Abstract
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of ...considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.
We study the optimal design of graph filters (GFs) to implement arbitrary linear transformations between graph signals. GFs can be represented by matrix polynomials of the graph-shift operator (GSO). ...Since this operator captures the local structure of the graph, GFs naturally give rise to distributed linear network operators. In most setups, the GSO is given so that GF design consists fundamentally in choosing the (filter) coefficients of the matrix polynomial to resemble desired linear transformations. We determine spectral conditions under which a specific linear transformation can be implemented perfectly using GFs. For the cases where perfect implementation is infeasible, we address the optimization of the filter coefficients to approximate the desired transformation. Additionally, for settings where the GSO itself can be modified, we study its optimal design as well. After this, we introduce the notion of a node-variant GF, which allows the simultaneous implementation of multiple (regular) GFs in different nodes of the graph. This additional flexibility enables the design of more general operators without undermining the locality in implementation. Perfect and approximate designs are also studied for this new type of GFs. To showcase the relevance of the results in the context of distributed linear network operators, this paper closes with the application of our framework to two particular distributed problems: finite-time consensus and analog network coding.
Political candidates use Spanish-language appeals in efforts to increase their support among Hispanic voters. We argue that candidates, Hispanic or not, can use Spanish to signal closeness to ...Hispanics and posit that the effectiveness of these appeals is conditional on proficiency. To test this, we run two experiments where participants listen to an audio clip of a hypothetical candidate’s stump speech. We vary the ethnicity of the candidate (Anglo or Hispanic) and the language of the speech (English, non-native Spanish, and native-like Spanish). We find that Hispanic support for the Anglo and Hispanic candidates is higher in the native-like Spanish condition compared with the English-only condition. Relative to the English condition, non-native Spanish does not increase support for the Anglo candidate, but it decreases support for the Hispanic candidate. We find mixed effects for Anglo participants. Our results suggest that candidates can effectively appeal to Hispanic voters using Spanish-language messages.
When non-Hispanic candidates make an appeal in Spanish they are typically labeled as "Hispandering." Some evidence has shown, however, that Hispanics have higher evaluations of candidates who make ...Spanish appeals, regardless of ethnicity. This paper explores how perceptions of pandering are formed among Hispanics. I expand the expectations gap literature by arguing that trait ownership and expectations are relevant for race. Given the expectation for a candidate's ability to perform a certain appeal, perceptions of pandering will be conditioned on the candidate's ability to exceed or fall short of those expectations. I test this idea with Spanish language appeals. I run an experiment where I randomly assign Hispanics to hear a message given by an Anglo or Hispanic candidate where the message is either in English or Spanish. I find support for the expectations gap theory. Anglo candidates, who are not expected to be able to speak Spanish can increase their perceived sincerity by speaking in grammatically correct Spanish. Meanwhile, Hispanic candidates who speak non-native-sounding Spanish have lower evaluations compared to their native-like or English-speaking counterparts. Given the low levels of political trust among Hispanics, I test the implications of perceived pandering on political trust. I run another experiment where I test whether perceiving that one politician panders has spillover effects for other candidates. I find that perceiving pandering decreases trust in that candidate but otherwise pandering had no spillover effect on other candidates' political trust. These studies help illuminate the important dimensions of pandering perceptions.
When facing graph signal processing tasks, it is typically assumed that the graph describing the support of the signals is known. However, in many relevant applications the available graph suffers ...from observational errors and perturbations . As a result, any method that relies on the graph topology and ignores the presence of perturbations may yield suboptimal results. Motivated by this, we propose a novel approach for handling perturbations on the links of the graph and apply it to the problem of robust graph filter (GF) identification from input-output observations. Different from existing works, we formulate a non-convex optimization problem that operates in the vertex domain and jointly performs GF identification and graph denoising. As a result, on top of learning the desired GF, an estimate of the graph is obtained as a byproduct. To handle the resulting bi-convex problem, we design an algorithm that blends techniques from alternating optimization and majorization minimization, showing its convergence to a stationary point. The second part of the paper i) generalizes the design to a robust setup where several GFs are jointly estimated, and ii) introduces an alternative algorithmic implementation that reduces the computational complexity. Finally, the detrimental influence of the perturbations and the benefits resulting from the robust approach are numerically analyzed over synthetic and real-world datasets, comparing them with other state-of-the-art alternatives.
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a ...frequency domain related to the structure of the graph. Most existing formulations focus on estimating an unknown graph signal by observing its value on a subset of nodes. By contrast, in this paper, we study the problem of inducing a known graph signal using as input a graph signal that is nonzero only for a small subset of nodes. The sparse signal is then percolated (interpolated) across the graph using a graph filter. Alternatively, one can interpret graph signals as network states and study graph-signal reconstruction as a network-control problem where the target class of states is represented by bandlimited signals. Three setups are investigated. In the first one, a single simultaneous injection takes place on several nodes in the graph. In the second one, successive value injections take place on a single node. The third one is a generalization where multiple nodes inject multiple signal values. For noiseless settings, conditions under which perfect reconstruction is feasible are given, and the corresponding schemes to recover the desired signal are specified. Scenarios leading to imperfect reconstruction, either due to insufficient or noisy signal value injections, are also analyzed. Moreover, connections with classical interpolation in the time domain are discussed. Specifically, for time-varying signals, where the ideal interpolator after uniform sampling is a (low-pass) filter, our proposed approach and the reconstruction of a sampled signal coincide. Nevertheless, for general graph signals, we show that these two approaches differ. The last part of the paper presents numerical experiments that illustrate the results developed through synthetic and real-world signal reconstruction problems.
