Miracle-dz/UniFAR-FTUs huggingface dataset:
CONSEQUENCES OF SPACE SHARING ON INDIVIDUAL

20 In heterogeneous habitats, individuals sharing a larger part of their home-range are also 21 likely to live in a very similar environment. This ‘common environment’ effect can 22 generate phenotypic similarities between neighbours and lead to the structuring of 23 phenotypes through the habitat. In this study, we used an intensely monitored population 24 of hihi (or stitchbird, Notiomystis cincta) from New Zealand, to assess whether home-range 25 overlap and genetic relatedness between birds could generate phenotypic resemblance for 26 a wide panel of morphological and life-history traits. Using a multiple-matrix animal model 27 approach to partition the phenotypic variance present in the population, we included a 28 spatial matrix measuring home range overlap between birds and estimated the proportion 29 of variance attributable to space sharing. We detected a clear contribution of space sharing 30 to the overall phenotypic similarity for two traits: hatchling mass and laying date. We also 31 confirmed the very low estimates of genetic heritability already found for this species. 32 These results suggest that models including space sharing can offer further insight into the 33 determinants of individual differences in phenotype. In particular, the spatial matrix helps 34 to capture fine-scale variation of the environment that classic animal models would 35 potentially miss or miss-assign. In this species, results also suggest that small but significant 36 genetic heritability estimates are not upwardly biased by clustering of close relatives in 37 space. 38 39
An Empirical Investigation of Representation Learning for Imitation

Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting expert demonstrations can be expensive. Recent work in vision, reinforcement learning, and NLP has shown that auxiliary representation learning objectives can reduce the need for large amounts of expensive, task-specific data. Our Empirical Investigation of Representation Learning for Imitation (EIRLI) investigates whether similar benefits apply to imitation learning. We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites. In the settings we evaluate, we find that existing algorithms for image-based representation learning provide limited value relative to a well-tuned baseline with image augmentations. To explain this result, we investigate differences between imitation learning and other settings where representation learning has provided significant benefit, such as image classification. Finally, we release a well-documented codebase which both replicates our findings and provides a modular framework for creating new representation learning algorithms out of reusable components.
High efficiency, phase-locked operation of the harmonic-multiplying inverted gyrotwystron oscillator

The inverted gyrotwystron (phigtron) is a millimeter wave frequency-doubling amplifier that has been demonstrated to produce over 300 kW peak power at twice the input frequency (centered at f/sub in/=16.85 GHz and f/sub out/=33.7 GHz) over a 0.5% bandwidth with a saturated gain of 30 dB and efficiency greater than 35%. The device has also been studied both theoretically and experimentally in a different operating regime where frequency-doubled, phase-locked oscillation is possible. A signal, injected via a fundamental gyro-traveling wave tube input section, modulated a 55 kV, 10 A electron beam. After transit through a drift section, the prebunched electron beam produced phase-locked, second harmonic oscillations in a TE/sub 03/ mode output cavity. RF output centered at either of two frequencies, 34.42 and 34.62 GHz, with a maximum output power of 180 kW, an efficiency of 32% and a locked signal gain of 35 dB was measured. A theoretical prediction of locking bandwidth, a design overview, and the experimental results are presented followed by a summary and discussion of the results.
Poor and Rational: Decision-Making under Scarcity

We investigate the link between poverty and decision-making in a sample of farmers in Zambia, who were given the opportunity to exchange randomly assigned household items for alternative items of similar value. Analyzing a total of 5,842 trading decisions and leveraging multiple sources of variation in financial constraints, we show that exchange asymmetries decrease in magnitude when participants are more constrained. This result is robust to experimental procedures and is not mediated by changes in cognitive performance. Consistent with the interpretation that scarcity leads to more rational decisions by increasing the utility loss from forgone trading, we show that trading probabilities go up when the market value of the items is exogenously increased.
Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution

This paper presents a novel image dataset with high intrinsic ambiguity and a long-tailed distribution built from the database of Pl@ntNet citizen observatory. It consists of 306,146 plant images covering 1,081 species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology: (i) the dataset has a strong class imbalance , i
A mechanical approach to overcome RF MEMS switch stiction problem

