Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Elasticity of Intertemporal Substitution Using Mortgage NotchesBest: michael.best@columbia.edu, Cloyne: jcloyne@ucdavis.edu, Ilzetzki: E.Ilzetzki@lse.ac.uk, Kleven: kleven@princeton.edu. We thank Orazio Attanasio, Adrien Auclert, Chris Carroll, Raj Chetty, Jeff Clemens, Gordon Dahl, M

Using a novel source of quasi-experimental variation in interest rates, we develop a new approach to estimating the Elasticity of Intertemporal Substitution (EIS). In the UK, the mortgage interest rate features discrete jumps — notches — at thresholds for the loan to value (LTV) ratio. These notches generate large bunching below the critical LTV thresholds and missing mass above them. We develop a dynamic model that links these empirical moments to the underlying structural EIS. The average EIS is small, around 0.1, and quite homogeneous in the population. This finding is robust to structural assumptions and can allow for uncertainty, a wide range of risk preferences, portfolio reallocation, liquidity constraints, present bias, and optimization frictions. Our findings have implications for the numerous calibration studies that rely on larger values of the EIS. ∗Best: michael.best@columbia.edu, Cloyne: jcloyne@ucdavis.edu, Ilzetzki: E.Ilzetzki@lse.ac. uk, Kleven: kleven@princeton.edu. We thank Orazio Attanasio, Adrien Auclert, Chris Carroll, Raj Chetty, Jeff Clemens, Gordon Dahl, Mariacristina De Nardi, Rebecca Diamond, Roger Gordon, Bob Hall, Ethan Kaplan, Greg Kaplan, Patrick Kehoe, David Laibson, Emi Nakamura, Petra Persson, Ricardo Reis, Jose-Victor Rios-Rull, Emmanuel Saez, Orie Shelef, Jón Steinsson, Gregory Thwaites, Gianluca Violante, Garry Young, Stephen Zeldes, and four anonymous referees for helpful comments and discussion. All charts and estimates use data provided to the Bank of England by the Financial Conduct Authority and MoneyFacts. This research was carried out as part of the Bank of England’s One Bank Research Agenda and an earlier draft was circulated under the title “Interest rates, debt and intertemporal allocation: evidence from notched mortgage contracts in the United Kingdom”. The views expressed are those of the authors and do not necessarily reflect the views of the Bank of England, the Monetary Policy Committee, the Financial Policy Committee or the Prudential Regulatory Authority.
Adaptive Spiral Layers for Efficient 3D Representation Learning on Meshes

The success of deep learning models on structured data has generated significant interest in extending their application to non-Euclidean domains. In this work, we introduce a novel intrinsic operator suitable for representation learning on 3D meshes. Our operator is specifically tailored to adapt its behavior to the irregular structure of the underlying graph and effectively utilize its long-range dependencies, while at the same time ensuring computational efficiency and ease of optimization. In particular, inspired by the framework of Spiral Convolution, which extracts and transforms the vertices in the 3D mesh following a local spiral ordering, we propose a general operator that dynamically adjusts the length of the spiral trajectory and the parameters of the transformation for each processed vertex and mesh. Then, we use polyadic decomposition to factorize its dense weight tensor into a sequence of lighter linear layers that separately process features and vertices information, hence significantly reducing the computational complexity without introducing any stringent inductive biases. Notably, we leverage dynamic gating to achieve spatial adaptivity and induce global reasoning with constant time complexity benefitting from an efficient dynamic pooling mechanism based on Summed-Area-tables. Used as a drop-in replacement on existing architectures for shape correspondence our operator significantly improves the performance-efficiency trade-off, and in 3D shape generation with morphable models achieves state-of-the-art performance with a three-fold reduction in the number of parameters required. Project page: https://github.com/Fb2221/DFC
Infrastructure and inequality $