Stable and efficient locomotion requires the precise coordination of movement across the limbs and body. Learned changes in interlimb coordination can be induced by exposure to a split-belt treadmill ...that imposes different speeds under each side of the body. Here, we demonstrate locomotor learning on a split-belt treadmill in mice. Mouse locomotor adaptation is specific to measures of interlimb coordination, has spatial and temporal components that adapt at different rates, and is context specific. The many similarities between human and mouse locomotor adaptation suggest that this form of locomotor learning is highly conserved across vertebrates. Using a variety of approaches, we demonstrate that split-belt adaptation in mice specifically depends on the intermediate cerebellum but is insensitive to large lesions of the cerebral cortex. Finally, cell-type-specific chemogenetics combined with quantitative behavioral analysis reveals that spatial and temporal components of locomotor adaptation are dissociable on the circuit level.
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•Locomotor learning on a split-belt treadmill is highly conserved across vertebrates•Mice regain gait symmetry by calibrating interlimb coordination in space and time•Locomotor adaptation requires intermediate cerebellum but not cerebral cortex•Circuit mechanisms for spatial and temporal components of learning are dissociable
Darmohray et al. describe a rapid form of cerebellum-dependent locomotor learning in mice that appears to be highly conserved across vertebrates. Cell-type-specific chemogenetics combined with quantitative behavioral analysis reveals that mechanisms for spatial and temporal components of learning are dissociable on the circuit level.
The food consumption of adolescents has changed nowadays, with an increase in ultra-processed food that in general shows higher calories and lower nutrients. Because of this, the objective of this ...study was to investigate the impacts of a 12-week multi-professional family versus isolated intervention on the food level processing of overweight adolescents. A randomized clinical trial study was carried out in which adolescents (
43; mean aged 13.73 years) who were divided into FG-family group (
21; the adolescents performed the activities with their parents) and IG-isolated group (
22; the adolescents performed the activities alone). The parameters measured before and after 12 weeks of multi-professional intervention (physical exercise, nutrition and psychoeducation) were: body mass, height and body mass index-BMI. The level of food processing was analyzed using a three-day food recall (24hR), classified according to the Food Guide for the Brazilian Population (fresh, minimally, processed and ultra-processed foods). The main results show that there was only a significant reduction in the consumption of processed foods (FG: 7.93%; IG: 49.73%) and ultra-processed foods (FG: 35.06%; IG: 67.16%) in grams (FG: 22.29%; IG: 65.23%) and calories (
0.05; for all comparisons). The consumption of fresh foods in grams (FG:61.97%; IG: 147.13%) and calories (FG: 147.13%; IG: 118.03%) and minimally processed foods (FG: 27.45%; IG: 14.64%) in grams increased significantly (
0.05; for all comparisons). However, no significant differences were observed between all variables analyzed for the groups, nor any interaction (
0.05). In conclusion, both groups who participated in the activities showed positive changes with increased consumption of fresh foods and reduced consumption of processed foods, without difference between them.
Adopting a responsible leadership (RL) lens and drawing on intergroup behavior and social identity theory, we analyze a sample of 111 expatriates using partial least squares structural equation ...modeling (PLS-SEM). Our findings indicate that RL enacted by host-country supervisors is positively associated with international assignees' cross-cultural adjustment and affective well-being at work, in turn leading to better expatriate performance. RL may thus facilitate adjustment and well-being in the host country. We emphasize the support potential when expatriates' supervisors are guided by a strong values foundation and exhibit stakeholder engagement. In this way, we aim to provide a perspective for studying leadership dynamics in the expatriation context that goes beyond leader-follower dyads within organizations. We believe that expatriation research can benefit from adopting a broader stakeholder view on leadership that considers business-society interrelations and outline some direction that might take.
In this paper, an inner loop for the control and frequency regulation of the doubly fed induction generator connected to a dc link through a diode bridge on the stator is presented. In this system, ...the stator is directly connected to the dc link using a diode bridge, and the rotor is fed by only a pulsewidth-modulated (PWM) converter. If compared to the DFIG connected to an ac grid, this system uses one PWM inverter less and a much less expensive diode bridge. Thus, the cost of power electronics is reduced. The application in mind is for dc networks such as dispersed generation grids and microgrids. These networks use several elements that should work together. Usually, these elements are connected with each other by power electronic devices in a common dc link. This paper presents a control system for the inner control loop in order to regulate the torque and the stator frequency. Simulation and experimental results show that the system works properly and is able to keep the stator frequency near the rated value of the machine.
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