MEMS switches provide high isolation when open and low insertion loss vhen closed. They also Iuve the advantages of low powr consumption. Thcrefore, RF MEMS switches are an attractive solution to switch antenna bands and transmit/rcceive switching for future multi-band. high bandwidth cell phones. However: Stiction is a major concern for resistive sn-itchcs with metal-to-mctal contact. An iterative-coupled electrostatic-structuml analysis is utilized to evaluate the effect of design parameters on restoring force of MEMS switches. Parameters including mctal thickness, diclcctric thickness. beam-to-ground gap height: metal and dielectric width. and cantilcvcr beam length can be evaluated. The clcctrostatic force is first calculated based on the clcctrical field component. A strnctural analysis is then performed to determine the cantilever beam deflection due to the electrostatic force. A unique integrated empiricalnumerical method is used to qualitativel? - dctcrminc the stiction force bascd on measured actuation \'ohages for real dcviccs. The analysis can provide quick evaluation and screenings of proposed designs to dctcnnine if their actuation voltage falls in the acceptable range. Simulation prediction agrees ver? well with test measurements. Although increasing cantilever thickncss and shortening cantilever length both increasc restoring force, the actuation vollage will increase significantly as a result. The most favorable moditication is to increase the clcctrode area. A short and wide smctnre with a large area can increase rcstoring force while maintaining low actuation voltage. Compared to similar bi-layer designs, sandwich designs can be actuated at fulther reduced voltages without changing the beam restoring force. In addition, the sandwich structure: being thermal-stress-balanced, is less sensitive to temperature excursion. With the properly selected design parameters, the new designs will be able to achieve the break awav restoring force of the original design at much lower actuation voltages. Switches with good electrical as well as mechanical performances have been successfully fabricated.
Overconﬁdence, Strategic Failures, and Experience: An Experiment on Persistent Speculative Trade

Individual investors are known to lower their portfolio returns by trading speculatively, thus incurring transaction fees. In this paper, I use a laboratory experiment to uncover why they do so persistently. In particular, I use experience as a criterion to compare two behavioral channels that may incentivize speculative trading: overconﬁdence and strategic naivete. Subjects in the experiment observe private information and then decide whether to swap Arrow securities with a partner. A no-trade theorem applies to the setting so that under rational expectations, trade should never be realized. I show that experience can reduce speculation by reducing overconﬁdence, but fails to correct strategic naivete: experienced subjects continue to ignore the adverse selection implied by their partners’ willingness to trade. This result is most salient for subjects with high-quality information, who trade more frequently after learning their information reliably predicts the state of the world but do not choose to trade less when learning their partners’ information is similarly high-quality. After revealing their partners’ private information about the state of the world— thus removing the role of strategic naivete—I ﬁnd subjects are less willing to trade and are more responsive to their partners’ information quality. My results suggest individual investors lose from trading because they fail to consider the information driving others’ trading decisions and repeated experience does not fully correct this bias. The experiment’s four between-session and toggle these two levers (showing subjects signal accuracies, showing subjects partners’ signal realizations) to how experience interacts with overconﬁdence and strategic naivete. In the baseline treatment SN (show nothing), neither accuracies nor partners’ signal realizations are revealed. In treatment SA (show accuracies), each subject observes her own signal accuracy and her partner’s signal accuracy. In treatment SS (show signals), each subject observes her own signal realization and her partner’s signal realization. In treatment SAS (show accuracies and signals), subjects observe both types of information.
Testing a Process Model of Causal Reasoning With Inhibitory Causal Links

In this paper, we test people’s causal judgments when the graphs have inhibitory causal relations. We find evidence that a particularly important class of errors known as Markov violations extend to these settings. These Markov violations are important because they are incompatible with causal graphical models, a theoretical framework that is often used as a computational level account of causal cognition. In contrast, the systematic pattern of errors are in line with the predictions of a recently proposed rational process model that models people as reasoning about concrete cases (Davis & Rehder, 2020). These findings demonstrate that errors in causal reasoning extend across a range of settings, and do so in line with the predictions of a model that describes the process by which causal judgments are drawn.
System security enhancement against voltage collapse via coordinated control

In this paper, the problem of analytically coordinating dissimilar voltage control actions to prevent voltage collapse in a large power system is addressed. A framework for hybrid control based on coordination of controls with different response time and dynamic characteristics is presented. The proposed method is based on a security constrained steady-state approach. The minimum distance from the operating point to the bifurcation boundary is used to evaluate system security. The optimal corrective control direction towards adequate security is then obtained by calculating the sensitivity of the minimum distance with respect to controls. The actual dispatch of controls along the optimal direction, which takes into account impacts of economic cost and control availability, is determined as the solution of a multiple stage optimization problem using differential dynamic programming. The algorithm is demonstrated for a system with dynamic load models representing the main grid of the New South Wales, Australia.
Estimating Bicycle Usage on a National Cycle Network

