Miracle-dz/UniFAR-FTUs huggingface dataset:
The carbon footprint of suburbanization: evidence from French household data

This paper investigates the impact of urban sprawl on fuel consumption and greenhouse gas emissions emanating from personal driving. The use of a very detailed French household survey helps control for the fact that some households may have moved to a residential location consonant to their socio-economic characteristiscs and travel predispositions. In addition to tackling self-selection, we also use instrumental variables to control for any omitted factor that would likely bias the estimated impact of the urban form. The results suggest that, by chosing to live at the urban fringe of a metropolitan area instead of a city-center, a non-Parisian household would bear an extra consumption of approximatively six fuel tanks per year. More generally, doubling residential density across a metropolitan area would result in an annual saving of approximatively two tanks per household, and even more if coupled with better population centrality, improved rail-routes toward city-centres and reduced pressure for road construction. As shown by the ``Hit-parade" of French cities drawn from our fuel predictions, moving a particular household from, say, Chamonix (in the French Alps), to a more compact city such as Paris would halve its travel emissions. This statistically significant adverse effect of urban sprawl on CO2 emissions has strong policy implications in the context of political resistance to carbon taxes in France.
Studying the phase diagram of the three-flavor Schwinger model in the presence of a chemical potential with measurement- and gate-based quantum computing

We propose an ansatz quantum circuit for the variational quantum eigensolver (VQE), suitable for exploring the phase structure of the multi-flavor Schwinger model in the presence of a chemical potential. Our ansatz is capable of incorporating relevant model symmetries via constrains on the parameters, and can be implemented on circuit-based as well as measurement-based quantum devices. We show via classical simulation of the VQE that our ansatz is able to capture the phase structure of the model, and can approximate the ground state to a high level of accuracy. Moreover, we perform proof-of-principle simulations on superconducting, gate-based quantum hardware. Our results show that our approach is suitable for current gate-based quantum devices, and can be readily implemented on measurement-based quantum devices once available.
Morphological and Soil Determinants of Forest Cover Changes in Świętokrzyski National Park and Its Buffer Zone in the last 200 Years

Abstract The research described in the paper utilized GIS methods and comparative cartography in order to analyze changes in forest cover in the period 1800-2011 in the Świętokrzyski National Park (76.26 km²) and its buffer zone (207.86 km²). The research was done for predefined elevation intervals, slope gradients, and genetic soil types. Source materials included historical maps as well as a digital elevation model. Changes in forest cover were noted in spatial and temporal terms and were usually linked to morphology and soil type. While the 19th century was characterized by intense deforestation, this process reversed starting in the early 20th century. Nevertheless, forest cover in the study area has still not returned to its state from 1800.
Controllable Expensive Multi-objective Optimization with Warm-starting Gaussian Processes

Pareto Set Learning (PSL) is a promising approach for approximating the entire Pareto front in multi-objective optimization (MOO) problems. However, existing derivative-free PSL methods are often unstable and inefficient, especially for expensive black-box MOO problems where objective function evaluations are costly. In this work, we propose to address the instability and inefficiency of existing PSL methods with a novel controllable PSL method, called Co-PSL. Particularly, Co-PSL consists of two stages: (1) warm-starting Bayesian optimization to obtain quality Gaussian Processes priors and (2) controllable Pareto set learning to accurately acquire a parametric mapping from preferences to the corresponding Pareto solutions. The former is to help stabilize the PSL process and reduce the number of expensive function evaluations. The latter is to support real-time trade-off control between conflicting objectives. Performances across synthesis and real-world MOO problems showcase the effectiveness of our Co-PSL for expensive multi-objective optimization tasks.
Nonequilibrium hyperuniform states in active turbulence

We demonstrate that the complex spatiotemporal structure in active fluids can feature characteristics of hyperuniformity. Using a hydrodynamic model, we show that the transition from hyperuniformity to non-hyperuniformity and anti-hyperuniformity depends on the strength of active forcing and can be related to features of active turbulence without and with scaling characteristics of inertial turbulence. Combined with identified signatures of Levy walks and non-universal diffusion in these systems, this allows for a biological interpretation and the speculation of non-equilibrium hyperuniform states in active fluids as optimal states with respect to robustness and strategies of evasion and foraging.
Modeling of temporal patterns and sources of atmospherically transported and deposited pesticides in ecosystems of concern: A case study of toxaphene in the Great Lakes

