Miracle-dz/UniFAR-FTUs huggingface dataset:
Detailed assessment of the reported economic costs of invasive species in Australia

The legacy of deliberate and accidental introductions of invasive alien species to Australia has had a hefty economic toll, yet quantifying the magnitude of the costs associated with direct loss and damage, as well as for management interventions, remains elusive. This is because the reliability of cost estimates and under-sampling have not been determined. We provide the first detailed analysis of the reported costs associated with invasive species to the Australian economy since the 1960s, based on the recently published InvaCost database and supplementary information, for a total of 2078 unique cost entries. Since the 1960s, Australia has spent or incurred losses totalling at least US$298.58 billion (2017 value) or AU$389.59 billion (2017 average exchange rate) from invasive species. However, this is an underestimate given that costs rise as the number of estimates increases following a power law. There was an average 1.8–6.3-fold increase in the total costs per decade since the 1970s to the present, producing estimated costs of US$6.09–57.91 billion year-1 (all costs combined) or US$225.31 million–6.84 billion year-1 (observed, highly reliable costs only). Costs arising from plant species were the highest among kingdoms (US$151.68 billion), although most of the costs were not attributable to single species. Of the identified weedy species, the costliest were annual ryegrass (Lolium rigidum), parthenium (Parthenium hysterophorus) and ragwort (Senecio jacobaea). The four costliest classes were mammals (US$48.63 billion), insects (US$11.95 billion), eudicots (US$4.10 billion) and monocots (US$1.92 billion). The three costliest species were all animals – cats (Felis catus), rabbits (Oryctolagus cuniculus) and red imported fire ants (Solenopsis invicta). Each State/Territory had a different suite of major costs by species, but with most (3–62%) costs derived from one to three species per political unit. Most (61%) of the reported costs applied to multiple environments and 73% of the total pertained to direct damage or loss compared to management costs only, with both of these findings reflecting the availability of data. Rising incursions of invasive species will continue to have substantial costs for the Australian economy, but with better investment, standardised assessments and reporting and coordinated interventions (including eradications), some of these costs could be substantially reduced.
The impact of parenthood on depressive symptoms across family policy regimes in Europe

The aim of this study is to examine the impact of parenthood on depressive symptoms, and to investigate what channels this influence in cross-country comparative perspective. We consider the overall impact of parenthood on mental health as a sum of the positive direct effects of raising children, and negative indirect effects such as distress related to intrahousehold division of paid labor or financial hardship. The role of these indirect effects may vary across countries with different family policies. We use data from European Social Survey and we implement mediation analysis to disentangle direct and indirect effects of raising children in country groups representing different family policies regimes. Our results show that the association between parenthood and depressive symptoms is mediated by distress related to intrahousehold division of paid labor and financial hardship, but these influences are context dependent. They are non-existent in family policy regimes with substantial state support in the form of childcare services and cash family benefits. Distress related to intrahousehold division of labor or financial hardship contributes to depression among parents only in family policy regimes where the state support for families with children is very limited.
Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
Risk-Taking, Capital Allocation and Optimal Stabilization Policy∗

We study the role of firm heterogeneity in affecting business cycle dynamics and optimal stabilization policy. Firms differ in their degree of cyclicality, and hence, exposure to aggregate risk, leading to firm-specific risk premia that influence resource allocations. The heterogeneous firm economy can be recast in a representative firm formulation, but where total factor productivity (TFP) is endogenous and depends on the resource allocation. The model uncovers a novel tradeoff between the long-run level and volatility of TFP. Inefficiencies distort this tradeoff and result in either excessive volatility or depressed output, implying a role for corrective policy. Embedding this mechanism into a workhorse New Keynesian model, we show that allocational considerations can strengthen the incentives for leaning against the wind, i.e., optimal policy is more strongly countercyclical than in an observationally equivalent economy that abstracts from heterogeneity. A quantitative exercise suggests that the losses from ignoring heterogeneity can be substantial, which stem largely from a less productive allocation of resources and so depressed TFP and output. ∗We thank Jonas Fisher, Gadi Barlevy, Francois Gourio, Rohan Kekre, Moritz Lenel, Mariacristina De Nardi, Pablo Kurlat and various seminar and conference participants for helpful comments and suggestions. The views expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System. †joel.david@chi.frb. org. ‡zeke@usc.edu. “The level of consumption risk in a society is, in part, subject to choice. When in an economy that is subject to larger shocks, people will live with more consumption variability and the associated loss in welfare, but they may also substitute into risk-avoiding technologies, accepting reduced average levels of production. ” — Robert E. Lucas, Jr. “Macroeconomic Priorities” 2003 Presidential Address to the American Economic Association
Motivating Physical Activity via Competitive Human-Robot Interaction

