Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Generalization via f−Mutual Information

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f−divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals. This allows us to control the measure of an event under the joint, using the product of the marginals (typically easier to compute) and a measure of how much the two distributions differ, i.e., an f−divergence between the joint and the product of the marginals, also known in the literature as f−Mutual Information. The result is general enough to induce, as special cases, bounds involving χ2-divergence, Hellinger distance, Total Variation, etc. Moreover, it also recovers a result involving Rényi’s α−divergence. As an application, we provide bounds on the generalization error of learning algorithms via f−divergences.
Issue profile : environmental issues and the storage of CO2

Increasing CO2 emissions will lead to climate change and ocean acidification with severe consequences for ecosystems and for human society. Strategies are being sought to reduce emissions including the geological storage of CO2. Existing studies operate within existing oil and gas regulatory frameworks, but if other non-oil reservoir geological formations are used these existing regulations may not apply. At UK and European levels the potential environmental impacts of uncontrolled CO2 releases from storage sites have been highlighted to be of significance for regulators. Thus a new regulatory framework may be needed. The precautionary principle is likely to be adopted by regulators, so it is important that the effects of acute and chronic exposures of ecosystems to CO2 leakages are evaluated. Consequently, existing regulations are likely to be developed to include specific recommendations concerning leakages. This review shows that many basic data simply do not exist to assist regulators in this process
Meteorological Study for Gangotri Glacier and Its Comparison with Other High Altitude Meteorological Stations in Central Himalayan Region

In spite of the vital role of high altitude climatology in melting of snow and glaciers, retreat or advancement of glaciers, flash floods, erosion and sediment transport, etc., weather conditions are not much studied for the high altitude regions of Himalayas. In this study, a comprehensive meteorological analysis has been made for the Gangotri Meteorological Station (Bhagirathi Valley, Garhwal Himalayas) using data observed for four consecutive melt seasons (2000–2003) covering a period from May to October for each year. The collected meteorological data includes rainfall, temperature, wind speed and direction, relative humidity, sunshine hours and evaporation. The results and their distribution over the different melt seasons were compared with available meteorological records for Dokriani Meteorological Station (Dingad Valley, Garhwal Himalayas) and Pyramid Meteorological Station (Khumbu Valley, Nepal Himalayas). The magnitude and distribution of temperature were found to be similar for different Himalayan regions, while rainfall varied from region to region. The influence of the monsoon was meagre on the rainfall in these areas. July was recorded to be the warmest month for all the regions and, in general, August had the maximum rainfall. For all the stations, daytime up-valley wind speeds were 3 to 4 times stronger than the nighttime down-valley wind speeds. It was found that the Gangotri Glacier area experienced relatively low humidity and high evaporation rates as compared to other parts of the Himalayas. Such analysis reveals the broad meteorological characteristics of the high altitude areas of the Central Himalayan region.
Evolution of paraglacial coasts in response to changes in fluvial sediment supply

Abstract Paraglacial coastal systems are formed on or proximal to formerly ice-covered terrain from sediments with direct or indirect glacial origin. This review addresses the roles of tectonic controls, glacial advances and retreats, sea-level changes, and coastal processes in sediment production, delivery and redistribution along the paraglacial Gulf of Maine coast (USA and Canada). Coastal accumulation forms are compositionally heterogeneous and found primarily at the seaward edge of the Gulf's largest estuaries; their existence is directly attributable to the availability of glacial sediments derived from erosion of weathered plutons within coastal river basins. Multiple post-glacial sea-level fluctuations drove the redistribution of these sediments across the modern lowland and inner shelf. Central to the formation of barrier systems was the paraglacial sand maximum, a time-transgressive phase of relative sea-level fall and enhanced fluvial sand export c. 2000–4000 years following deglaciation. Vast quantities of sand and gravel were reworked landward during the subsequent transgression and combined with additional riverine sediments to form the modern barrier systems. Today, reduced fluvial sediment loads, anthropogenic modifications of barrier and river systems, and sea-level rise have combined to exacerbate long-term coastal erosion and may eventually force these barriers toward a state of rapid landward migration.
Roy's largest root under rank-one alternatives: The complex valued case and applications