Pesticides have adverse effects on human health and the environment and can be transported through the atmosphere from application sites and deposited to sensitive ecosystems. This study applies a comprehensive multimedia regional pesticide fate and chemical transport modeling system that we developed to investigate the atmospheric transport and deposition of toxaphene to the Great Lakes. Simulated results predict a significant amount of toxaphene (~350 kg) being transported through the atmosphere and deposited into the Great Lakes in the simulation year. Results also show that U.S. residues and global background are major sources to toxaphene deposition into the Great Lakes and atmospheric concentrations in the region. While the U.S. residues are the dominant source in warm months, the background dominates during winter months. In addition, different sources have different influences on the individual Great Lakes due to their proximity and relative geographical positions to the sources; U.S. residues are the dominant source to Lakes Ontario, Erie, Huron, and Michigan, but they are a much less important source to Lake Superior. These results shed light on the mystery that observed toxaphene concentrations in Great Lakes' lake trout and smelt declined between 1982 and 1992 in four of the Great Lakes except Lake Superior. While monthly total depositions to Lakes Ontario, Erie, Huron, and Michigan have clear seasonal variability with much greater values in April, May, and June, monthly total depositions to Lake Superior are more uniformly distributed over the year with comparatively greater levels in cold months.
Frobenius-Type Norms and Inner Products of Matrices and Linear Maps with Applications to Neural Network Training

The Frobenius norm is a frequent choice of norm for matrices. In particular, the underlying Frobenius inner product is typically used to evaluate the gradient of an objective with respect to matrix variable, such as those occuring in the training of neural networks. We provide a broader view on the Frobenius norm and inner product for linear maps or matrices, and establish their dependence on inner products in the domain and co-domain spaces. This shows that the classical Frobenius norm is merely one special element of a family of more general Frobenius-type norms. The significant extra freedom furnished by this realization can be used, among other things, to precondition neural network training.
The Pickup and Delivery Problem with Crossdock for Perishable Goods

Our work departs from the original definition of the Pickup and Delivery Problem (PDP) and extends it by considering an interchange point (crossdock) where vehicles can exchange their goods with other vehicles in order to shorten their delivery routes and reduce their running times. Multiple operational constraints, such as time windows, vehicle capacities, and the synchronization of vehicles at the crossdock, are considered. In addition, the specific requirements of perishable goods, which should not be carried on long trips, are taken into account. Given this consideration, this study introduces the Pickup and Delivery Problem with Crossdock for Perishable Goods (PDPCDPG) and models it as a nonlinear programming problem. PDPCDPG is then reformulated to a MILP with the use of linearizations and its search space is tightened with the addition of valid inequalities that are employed when solving the problem to global optimality with Branch-and-Cut. Various computational experiments are conducted on benchmark instances found in the literature to assess the performance of our model. The results demonstrate the solution stability of the proposed approach. The proposed model aims to provide a practical and effective approach for transportation and logistics companies dealing with time-sensitive deliveries.
Enhancing Diffusion Models with Text-Encoder Reinforcement Learning

Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such as image aesthetics and image-text alignment. Recent research addresses this issue by refining the diffusion U-Net using human rewards through reinforcement learning or direct backpropagation. However, many of them overlook the importance of the text encoder, which is typically pretrained and fixed during training. In this paper, we demonstrate that by finetuning the text encoder through reinforcement learning, we can enhance the text-image alignment of the results, thereby improving the visual quality. Our primary motivation comes from the observation that the current text encoder is suboptimal, often requiring careful prompt adjustment. While fine-tuning the U-Net can partially improve performance, it remains suffering from the suboptimal text encoder. Therefore, we propose to use reinforcement learning with low-rank adaptation to finetune the text encoder based on task-specific rewards, referred as \textbf{TexForce}. We first show that finetuning the text encoder can improve the performance of diffusion models. Then, we illustrate that TexForce can be simply combined with existing U-Net finetuned models to get much better results without additional training. Finally, we showcase the adaptability of our method in diverse applications, including the generation of high-quality face and hand images.
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of ne-onates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 682 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 60 infants who had longitudinal scans. The model was validated on 30 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, the folding morphology demonstrated greater discriminative capability than the cortical thickness, which could serve as the morphological fingerprint in perinatal brains. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of in-dividual uniqueness in the brain during early development.
Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning

Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing a series of unbiased visual-semantics mappings, wherein, the precision relies heavily on the completeness of extracted visual features from both seen and unseen classes. However, as a common practice in GZSL, the pre-trained feature extractor may easily exhibit difficulty in capturing domain-specific traits of the downstream tasks/datasets to provide fine-grained discriminative features, i.e., domain bias, which hinders the overall recognition performance, especially for unseen classes. Recent studies partially address this issue by fine-tuning feature extractors, while may inevitably incur catastrophic forgetting and overfitting issues. In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features. Specifically, our method consists of two key components, i.e., Unseen-Aware Distillation (UAD) and Attribute-Guided Learning (AGL). During training, UAD exploits the prior knowledge of attribute texts that are shared by both seen/unseen classes with attention mechanisms to detect and maintain unseen class-sensitive visual features in a targeted manner, and meanwhile, AGL aims to steer the model to focus on valuable features and suppress them to fit noisy elements in the seen classes by attribute-guided representation learning. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method.
UST Research Online UST Research Online

Purpose – The purpose of this paper is to look at the ways in which the internet has changed the way in which many organizations now do business. The internet has made the transference of information easy but fulﬁlling online orders has proved to be a challenge. Design/methodology/approach – This study presents detailed analysis of 238 customers of a world-renowned not-for-proﬁt organization – the Chicago Symphony Orchestra (CSO). Our sample of customers consists of patrons who ordered tickets online at least once during the 2001/2002 concert seasons. Factors inﬂuencing the development of an e-loyal customer database were examined. Findings – The results indicate that customers realize signiﬁcant beneﬁts from using the internet to purchase concert tickets. Patrons also indicated that they were satisﬁed with their internet service experience. Originality/value – The role of the internet within organizations will change dramatically over the next decade. For organizations that are attempting to use the internet as a primary sales medium, they must realize that their success hinges on the development of a sustainable customer base. For established organizations, the integration of the internet into their existing business will be one of the keys to future success.
Mosses of the Mus-Khaya Mountain (Yakutia, Asiatic Russia)

Moss flora of the area around the second highest peak of Yakutia, the MusKhaya Mt., is studied. This area is close to the coldest place of the Northern Hemisphere. Severe condition and high content of heavy metals result is a rather poor diversity of 180 species; their list is annotated by altitudinal range, frequency, locality and habitat information. Map of Gollania turgens is provided; Mielichhoferia mielichhoferiana, M. elongata and Andreaea papillosa are discussed and illustrated.
Ammonites from the Saratoga Chalk (Upper Cretaceous), Arkansas

The Saratoga Chalk of Arkansas yields a rich ammonite fauna of 17 species, referred to the Nostoceras (N.) hyatti zone. Recognition that the northwest European N. (N.) pozaryskii Blaszkiewicz, 1980, is a synonym of N. (N.) hyatti Stephenson, 1941, and N. (N.) helicinum (Shumard, 1861) dates the zone as latest Campanian on the basis of co-occurrence with Belemnitella langei Jeletzky, 1948, in Poland. Most previous estimates of the Campanian–Maastrichtian boundary in the Gulf Coast have been drawn at too low a level, at least in terms of ammonite faunas. Elements of the N. (N.) hyatti zone fauna occur in the United States Western Interior, and show the base of the Maastrichtian there to lie above the Baculites jenseni zone.
Annual Review of Statistics and Its Application Adaptive Enrichment Designs in Clinical Trials

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design’s structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.
R E P