This paper describes the methodology and results from the 1999–2002 Veloland Schweiz surveys of the Swiss national cycle network, using an intercept survey technique, supplemented by cyclist counts and trackside interviews, to obtain information on the magnitude and characteristics of cyclist flows on the Veloland Schweiz national cycling routes. The project used a method of weighting and expanding the sample data obtained from the survey sites to obtain estimates of annual trips and kilometers traveled for catchment areas around each survey site and for the network as a whole. These expansion weights account for the response to the survey and to the spatial and temporal sampling involved in the survey. They also account for weather conditions on the days when the sample surveys were conducted compared with the average weather during the survey year. Finally, cycling-related expenditures associated with trips made on the national network are estimated. Although this method was developed in the context of a national network of cycle routes, it is considered to be applicable at other geographic scales including regional and citywide cycle networks.
Morphological , isotopic and proteomic study of the Pleistocene and Holocene fauna of

The study of large Pleistocene and Holocene mammal fauna is one of the main evidences for the reconstruction of Quaternary ecosystems. The Pleistocene is characterised by extreme climatic oscillations of short duration, on the scale of millennia (Sánchez-Goñi & Harrison, 2010) whose infl uence on the distribution of mammal faunas has been extensively studied (e.g., Hewitt, 2000; Stewart & Lister, 2001; García-Vázquez et al., 2019). Each climatic cooling episode, and the subsequent advance of glacial fronts, led to the arrival on the Iberian Peninsula of new faunal communities that would fi nd in the Cantabrian Mountains, parallel to the coast and at a short distance from the current sea level, a signifi cant geographical barrier to their expansion. However, the OPEN ACCESS RESEARCH PAPER Spanish Journal of Palaeontology 36 (2), 2021 https://doi.org/10.7203/sjp.36. 2.21870 Sociedad Española de Paleontología ISSN 2255-0550 / eISSN 2660-9568
A level set methodology for predicting the effect of mask wear on surface evolution of features in abrasive jet micro-machining

A previous implementation of narrow-band level set methodology developed by the authors was extended to allow for the modelling of mask erosive wear in abrasive jet micro-machining (AJM). The model permits the prediction of the surface evolution of both the mask and the target simultaneously, by representing them as a hybrid and continuous mask–target surface. The model also accounts for the change in abrasive mass flux incident to both the target surface and, for the first time, the eroding mask edge, that is brought about by the presence of the mask edge itself. The predictions of the channel surface and eroded mask profiles were compared with measurements on channels machined in both glass and poly-methyl-methacrylate (PMMA) targets at both normal and oblique incidence, using tempered steel and elastomeric masks. A much better agreement between the predicted and measured profiles was found when mask wear was taken into account. Mask wear generally resulted in wider and deeper glass target profiles and wider PMMA target profiles, respectively, when compared to cases where no mask wear was present. This work has important implications for the AJM of complex MEMS and microfluidic devices that require longer machining times.
Tackling Sexual Harassment: Experimental Evidence from India∗

Sexual harassment imposes substantial economic costs on the victims, yet there is limited evidence on how to effectively deter it. I present experimental evidence on the effects of a sexual harassment awareness training for college students in New Delhi, using a randomized controlled trial. I find that sexual harassment awareness training for men reduces sexual harassment reported by women in their peer groups by 0.06 standard deviations. However, the training also reduces inter-personal relationships between men and women. I find that this is driven by women’s choices, using a lab-in-the-field experiment in which women prefer to cooperate with women rather than men on an experimental task. Using a theoretical framework of signalling, I show that this is consistent with some men undertaking “good” behaviours even though they would prefer to harass women, to avoid disapproval from their peers. Empirically, I find that there is an increase in men’s perception of peer disapproval against sexual harassment and no change in their intrinsic attitudes towards it, consistent with the theoretical predictions. I cannot reject a null effect on sexual harassment and opposite sex relationships of a similar intervention that was delivered exclusively to women in a separate college. Thus, this paper shows that it is possible to engage men for women’s empowerment. JEL codes: D91, J16, J28, K42, O12.
Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning

Many subﬁelds of machine learning share a common stumbling block: evaluation. Advances in machine learning often evaporate under closer scrutiny or turn out to be less widely applicable than originally hoped. We conduct a meta-review of 107 survey papers from computer vision, natural language processing, recommender systems, reinforcement learning, graph processing, metric learning, and more, organizing a wide range of surprisingly consistent critique into a concrete taxonomy of observed failure modes. Inspired by measurement and evaluation theory, we divide failure modes into two categories: internal and external validity. Internal validity pertains to evaluation on a learning problem in isolation, such as improper comparisons to baselines or overﬁtting from test set re-use. External validity relies on relationships between different learning problems, for instance, whether progress on a learning problem translates to progress on seemingly related tasks.
STAR: A Benchmark for Situated Reasoning in Real-World Videos

Reasoning in the real world is not divorced from situations. How to capture the present knowledge from surrounding situations and perform reasoning accordingly is crucial and challenging for machine intelligence. This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR). This benchmark is built upon the real-world videos associated with human actions or interactions, which are naturally dynamic, compositional, and logical. The dataset includes four types of questions, including interaction, sequence, prediction, and feasibility. We represent the situations in real-world videos by hyper-graphs connecting extracted atomic entities and relations ( e . g ., actions, persons, objects, and relationships). Besides visual perception, situated reasoning also requires structured situation comprehension and logical reasoning. Questions and answers are procedurally generated. The answering logic represents by a functional program based on the situation hyper-graph. We compare various existing video reasoning models and find that they all struggle on this challenging situated reasoning task. We further propose a diagnostic neuro-symbolic model that can disentangle visual perception, situation abstraction, language understanding, and functional reasoning to understand the challenges of this benchmark.
An Empirical Study of Adder Neural Networks for Object Detection

Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations, which are more energy efficient than traditional convolutional neural networks built with multiplications. Compared with classification, there is a strong demand on reducing the energy consumption of modern object detectors via AdderNets for real-world applications such as autonomous driving and face detection. In this paper, we present an empirical study of AdderNets for object detection. We first reveal that the batch normalization statistics in the pre-trained adder backbone should not be frozen, since the relatively large feature variance of AdderNets. Moreover, we insert more shortcut connections in the neck part and design a new feature fusion architecture for avoiding the sparse features of adder layers. We present extensive ablation studies to explore several design choices of adder detectors. Comparisons with state-of-the-arts are conducted on COCO and PASCAL VOC benchmarks. Specifically, the proposed Adder FCOS achieves a 37.8\% AP on the COCO val set, demonstrating comparable performance to that of the convolutional counterpart with an about $1.4\times$ energy reduction.
Health Professionals’ Perspectives on Electronic Medical Record Infusion and Individual Performance: Model Development and Questionnaire Survey Study

Background Electronic medical records (EMRs) are integrated information sources generated by health care professionals (HCPs) from various health care information systems. EMRs play crucial roles in improving the quality of care and medical decision-making and in facilitating cross-hospital health information exchange. Although many hospitals have invested considerable resources and efforts to develop EMRs for several years, the factors affecting the long-term success of EMRs, particularly in the EMR infusion stage, remain unclear. Objective The aim of this study was to investigate the effects of technology, user, and task characteristics on EMR infusion to determine the factors that largely affect EMR infusion. In addition, we examined the effect of EMR infusion on individual HCP performance. Methods A questionnaire survey was used to collect data from HCPs with >6 months experience in using EMRs in a Taiwanese teaching hospital. A total of 316 questionnaires were distributed and 211 complete copies were returned, yielding a valid response rate of 66.8%. The collected data were further analyzed using WarpPLS 5.0. Results EMR infusion (R2=0.771) was mainly affected by user habits (β=.411), portability (β=.217), personal innovativeness (β=.198), technostress (β=.169), and time criticality (β=.168), and individual performance (R2=0.541) was affected by EMR infusion (β=.735). This finding indicated that user (habit, personal innovativeness, and technostress), technology (portability), and task (mobility and time criticality) characteristics have major effects on EMR infusion. Furthermore, the results indicated that EMR infusion positively affects individual performance. Conclusions The factors identified in this study can extend information systems infusion theory and provide useful insights for the further improvement of EMR development in hospitals and by the government, specifically in its infusion stage. In addition, the developed instrument can be used as an assessment tool to identify the key factors for EMR infusion, and to evaluate the extent of EMR infusion and the individual performance of hospitals that have implemented EMR systems. Moreover, the results can help governments to understand the urgent needs of hospitals in implementing EMR systems, provide sufficient resources and support to improve the incentives of EMR development, and develop adequate EMR policies for the meaningful use of electronic health records among hospitals and clinics.