We develop a model in which public capital is both an engine of growth and a determinant of the distributions of wealth, income, and welfare. Government investment increases wealth inequality over time, regardless of its ﬁnancing. The time path of income inequality is, however, highly sensitive to ﬁnancing policies, and is often characterized by sharp intertemporal tradeoffs, with income inequality declining in the short run but increasing in the long run. Public investment generates a positive correlation between growth and income inequality along the transition path, but their short-run and long-run relationship depends critically on (i) how externalities impinge on allocation decisions, (ii) ﬁnancing policies, and (iii) the time period of consideration. Finally, these policies also generate sharp trade-offs between average welfare and its distribution, with government investment improving average welfare, but also increasing its dispersion. Our results are obtained numerically but extensive sensitivity analysis conﬁrms their robustness across key parameter values. & 2012 Elsevier B.V. All rights reserved.
Interventions for mitigating occupational stress for professional dementia caregivers in residential aged care: A systematic review with meta-analysis

Objective Occupational stress in professional dementia caregivers in residential aged care facilities has adverse effects on care quality, caregivers’ health, and workforce sustainability. The purpose of this study was to examine the evidence regarding interventions to mitigate occupational stress for this population. Methods A systematic review of CINAHL, PsycINFO, PubMed and MEDLINE databases was conducted to identify original RCT research reporting on stress interventions, published in English between 1995 and March 2022. Search results were screened by two independent reviewers. Quality and risk of bias were appraised using the Downs and Black Checklist and Risk of Bias by two reviewers. Meta-analysis and subgroup analysis examined the pooled intervention effects on stress compared to control. Results 10 studies met the inclusion criteria, and these reported on 15 interventions and 28 outcomes from 92 facilities, involving 1,397 caregivers. We found a small and insignificant effect of interventions on caregiver stress (g = −.27, p = .16). Heterogeneity was partially explained by subgroup analysis. Interventions can mitigate stress and burden not attributed to client behaviour (n = 3) (g = −.85, p < .001), and improve caregivers’ self-efficacy (n = 4) (g = −.35, p = .07). We were unable to determine the most effective type of intervention, although organisation focused interventions showed the greatest potential (g = −.58, p = .08). Conclusion Interventions that improve caregivers’ personal and organisational resources can reduce non-client associated stress and burden and increase self-efficacy. Aged care providers are recommended to prioritise education with organisational support interventions. Research on longitudinal effects and high-risk caregivers is required. Limitations are discussed. Prospero Registration Number CRD42022313715 (registered April 2022).
The impact of combinations of trash management and tillage on grain legume and subsequent sugarcane productivity in the bundaberg/childers district

PRODUCERS IN THE Bundaberg/Childers and Maryborough districts are implementing various combinations of legume rotations, trash retention, reduced tillage and controlled traffic in new and evolving farming systems. There are challenges in successfully integrating these components. Two trial sites were established to measure the impact of different trash management (maintaining full GCTB, removing some trash via baling and full removal through burning) and tillage (conventional, strip-till and direct drill) techniques on grain legume production, with one site focussed on grain soybean productivity and the other on peanuts. The different tillage systems were reinstituted after harvest of the legume crop during the establishment of the plant cane crop. Impacts of tillage and additional nitrogen application on plant cane productivity were measured. The soybean trial site was established on a farm growing cane in 1.57 m row configuration, and neither trash management nor tillage affected soybean productivity. However there was a trend for lower plant cane productivity with reduced tillage and there was a 13% yield improvement through the application of fertiliser N in a very wet season. In contrast, the peanut trial site was established on a farm growing cane in 1.8 m row spacings. Sugarcane trash management didn't impact on peanut productivity, although yields were lower in reduced tillage treatments. In the following plant cane crop there was no impact of tillage on sugarcane productivity, and there was only a 5% response to N fertiliser application on cane productivity, but not sugar productivity. Data demonstrate that even though soybean productivity was unaffected, unamended soil compaction from the previous cane cycle can limit productivity of both fallow peanut crops and the next cane crop cycle. Further research may be able to improve productivity and harvest efficiency of peanuts in reduced tillage systems. However, it seems clear that appropriate row configurations to control traffic and minimise compaction are imperative to allow successful sugarcane production in reduced tillage farming systems.
Moving Beyond the Relative Assessment of Implicit Biases: Navigating the Complexities of Absolute Measurement