This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games. With this goal in mind, we introduce the Fencing Game, a human-robot competition used to evaluate both the capabilities of the robot competitor and user experience. We develop the robot competitor through iterative multi-agent reinforcement learning and show that it can perform well against human competitors. Our user study additionally found that our system was able to continuously create challenging and enjoyable interactions that significantly increased human subjects' heart rates. The majority of human subjects considered the system to be entertaining and desirable for improving the quality of their exercise.
Using Dynamic Neural Networks to Model the Speed-Accuracy Trade-Off in People

Neural networks have been shown to exhibit remarkable object recognition per-1 formance. We ask here whether such networks can provide a useful model for 2 how people recognize objects. Human recognition time varies, from 0.1 to 10 s, 3 depending on the stimulus and task. Slowness of recognition is a key feature in 4 some public health issues, such as dyslexia, so it is crucial to create a model of 5 human speed-accuracy trade-offs. This is an essential aspect of any useful com-6
General Anxiety, Academic Distress, and Family Distress Among Doctor of Pharmacy Students

Objective. To examine the prevalence of general anxiety among Doctor of Pharmacy (PharmD) students and the role of academic distress and family distress in causing general anxiety. Methods. A cross-sectional study was conducted among first, second, and third year PharmD students. All students received an online survey consisting of the Counseling Center Assessment of Psychological Symptoms-62 (CCAPS-62) questionnaire and sample characteristics. Variables from CCAPS-62 considered in this study included academic distress and family distress measured on a three-level scale: no, low, and high clinical level. Data on gender, race, work status, and physical activity were also collected. Descriptive and multinomial logistic regression were conducted to identify factors associated with general anxiety. Results. Of the 238 students who took the online survey (63% response rate), the majority were female (67%) and Asian (49%). Overall, 69 first year, 75 second year, and 94 third year students participated. The prevalence of general anxiety was 50%, with equal distribution (25% each) among high-clinical and low-clinical general anxiety groups. High academic distress and high family distress were associated with a greater probability of a student having high general anxiety. Conclusion. General anxiety was quite prevalent among pharmacy students. Identification and implementation of strategies to lower general anxiety as well as academic distress is of great importance. Also, understanding and enhancing the role of family members in students’ lives is essential. College administrators can provide support for students as well as for family members to make improvements in these areas.
Learning Spatiotemporal Occupancy Grid Maps for Lifelong Navigation in Dynamic Scenes

We present a novel method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future information of dynamic scenes. Our au-tomated generation process creates groundtruth SOGMs from previous navigation data. We build on prior work to annotate lidar points based on their dynamic properties, which are then projected on time-stamped 2D grids: SOGMs. We design a 3D-2D feedforward architecture, trained to predict the future time steps of SOGMs, given 3D lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for robots. The network is composed of a 3D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2D front-end that predicts the future information embedded in the SOGMs within planning. We also design a navigation pipeline that uses these predicted SOGMs. We provide both quantitative and qualitative insights into the predictions and validate our choices of network design with a comparison to the state of the art and ablation studies.
A Grounded Theory of Pro-Nature Behaviour: From Moral Concern to Sustained Action

Worldwide ecosystem decline is a pressing issue that has led governments to sign up to biodiversity-related targets, but little is known about what drives individuals’ conservation behaviour. This study uses a qualitative methodology (grounded theory) to understand what leads to pro-nature attitudes and behaviours. Twenty participants (10 men; 10 women) underwent semi-structured interviews and audio files were transcribed and coded to form the basis of theory. The authors propose a model based on interview material in which species with intrinsic value (i.e., deemed valuable in their own right, not for their usefulness to humans) are within the bounds of moral consideration. Individuals with generalised beliefs about the intrinsic value of non-human species expressed moral concern for human-caused impacts on nature. External prompts, including social messages, were associated with sporadic pro-nature behaviours. Individuals engaging in sustained behaviour showed evidence of having internalised moral standard of pro-nature actions and also appeared to fashion a social environment that would sustain and enhance their moral views. We discuss the implications of our findings with respect to conservation campaign messaging and government policy.
Early detection of macrovascular complications in type 2 diabetes mellitus in Medan, North Sumatera, Indonesia: A cross-sectional study