The largest eigenvalue of a Wishart matrix, known as Roy's largest root (RLR), plays an important role in a variety of applications. Most works to date derived approximations to its distribution under various asymptotic regimes, such as degrees of freedom, dimension, or both tending to infinity. However, several applications involve finite and relative small parameters, for which the above approximations may be inaccurate. Recently, via a small noise perturbation approach with fixed dimension and degrees of freedom, Johnstone and Nadler derived simple yet accurate stochastic approximations to the distribution of Roy's largest root in the real valued case, under a rank-one alternative. In this paper, we extend their results to the complex valued case. Furthermore, we analyze the behavior of the leading eigenvector by developing new stochastic approximations. Specifically, we derive simple stochastic approximations to the distribution of the largest eigenvalue under five common complex single-matrix and double-matrix scenarios. We then apply these results to investigate several problems in signal detection and communications. In particular, we analyze the performance of RLR detector in cognitive radio spectrum sensing and constant modulus signal detection in the high signal-to-noise ratio (SNR) regime. Moreover, we address the problem of determining the optimal transmit-receive antenna configuration (here optimality is in the sense of outage minimization) for rank-one multiple-input multiple-output Rician Fading channels at high SNR.
A Holocene CO2 record from the stomatal index of subfossil Salix herbacea L. leaves from northern Sweden

A stomatal-based method of palaeo-CO2 estimation has been applied to a temporally detailed sequence of leaves from a high-latitude lake (68°N) in northern Sweden spanning the last 9000 years. The resulting atmospheric CO2 reconstruction documents the onset of a gradual increase c. 5000 years before present indicating that the carbon cycle has not been in steady state over this time. Stable carbon isotope (δ13C) measurements of the subfossil leaves constrain the interpretation of the inferred changes in the operation of the global carbon cycle. The δ13C data reveal no marked or systematic shifts towards more negative values indicating that the CO2 addition to the atmosphere 5000–1000 yr BP may have been predominantly of oceanic origin.
Squared 𝓁2 Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations

Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmented samples, a torrent of methods regularizing the distance between embeddings/representations of the original samples and their augmented counterparts have been introduced. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. Our analysis suggests the ideal choices of regularization correspond to various assumptions. With an invariance test, we argue that regularization is important if the model is to be used in a broader context than the accuracy-driven setting because non-regularized approaches are limited in learning the concept of invariance, despite equally high accuracy. Finally, we also show that the generic approach we identified (squared $\ell_2$ norm regularized augmentation) outperforms several recent methods, which are each specially designed for one task and significantly more complicated than ours, over three different tasks.
Evaluation of the effects of general circulation models' subgrid variability and patchiness of rainfall and soil moisture on land surface water balance fluxes

Many existing general circulation models (GCMs) use so-called ‘bucket algorithms’ to represent land-surface hydrology. Wood et al. (1992) presented a generalization of the simple bucket representation, based on their variable infiltration capacity (VIC) model. The VIC model, in essence, assumes a statistical distribution of bucket sizes within the grid square. In this way, it provides a simple, computationally efficient and yet physically realistic model of land-surface hydrology. A preliminary attempt is made here, using this simple model, to evaluate the effects of the spatial variability of rainfall intensities and the resulting soil moisture within a hypothetical GCM grid square. Rainfall is assumed to be patchy, with only a fraction of the grid square being wetted by rainfall at any given time. Within the wetted area, however, rainfall is allowed to vary randomly in space. Evaporation during interstorm periods is estimated from the rest of the grid square by a simple non-linear function of the available soil moisture. The soil moisture is also assumed to be patchy and spatially variable due to the antecedent rainfall that caused it. A number of simplifying assumptions have been made in the model about the redistribution of soil moisture at the end of storm and interstorm periods, and the effects of a vegetation canopy have been ignored. The model is applied, under a variety of conditions, to estimate the biases in the modelled water balance fluxes if the assumed spatial heterogeneity is neglected. The model is also used to simulate the long-term water balance dynamics under assumed hypothetical storm and inter-storm climatic inputs, to see how the steady-state hydrological regime is affected by spatial variability. These simulations are relevant to current efforts towards developing simple parameterizations of land-surface hydrology that explicitly incorporate subgrid heterogeneity.
An Optimal Algorithm for Stochastic and Adversarial Bandits

We provide an algorithm that achieves the optimal (up to constants) finite time regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and time horizon. The result provides a negative answer to the open problem of whether extra price has to be paid for the lack of information about the adversariality/stochasticity of the environment. We provide a complete characterization of online mirror descent algorithms based on Tsallis entropy and show that the power ${\alpha} = \frac{1}{2}$ achieves the goal. In addition, the proposed algorithm enjoys improved regret guarantees in two intermediate regimes: the moderately contaminated stochastic regime defined by Seldin and Slivkins (2014) and the stochastically constrained adversary studied by Wei and Luo (2018). The algorithm also obtains adversarial and stochastic optimality in the utility-based dueling bandit setting.
Analysis of seasonal variations in mass balance and meltwater discharge of the tropical Zongo Glacier by application of a distributed energy balance model