A relative assessment of implicit biases is limited because it produces a combined summary evaluation of two attitudinal beliefs while concealing the biases driving this evaluation. Similar limitations occur for relative explicit measures. Here, we will discuss the benefits and weaknesses of using relative versus absolute (individual/separate) assessments of implicit and explicit attitudes. The Implicit Association Test (IAT) will be the focal implicit measure discussed, and we will present a new perspective challenging the evidence that the IAT can only be utilized to measure relative, not absolute, implicit attitudes. Modeling techniques (i
Seismic and gravity constraints on the crustal architecture of the Intermontane terranes, central Yukon

In 2004, two seismic reflection lines were shot across the Mesozoic Whitehorse trough and adjacent terranes. Three-dimensional first-arrival tomographic inversion is used to constrain lithology to 800–1200 m depth, and surface structures are extrapolated into the middle crust using the coincident reflection data. In the Yukon–Tanana terrane, the metasedimentary Snowcap assemblage is characterized by velocities of 4.5–5.5 km/s, while in Quesnellia, velocities of 5.0–6.0 km/s occur at 500 m depth, and probably represent igneous rocks of the Tatchun batholith. Across the Whitehorse trough, velocities >4.0 km/s correspond to clastic rocks of the Jurassic Laberge and Triassic Lewes River groups; velocities <4.0 km/s probably present the clastic Jurassic to Cretaceous Tantalus Formation. Several near-surface units with velocities of 2.0–3.0 km/s are identified; some correlate well with volcanic rocks of the Upper Cretaceous Carmacks Group, but others could be attributable to alluvial deposits or faulting. The B.
eP-ALM: Eﬀicient Perceptual Augmentation of Language Models

Large Language Models (LLMs) have so far impressed the world, with unprecedented capabilities that emerge in models at large scales. On the vision side, transformer models ( i.e. , ViT) are following the same trend, achieving the best performance on challenging benchmarks. With the abundance of such unimodal models, a natural question arises; do we need also to follow this trend to tackle multimodal tasks? In this work, we propose to rather direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. In particular, they still train a large number of parameters, rely on large multimodal pretraining, use encoders ( e.g. , CLIP) trained on huge image-text datasets, and add significant inference overhead. In addition, most of these approaches have focused on Zero-Shot and In Context Learning, with little to no effort on direct finetuning. We investigate the minimal computational effort needed to adapt unimodal models for multimodal tasks and propose a new challenging setup, alongside different approaches, that efficiently adapts uni-modal pretrained models. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning across Image, Video, and Audio modalities, following the proposed setup. The code will be available here: https://github.com/mshukor/eP-ALM.
Soot aerosol from commercial aviation engines are poor ice nucleating particles at cirrus cloud temperatures

Ice nucleating particles catalyze ice formation in clouds, affecting climate through radiative forcing from aerosol-cloud interactions. Aviation directly emits particles into the upper troposphere where ice formation conditions are favorable. Previous studies have used proxies of aviation soot to estimate their ice nucleation activity, however the investigations with commercial aircraft soot from modern in-use aircraft engine have not been quantified. In this work, we sample aviation soot particles at ground level from different commercial aircraft engines to test their ice nucleation ability at temperatures ≤ 228 5 K , as a function of engine thrust and soot particle size. Additionally soot particles were catalytically stripped to reveal the impact of mixing state on their ice nucleation ability. Particle physical and chemical properties were further characterized and related to the ice nucleation properties. The results show that aviation soot nucleates ice at or above relative humidity conditions required for homogeneous freezing of solution droplets (RH hom ). We attribute this to a mesopore paucity inhibiting pore condensation and the sulfur content which suppresses freezing. Only large soot aggregates (400 nm ) emitted under 30-100 10 % thrust conditions for a subset of engines (2/10) nucleate ice via pore condensation and freezing. For those specific engines, the presence of hydrophilic chemical groups facilitates the nucleation. Aviation soot emitted at thrust ≥ 100 % (sea level thrust) nucleates ice at or above RH hom . Overall our results suggest that aviation soot will not contribute to natural cirrus formation and can be used in models to update impacts of soot-cirrus clouds
Biosolids in a Chihuahuan desert ecosystem.