Background: Macrovascular complications occur very frequently in patients with type 2 diabetes mellitus (T2DM) with a high mortality rate, due to the development of cardiovascular disease (CVD), such as stroke, atherosclerosis acceleration, and atrial fibrillation. T2DM is a significant risk factor for CVD and has become the leading cause of death. The purpose of this study was to detect the early risk of macrovascular complications by using the ankle brachial index (ABI) as a marker. Methods: This study was an analytic study with a cross-sectional approach. The study population was patients with T2DM from several primary health care centers in Medan. In total, 89 subjects who met the inclusion and exclusion criteria were recruited with consecutive sampling. ABI was determined as the ratio of systolic blood pressure in the brachial artery to the posterior tibial artery after the subjects had been relaxed and felt comfortable in a supine position. Examination of vitamin D and lipid profile was derived from examination of venous blood. Data were processed using SPSS and analyzed with one-way ANOVA. Results: The study found that there was a relationship between LDL-C, triglyceride, and vitamin D (25OH-D) based on the ABI (p > 0.05). Conclusions: ABI can be used for an early detection of macrovascular complications. Apart from being easy to perform, ABI was non-invasive. Some other risk factors that can also be used to assess complications and have relationships with ABI were LDL-C, triglyceride, and vitamin D (25OH-D). Complications in T2DM patients can be prevented with reasonable blood sugar control and lifestyle changes. Education and motivation need to be given to patients so that they become more independent in controlling their disease and improving their quality of life.
Physics Engines as Cognitive Models of Intuitive Physical Reasoning

Many studies have claimed that human physical reasoning consists largely of running “physics engines in the head” in which the future trajectory of the physical system under consideration is computed precisely using accurate scientific theories. In such models, uncertainty and incomplete knowledge is dealt with by sampling probabilistically over the space of possible trajectories (“Monte Carlo simulation”). We argue that such simulation-based models are too weak, in that there are many important aspects of human physical reasoning that cannot be carried out this way, or can only be carried out very inefficiently; and too strong, in that humans make large systematic errors that the models cannot account for. We conclude that simulation-based reasoning makes up at most a small part of a larger system that encompasses a wide range of additional cognitive processes.
Effect of Acetate on Molecular and Physiological Aspects of <italic toggle='yes'>Clostridium beijerinckii</italic> NCIMB 8052 Solvent Production and Strain Degeneration

The addition of sodium acetate to chemically defined MP2 medium was found to increase and stabilize solvent production and also increase glucose utilization by Clostridium beijerinckii NCIMB 8052. RNA and enzyme analyses indicated that coenzyme A (CoA) transferase was highly expressed and has higher activity in C. beijerinckii NCIMB 8052 grown in MP2 medium containing added sodium acetate than in the microorganism grown without sodium acetate. RNA analysis suggested the existence of a sol operon and confirmed the presence of a ptb-buk operon in C. beijerinckii NCIMB 8052. In addition to CoA transferase, C. beijerinckii NCIMB 8052 grown in MP2 medium containing added acetate demonstrated higher acetate kinaseand butyrate kinase-specific activity than when the culture was grown in MP2 medium containing no added acetate. Southern blot analysis with chromosomal DNA isolated from solventogenic and degenerated C. beijerinckii NCIMB 8052 indicated that C. beijerinckii NCIMB 8052 strain degeneration does not involve loss of the CoA transferase genes. The addition of acetate to MP2 medium may induce the expression of the sol operon, which ensures solvent production and prevents strain degeneration in C. beijerinckii NCIMB 8052.
Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers

Despite the success of fine-tuning pretrained language encoders like BERT for downstream natural language understanding (NLU) tasks, it is still poorly understood how neural networks change after fine-tuning. In this work, we use centered kernel alignment (CKA), a method for comparing learned representations, to measure the similarity of representations in task-tuned models across layers. In experiments across twelve NLU tasks, we discover a consistent block diagonal structure in the similarity of representations within fine-tuned RoBERTa and ALBERT models, with strong similarity within clusters of earlier and later layers, but not between them. The similarity of later layer representations implies that later layers only marginally contribute to task performance, and we verify in experiments that the top few layers of fine-tuned Transformers can be discarded without hurting performance, even with no further tuning.
Generating Datasets of 3D Garments with Sewing Patterns

Garments are ubiquitous in both real and many of the virtual worlds. They are highly deformable objects, exhibit an immense variety of designs and shapes, and yet, most garments are created from a set of regularly shaped flat pieces. Exploration of garment structure presents a peculiar case for an object structure estimation task and might prove useful for downstream tasks of neural 3D garment modeling and reconstruction by providing strong prior on garment shapes. To facilitate research in these directions, we propose a method for generating large synthetic datasets of 3D garment designs and their sewing patterns. Our method consists of a flexible description structure for specifying parametric sewing pattern templates and the automatic generation pipeline to produce garment 3D models with little-to-none manual intervention. To add realism, the pipeline additionally creates corrupted versions of the final meshes that imitate artifacts of 3D scanning. With this pipeline, we created the first large-scale synthetic dataset of 3D garment models with their sewing patterns. The dataset contains more than 20000 garment design variations produced from 19 different base types. Seven of these garment types are specifically designed to target evaluation of the generalization across garment sewing pattern topologies.
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation

In this paper, we propose GOHOME, a method leveraging graph representations of the High Definition Map and sparse projections to generate a heatmap output representing the future position probability distribution for a given agent in a traffic scene. This heatmap output yields an unconstrained 2D grid representation of agent future possible locations, allowing inherent multimodality and a measure of the uncertainty of the prediction. Our graph-oriented model avoids the high computation burden of representing the surrounding context as squared images and processing it with classical CNNs, but focuses instead only on the most probable lanes where the agent could end up in the immediate future. GOHOME reaches 2nd on Argoverse Motion Forecasting Benchmark on the Misskate6metric while achieving significant speed-up and memory burden diminution compared to Argoverse 1st place method HOME. We also highlight that heatmap output enables multimodal ensembling and improve 1st place MissRate6by more than 15% with our best ensemble on Argoverse. Finally, we evaluate and reach state-of-the-art performance on the other trajectory prediction datasets nuScenes and Interaction, demonstrating the generalizability of our method.
Multimodal AutoML on Structured Tables with Text Fields

We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text ﬁelds as well. Here we assemble 15 multimodal data tables that each contain some text ﬁelds and stem from a real business application. Over this benchmark 1 , we evaluate numerous multimodal AutoML strategies, including a standardtwo-stageapproachwhereNLPisusedtofeaturizethetextsuchthatAutoMLfor tabulardatacanthenbeapplied.WeproposevariouspracticallysuperiorstrategiesbasedonmultimodaladaptationsofTransformernetworksandstackensemblingofthesenetworkswithclassicaltabularmodels . Beyondperformingthebestinourbenchmark,ourproposed(fullyautomated)methodology 2 managestorank1stplace(againsthumandatascientists) when ﬁt to the raw tabular/text data in two MachineHack prediction competitions and 2nd place (out of 2380 teams) in Kaggle’s Mercari Price Suggestion Challenge.
Optimized Stoichiometry for CuCrO2 Thin Films as Hole Transparent Layer in PBDD4T-2F:PC70BM Organic Solar Cells

The performance and stability in atmospheric conditions of organic photovoltaic devices can be improved by the integration of stable and efficient photoactive materials as substituent of the chemically unstable poly (3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS), generally used as organic hole transport layer. Promising candidates are p-type transparent conductive oxides, which combine good optoelectronic and a higher mechanical and chemical stability than the organic counterpart. In this work, we synthesize Cu-rich CuCrO2 thin films by aerosol-assisted chemical vapour deposition as an efficient alternative to PEDOT:PSS. The effect of stoichiometry on the structural, electrical, and optical properties was analysed to find a good compromise between transparency, resistivity, and energy bands alignment, to maximize the photovoltaic performances. , Average transmittance and bandgap are reduced when increasing the Cu content in these out of stoichiometry CuCrO2 films. The lowest electrical resistivity is found for samples synthesized from a solution composition in the 60–70% range. The optimal starting solution composition was found at 65% of Cu cationic ratio corresponding to a singular point in Hackee’s figure of merit of 1 × 10−7 Ω−1. PBDD4T-2F:PC70BM organic solar cells were fabricated by integrating CuCrO2 films grown from a solution composition ranging between 40% to 100% of Cu as hole transport layers. The solar cells integrating a film grown with a Cu solution composition of 65% achieved a power conversion efficiency as high as 3.1%, representing the best trade-off of the optoelectronic properties among the studied candidates. Additionally, despite the efficiencies achieved from CuCrO2-based organic solar cells are still inferior to the PEDOT:PSS counterpart, we demonstrated a significant enhancement of the lifetime in atmospheric conditions of optimal oxides-based organic photovoltaic devices.
Gendered demand for modern cookstoves in the presence of environmental health complementarities

We study how household demand for a technology that improves environmental health an electric cookstove is related to exposure to prior health-related interventions, among households in 40 villages in rural Odisha. More than a decade before the demand study, a random half of our sample had been exposed to an intensive villagelevel behavior change campaign aimed at reducing open defecation. We observe that auction bids for electric cookstoves vary according to both gender and prior exposure to the sanitation campaign. These results suggest the importance of interactions between information, preferences and bargaining power. JEL: I12, J16, O13