[1] A distributed energy balance model was applied to Zongo Glacier, Bolivia (16°S, 6000–4900 m above sea level, 2.4 km2), to investigate atmospheric forcing that controls seasonal variations in the mass balance and in meltwater discharge of glaciers in the outer tropics. Surface energy fluxes and melt rates were simulated for each 20 × 20 m2 grid cell at an hourly resolution, for the hydrological year 1999–2000, using meteorological measurements in the ablation area. Model outputs were compared to measurements of meltwater discharge, snow cover extent, and albedo at two weather stations set up on the glacier. Changes in melt rate in three distinct seasons were related to snowfall and cloud radiative properties. During the dry season (May to August), the low melt rate was mainly caused by low long-wave emission of the cloudless thin atmosphere found at these high altitudes. From September to December, meltwater discharge increased to its annual maximum caused by an increase in solar radiation, which was close to its summer peak, as well as a decrease in glacier albedo. From January on, melt was reduced by snowfalls in the core wet season via the albedo effect but was maintained thanks to high long-wave emission from convective clouds. The frequent changes in snow cover throughout the long ablation season lead to large vertical mass balance gradients. Annual mass balance depends on the timing and length of the wet season, which interrupts the period of highest melt rates caused by solar radiation.
PALEOMAGNETIC EVIDENCE OF AN EARLY PALEOZOIC ROTATED TERRANE IN NORTHWEST ARGENTINA : A CLUE FOR GONDWANA-LAURENTIA INTERACTION?

Paleomagnetic results from three Lower Ordovician units in northwest Argentina are reported. When assessed with available geologic evidence, these data permit the definition of the Puna Oriental–Famatina rotated terrane. Paleogeographic reconstructions based on these paleomagnetic results place Puna Oriental–Famatina in the South Iapetus ocean, close to the Gondwana margin and near some of the Lower-Middle Ordovician Central Mobile belt terranes of the Northern Appalachians. The accretion of Puna Oriental–Famatina may have resulted from the closure of this ocean in Middle Ordovician time.
Power and cost aware localized routing with guaranteed delivery in unit graph based ad hoc networks

In a localized routing algorithm, each node currently holding a message makes forwarding decision solely based on the position information about itself, its neighbors and destination. In a unit graph, two nodes can communicate if and only if the distance between them is no more than the transmission radius, which is the same for each node. This paper proposes localized routing algorithms, aimed at minimizing total power for routing a message or maximizing the total number of routing tasks that a network can perform before a partition. The algorithms are combinations of known greedy power and/or cost aware localized routing algorithms and an algorithm that guarantees delivery. A shortcut procedure is introduced in later algorithm to enhance its performance. Another improvement is to restrict the routing to nodes in a dominating set. These improvements require two-hop knowledge at each node. The efficiency of proposed algorithms is verified experimentally by comparing their power savings, and the number of routing tasks a network can perform before a node loses all its energy, with the corresponding shortest weighted path algorithms and localized algorithms that use fixed transmission power at each node. Significant energy savings are obtained, and feasibility of applying power and cost-aware localized schemes is demonstrated. Copyright © 2004 John Wiley & Sons, Ltd.
Electrical Conductivity in Soils: Underlying Phenomena

Electrical conductivity can be accurately and readily measured in the laboratory and in the field, with minimal electrode effects even in high specific surface soils and∕or high ionic concentration pore fluids. Electrical conductivity combines the contributions of particle conduction, surface conduction and pore fluid conduction, and the effects of particle shape and fabric. The interplay between participating soil parameters is often obscured in typical empirical equations, such as Archie’s law. New experimental results show that surface conduction is an important contributor to global soil conduction in high specific surface soils that are saturated with low-ionic concentration pore fluids; the relevance of surface conduction increases with decreasing porosity. On the other hand, pore fluid conduction prevails as the conductivity of the electrolyte and the porosity of the soil increase. Furthermore, low frequency conductivity anisotropy increases with increasing ionic concentration. Simple yet robust mi.
Porosity Loss, Fluid Flow, and Mass Transfer in Limestone Reservoirs: Application to the Upper Jurassic Smackover Formation, Mississippi