Biosolids, formerly called "sewage sludge," are a byproduct of waste water treatment. The U.S. produced 7.6 million tons of biosolids in 1998, and annual biosolids production is expected to increase to 9 million tons by 20 10. Land-based disposal methods include incineration, land-filling, pelletization, composting, and land application. Biosolids contain organic matter and a host of macroand micro-nutrients (Table 1).
Suspended sediment yield transportation by rivers of the Kamchatsky Krai into the Pacific Ocean, the sea of Okhotsk and the Bering Sea

ABSTRACT The amount of suspended sediments transported by rivers of the Kamchatsky Krai into the Pacific Ocean, the Sea of Okhotsk and the Bering Sea was estimated. The suspended sediment yield from ungauged territories was also assessed on the basis of geography-hydrology generalization and regression equations. The total suspended sediment yield from the Kamchatsky Krai territory is estimated as 11.4 Mt per year. Most part of these sediments (73.9%) are transported by rivers of the eastern coast and only 26.1% is transported by rivers of the western Kamchatka coast. Such distribution of suspended sediment yield in the Kamchatsky Krai territory is explained by position of active volcanoes there. About one third of the total suspended sediment yield is delivered by two major rivers – the Penzhina River (flows into the sea of Okhotsk, covers 16.1% of the drainage area) and the Kamchatka River (flows into the Pacific Ocean, covers 12.3% of the drainage area).
A protocol for systematic review and meta-analysis

Background: Systematic evaluation of the effectiveness and safety of combined procarbazine, lomustine, and vincristine for treating recurrent high-grade glioma. Methods: Electronic databases including PubMed, MEDLINE, EMBASE, Cochrane Library Central Register of Controlled Trials, WanFang, and China National Knowledge Infrastructure (CNKI) were used to search for studies related to the utilization of combined procarbazine, lomustine, and vincristine as a therapeutic method for recurrent high-grade glioma. Literature screening, extraction of data, and evaluation of high standard studies were conducted by 2 independent researchers. The robustness and strength of the effectiveness and safety of combined procarbazine, lomustine, and vincristine as a therapeutic methodology for recurrent high-grade glioma was assessed based on the odds ratio (OR), mean differences (MDs), and 95% confidence interval (CI). RevMan 5.3 software was used for carrying out the statistical analysis. Results: These results obtained in this study will be published in a peer-reviewed journal. Conclusion:Evidently, the conclusion of this study will provide an assessment on whether combined procarbazine, lomustine, and vincristine provides an effective and safe form of treatment for recurrent high-grade glioma. Systematic review registration number: INPLASY202080078. Abbreviations: CI = confidence interval, CNKI = China National Knowledge Infrastructure, CNS = central nervous system, GBM = glioblastomas, MD = mean differences, ORs = odds ratios, PFS = progression-free survival, WHO = World Health Organization.
Modelling model-based and model-free decision-making in a transdiagnostic sample to investigate disorder-specific and transdiagnostic alterations