Widespread use of transgenic crops that express an insecticidal endotoxin from Bacillus thuringiensis increases the risk of evolution of resistance by the European corn borer and other insect pests. To delay resistance evolution, the high-dose ⁄ refuge strategy is being implemented for Bt maize and Bt cotton. We develop a general modelling framework to understand the invasion and spread of alleles conferring resistance. We show that at least three processes are involved in explaining the effectiveness of the high-dose ⁄ refuge strategy: the intensity of selection, assortative (non-random) mating due to spatial subdivision, and variation in male mating success also due to spatial subdivision. Understanding these processes leads to a greater range of possible resistance management tactics. For example, efforts to encourage adults to leave their natal fields may have the unwanted effect of speeding rather than slowing resistance evolution. Furthermore, when Bt maize causes high mortality to susceptible target pests, spraying insecticides in refuges to reduce pest populations may not greatly disrupt resistance management.
Improved ocean-geoid resolution from retracked ERS-1 satellite altimeter waveforms

SUMMARY The ocean geoid can be inferred from the topography of the mean sea surface. Satellite altimeters transmit radar pulses and determine the return traveltime to measure seasurface height. The ERS-1 altimeter stacks 51 consecutive radar reflections on board the satellite to a single waveform. Tracking the time shift of the waveform gives an estimate of the distance to the sea surface. We retrack the ERS-1 radar traveltimes using a model that is focused on the leading edge of the waveforms. While earlier methods regarded adjacent waveforms as independent statistical events, we invert a whole sequence of waveforms simultaneously for a spline geoid solution. Smoothness is controlled by spectral constraints on the spline coeYcients. Our geoid solutions have an average spectral density equal to the expected power spectrum of the true geoid. The coherence of repeat track solutions indicates a spatial resolution of 31 km, as compared to 41 km resolution for the ERS-1 Ocean Product. While the resolution of the latter deteriorates to 47 km for wave heights above 2 m, our geoid solution maintains its resolution of 31 km for rough sea. Retracking altimeter waveform data and constraining the solution by a spectral model leads to a realistic geoid solution with significantly improved along-track resolution.
Forest response to increased disturbance in the central Amazon and comparison to western Amazonian forests

Uncertainties surrounding vegetation response to increased disturbance rates associated with climate change remains a major global change issue for Amazonian forests. Additionally, turnover rates computed as the average of mor- tality and recruitment rates in the western Amazon basin are doubled when compared to the central Amazon, and notable gradients currently exist in specific wood density and above- ground biomass (AGB) between these two regions. This study investigates the extent to which the variation in dis- turbance regimes contributes to these regional gradients. To address this issue, we evaluated disturbance-recovery pro- cesses in a central Amazonian forest under two scenarios of increased disturbance rates using first ZELIG-TROP, a dy- namic vegetation gap model which we calibrated using long- term inventory data, and second using the Community Land Model (CLM), a global land surface model that is part of the Community Earth System Model (CESM). Upon doubling the mortality rate in the central Amazon to mirror the natural disturbance regime in the western Amazon of 2 % mortal- ity, the two regions continued to differ in multiple forest pro- cesses. With the inclusion of elevated natural disturbances, at steady state, AGB significantly decreased by 41.9 % with no significant difference between modeled AGB and em- pirical AGB from the western Amazon data sets (104 vs. 107 Mg C ha 1 , respectively). However, different processes were responsible for the reductions in AGB between the models and empirical data set. The empirical data set sug- gests that a decrease in wood density is a driver leading to the reduction in AGB. While decreased stand basal area was the driver of AGB loss in ZELIG-TROP, a forest attribute that does not significantly vary across the Amazon Basin. Further comparisons found that stem density, specific wood density, and basal area growth rates differed between the two Amazo- nian regions. Last, to help quantify the impacts of increased disturbances on the climate and earth system, we evaluated the fidelity of tree mortality and disturbance in CLM. Similar to ZELIG-TROP, CLM predicted a net carbon loss of 49.9 %, with an insignificant effect on aboveground net primary pro- ductivity (ANPP). Decreased leaf area index (LAI) was the driver of AGB loss in CLM, another forest attribute that does not significantly vary across the Amazon Basin, and the tem- poral variability in carbon stock and fluxes was not replicated in CLM. Our results suggest that (1) the variability between regions cannot be entirely explained by the variability in dis- turbance regime, but rather potentially sensitive to intrinsic environmental factors; or (2) the models are not accurately simulating all tropical forest characteristics in response to in- creased disturbances.