Ooid grainstones of the Upper Jurassic Smack over Formation are buried to a depth of over 6 km and exposed to temperatures in excess of 200°C at Black Creek field, Mississippi. Combined effects of mechanical compaction, intergranular pressure solution, and cementation have reduced intergranular porosity of these ooid grainstones to 0%, indicating that porosity reduction has gone to completion. Modal analysis of 24 samples lacking preburial cements indicates that from the original 40% porosity, 13 porosity units (range: 4 to 21) were lost by mechanical compaction, 15 porosity units (range: 8 to 23) were reduced by intergranular pressure solution, and 12 porosity units (range: <1 to 26) were destroyed by cementation. Intergranular pressure solution caused an average of 28% (range: 15 to 51%) vertical shortening in Smackover ooid grainstones. Under ideal conditions, the 28% vertical shortening will generate enough calcium carbonate to precipitate 10% calcite cement. This is close to the measured volume of cements in Smackover grainstones (12%), suggesting that intergranular pressure solution provided most of the calcite cement present. No external sources of calcium carbonate are required. Fine-grained samples that experienced high degrees of intergranular pressure solution and contain only small amounts of cement occur at the top of the reservoir, whereas coarse-grained samples with abundant cement and low degrees of pressure solution occur in the middle and basal parts of the reservoir, suggesting that the fine-grained intervals acted as sources and the coarse-grained intervals as sinks for calcium carbonate. Mass transfer of pressure solution-generated calcium carbonate from the top of the unit to precipitation sites in the middle and basal parts of the reservoir could have occurred by a non-Rayleigh-type convection cell. Due to calcite's reverse solubility with respect to temperature, the cooling, upward-moving limb of the convection cell would become progressively more undersaturated, and hence able to transport more dissolved calcium carbonate released by intergranular pressure solution in the upper portion of the reservoir. Pore fluids descending in the downward-moving limb of the cell would become progressively more supersaturated, and calcium carbonate would tend to precipitate as cement in the middle and basal parts of the reservoir as fluids become progressively hotter.
Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework

The New Keynesian framework has emerged as the workhorse for the analysis of monetary policy and its implications for inflation, economic fluctuations, and welfare. It is the backbone of the new generation of medium-scale models under development at major central banks and international policy institutions, and provides the theoretical underpinnings of the inflation stability-oriented strategies adopted by most central banks throughout the industrialized world. This graduate-level textbook provides an introduction to the New Keynesian framework and its applications to monetary policy. Using a canonical version of the New Keynesian model as a reference framework, Jordi Gali explores issues pertaining to the design of monetary policy, including the determination of the optimal monetary policy and the desirability of simple policy rules. He analyzes several extensions of the baseline model, allowing for cost-push shocks, nominal wage rigidities, and open economy factors. In each case, the implications for monetary policy are addressed, with a special emphasis on the desirability of inflation targeting policies. The most up-to-date and accessible introduction to the New Keynesian framework available Uses a single benchmark model throughout Concise and easy to use Includes exercises An ideal resource for graduate students, researchers, and market analysts
Coded ResNeXt: a network for designing disentangled information paths

To avoid treating neural networks as highly complex black boxes, the deep learning research community has tried to build interpretable models allowing humans to understand the decisions taken by the model. Unfortunately, the focus is mostly on manipulating only the very high-level features associated with the last layers. In this work, we look at neural network architectures for classiﬁcation in a more general way and introduce an algorithm which deﬁnes before the training the paths of the network through which the per-class information ﬂows. We show that using our al-gorithm we can extract a lighter single-purpose binary clas-siﬁer for a particular class by removing the parameters that do not participate in the predeﬁned information path of that class, which is approximately 60% of the total parameters. Notably, leveraging coding theory to design the information paths enables us to use intermediate network layers for making early predictions without having to evaluate the full network. We demonstrate that a slightly modiﬁed ResNeXt model, trained with our algorithm, can achieve higher clas-siﬁcation accuracy on CIFAR-10/100 and ImageNet than the original ResNeXt, while having all the aforementioned properties. 1
Pseudo Supervised Metrics: Evaluating Unsupervised Image to Image Translation Models In Unsupervised Cross-Domain Classification Frameworks

The ability to classify images accurately and efficiently is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a labor-intensive human effort. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. This paper introduces a new method called Pseudo Supervised Metrics that was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the Fr´echet Inception Distance (FID) which were designed to evaluate the model in terms of the quality of the generated im∗