and nonlinear VQR which maintain a fixed memory footprint, and demonstrate that they scale to millions of samples and thousands of quantile levels; (iv) We release an optimized python package of our solvers as to widespread the use of VQR in real-world applications.Comment: 35 pages, 15 figures, code: https://github.com/vistalab-technion/vq

Temperature effects on high strain rate properties of graphite/epoxy composites

A unidirectional graphite epoxy material (AS4/3501-6) was characterized at strain rates ranging from 5 x 10(exp 6) s(exp -1) to 5(exp -1), at room temperature and at 128 C. Results are presented in the form of stress-strain curves to failure. The longitudinal properties remain nearly unchanged with strain rate and temperature. The transverse modulus increases with strain rate but decreases with temperature. The transverse strength and transverse ultimate tensile strain have a positive rate sensitivity at low rates, which changes to negative at intermediate rates and returns to positive rate sensitivity at the highest rates tested. A temperature-time equivalence principle was applied and master curves were obtained for the transverse mechanical properties. The in-plane shear modulus and in-plane shear strength have a positive rate sensitivity. The ultimate intralaminar shear strain has a positive rate sensitivity at low rates, which changes to negative at high rates. At the elevated temperature of 128 C, the ultimate shear strain is 25 to 30 percent higher than the room temperature value, but its strain rate dependence is moderate