Alterations in decision-making have been suggested to underlie different psychiatric illnesses, such as major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. Studies report overlapping and divergent alterations in individuals diagnosed with these disorders, which have been linked to symptoms of the respective disorders. One symptom characterizing all these disorders is impaired decision-making. To facilitate early diagnosis and treatment among these disorders which may also show comorbidities among each other, it is crucial to disentangle alterations in decision-making that are unique to specific disorders, from those that occur across diagnostic boundaries. Recent developments in computational modelling have enabled a mechanistic deconstruction of decision-making processes, facilitating the identification of disorder specific and disorder general alterations. We, therefore, employed two computational modelling approaches on to a two-step Markov decision task in a transdiagnostic cohort combining individuals diagnosed with MDD (N=23), OCD (N=25), and schizophrenia (N=27) to identify disorder specific and disorder general decision-making alterations, and associations with symptoms. The results revealed overall reliance on model-free decision-making behavior across all participants. Additionally, schizophrenia patients showed the lowest learning rate in stage one and highest switching rate (lowest perseverance) compared to all other groups. All patient groups were more random in their response profiles than controls in stage two, with schizophrenia patients revealing highest levels of randomness, while OCD patients were additionally more random in stage one compared to controls and MDD patients. Importantly, the findings revealed a correlation between increased model-free behavior and elevated depressive symptoms, while more model-based choices were linked to lower levels of anhedonia across all patients. Consequently, this study shows noteworthy disorder general and disorder specific decision-making alterations in individuals diagnosed with MDD, OCD, and schizophrenia, suggesting that concurrent alterations and symptoms might potentially have common underlying behavioral mechanisms.
P-tau217 and other blood biomarkers of dementia: variation with time of day

Plasma biomarkers of dementia, including phosphorylated tau (p-tau217), offer promise as tools for diagnosis, stratification for clinical trials, monitoring disease progression, and assessing the success of interventions in those living with Alzheimer's disease. However, currently, it is unknown whether these dementia biomarker levels vary with time of day, which could have implications for their clinical value. In two protocols, we studied 38 participants (70.8 {+/-} 7.6 years; mean {+/-} SD) in a 27-hour laboratory protocol with either two samples taken 12 hours apart or 3-hourly blood sampling for 24 hours in the presence of a sleep-wake cycle. The study population comprised people living with mild Alzheimer's disease (PLWA, n = 8), partners/caregivers of PLWA (n = 6) and cognitively intact older adults (n = 24). Single molecule array technology was used to measure phosphorylated tau (p-tau217) (ALZpath), amyloid-beta 40 (A{beta}40), amyloid-beta 42 (A{beta}42), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) (Neuro 4-Plex E). Analysis with a linear mixed model (SAS, PROC MIXED) revealed a significant effect of time of day for p-tau217, A{beta}40, A{beta}42, and NfL, and a significant effect of participant group for p-tau217. For p-tau217, lowest levels were observed in the morning upon waking and highest values in the afternoon/early evening. The magnitude of the diurnal variation for p-tau217 was similar to the reported increase in p-tau217 over one year in amyloid-{beta}-positive mild cognitively impaired people. Currently, the factors driving this diurnal variation are unknown and could be related to sleep, circadian mechanisms, activity, posture, or meals. Overall, this work implies that time of day of sample collection may be relevant in the implementation and interpretation of plasma biomarkers in dementia research and care.
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning

Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be label-efficient: achieving high predictive performance from relatively few labeled examples. While obtaining the best label-efficiency in practice often requires combinations of these techniques, existing benchmark and evaluation frameworks do not capture a concerted combination of all such techniques. This paper addresses this deficiency by introducing LabelBench, a new computationally-efficient framework for joint evaluation of multiple label-efficient learning techniques. As an application of LabelBench, we introduce a novel benchmark of state-of-the-art active learning methods in combination with semi-supervised learning for fine-tuning pretrained vision transformers. Our benchmark demonstrates better label-efficiencies than previously reported in active learning. LabelBench's modular codebase is open-sourced for the broader community to contribute label-efficient learning methods and benchmarks. The repository can be found at: https://github.com/EfficientTraining/LabelBench.