Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated processing to derive dip angles of englacial radar reflectors in ice sheets

Abstract We present a novel automated processing method for obtaining layer dip from radio-echo sounding (RES) data. The method is robust, easily applicable and can be used to process large (several terabytes) ground and airborne RES datasets using modest computing resources. We give test results from the application of the method to two Antarctic datasets: the Fletcher Promontory ground-based radar dataset and the Wilkes Subglacial Basin airborne radar dataset. The automated RES processing (ARESP) method comprises the basic steps: (1) RES noise reduction; (2) radar layer identification; (3) isolation of individual ‘layer objects’; (4) measurement of orientation and other object properties; (5) elimination of noise in the orientation data; and (6) collation of the valid dip information. The apparent dip datasets produced by the method will aid glaciologists seeking to understand ice-flow dynamics in Greenland and Antarctica: ARESP could enable a shift from selective regional case studies to ice-sheet-scale studies.
Blind assistant tool using deep learning

—This According to the World Health Organization, around 40 million people are blind, and another 250 million have some sort of vision impairment. Unfortunately, those with vision impairments typically cannot view images. Our research proposes an approach that can automatically generate audio description of images, which can substantially assist the visually impaired. There are less opportunities for visually impaired people to interact in today’s environment, this paper is necessary. For those who are blind or visually impaired, using an image describer can be an enabler. The processed visuals that the visually handicapped cannot see are then given a suitable description and outputted as voice. A simplistic web-interface is developed using Stream lit. The paper uses the Inceptionv3 model as the feature extractor. The Transformer encoder-decoder model to extract image characteristics and generate the text description of the image, which is then converted to an audio using Google Text-to-Speech converter. The generated captions must now precisely reflect the image’s graphical information and be highly syntactically understandable. BLEU, METEOR, ROUGE-L, CIDEr score is used for evaluation after captioning. The results were shown to be more accurate, and as a result, they may help the blind access digital media.
A Strong Baseline for Generalized Few-Shot Semantic Segmentation

We evaluated our method on two widely-used few-shot segmentation benchmarks: PASCAL-5 [5] and COCO-20 [5]. The former is built based on PASCAL VOC 2012 [1] (containing 20 semantic classes) with additional annotations from SDS [2], while the latter is built from MS-COCO [4] (containing 80 semantic classes). In both datasets, the classes are split into 4 disjoint subsets, and the experiments are done in a cross-validation manner. For each fold, the set of novel classes is extracted from one of these subsets while the union of the remaining subsets will be the set of base classes. Furthermore, as discussed in the main paper, we have introduced a new scenario, where the number of novel classes is increased, referred to as PASCAL-10. The semantic classes in each fold of PASCAL-10 are detailed in Tab. 1.
Global Journal of Business, Economics and Management: Current Issues

Zasadzki Pawel a , Department of Economic and Regional Policy, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland. https://orcid.org/0000-0002-8626-9873 Lukasz Markowski , Department of Economic and Regional Policy, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland. https://orcid.org/0000-0002-3921-1820 Damian Opalach c Department of Economic and Regional Policy, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland. https://orcid.org/0000-0002-4484-3419
A Geological and Geophysical Information System for Eurasia

Abstract : Topography and heterogeneous crustal structure have major effects on the propagation of regional seismic phases. We are collecting topographical, geological, and geophysical datasets for Eurasia into an information system that can be accessed via Internet connections. Now available are digital topography, satellite imagery, and data on sedimentary basins and crustal structure thicknesses. We are expanding the scope of our information system to include data on the Middle East and North Africa. Under the auspices of several other ongoing projects at Cornell we have collected a very large literature collection, satellite imagery and some detailed datasets, especially in Syria and Morocco. New datasets for Eurasia include maps of depth to Moho beneath Europe and Scandinavia. We have created regularly spaced grids of the crustal thickness values from these maps that can be used to create profiles of crustal structure. These profiles can be compared by an analyst or an automatic program with the crustal seismic phases received along the propagation path to better understand and predict the path effects on phase amplitudes, a key to estimating magnitudes and yields, and for understanding variations in travel-time delays for phases such as Pn, important for improving regional event locations. The gridded data could also be used to model propagation of crustal phases in three dimensions. In the near future, we will be making new databases from the Middle East and North Africa available via the Internet. We encourage ARPA and Air Force researchers to contact us about gaining access to our databases at these computer mail addresses: "eric/geology.cornell. edu" -or- "fielding/seismo.css. gov".
SANO: Score-based Diffusion Model for Anomaly Localization in Dermatology

Supervised learning for dermatology requires a large volume of annotated images, but collecting clinical data is costly, and it is virtually impossible to cover all clinical cases. Unsupervised anomaly localization circumvents this problem by learning the healthy data distribution. However, algorithms which use a generative model and localize pathologic regions based on a reconstruction error are not robust to domain shift, which is a problem for dermatology due to the low level of standardization expected in many applications. Our method, SANO, uses score-based diffusion models to produce a log-likelihood gradient map highlighting areas that contain abnormalities. A segmentation mask can then be calculated based on deviations from typical values observed during training. After benchmarking SANO on an industrial dataset, we train it on a public non-clinical dataset of healthy hand images without ornaments, evaluate it on the task of detecting jewelry within images from the same dataset, and prove its robustness by using it on clinical pictures to localize hand eczema. We demonstrate that SANO outperforms competing approaches from the literature without introducing additional computational costs.
Queering Environmental Justice: Unequal Environmental Health Burden on the LGBTQ+ Community.

The LGBTQ+ (lesbian, gay, bisexual, transgender/-sexual, queer or questioning, intersex, asexual, and all subsects) population has been the target of federal and state discriminatory policies leading to high levels of institutional discrimination in the housing, employment, and health sectors. Social determinants of health such as housing conditions, economic opportunities, and access to health care may negatively and disproportionately affect the LGBTQ+ population and reduce their capacity to respond to environmental harm (e.g., obtaining necessary medical care). Social determinants of health have been shown to be associated with unequal harmful environmental exposure, primarily along lines of race/ethnicity and socioeconomic status. However, chronic diseases, such as respiratory diseases, cardiovascular disease, and cancer, associated with environmental exposure have been shown to occur in higher rates in the LGBTQ+ population than in the cisgender, heterosexual population. We explore how environmental exposures may disproportionately affect the LGBTQ+ population through examples of environmental exposures, health risks that have been linked to environmental exposures, and social institutions that could affect resilience to environmental stressors for this population. We provide recommendations for policymakers, public health officials, and researchers. (Am J Public Health. 2022;112(1):79-87. https://doi.org/10.2105/AJPH.2021. 306406).
Influence of Strong Winds on Snow Distribution and Avalanche Activity

In a local range, crest winds were compared with winds at lower stations to make it possible to initiate a drift-transport model which would predict snow accumulation patterns on leeward slopes. Corrections to the model input were made after consideration of detailed drift-flux measurements in the lowest 2 m above snow surface. Good agreement was found between the total length of large avalanches in a path near the crest, the appropriate wind reading and the corrected snow-depth increments in the rupture zone. Control of medium-sized avalanches likely to cause injury to skiers can be improved with the proposed method.
The Formulation of the Andr Robert MC (Mesoscale Compressible Community) Model

ABSTRACT A description of the numerical formulation of the dynamics module of the Mesoscale Compressible Community (MC2) model is presented. This model is based on the fully elastic, semi-implicit semi-Lagrangian model developed by Tanguay et al. (1990). This version was extended to incorporate topography by Denis (1990), and later variable vertical resolution was added as an option. This article is a condensed version of an extensive report by Bergeron et al. (1994) that documents all the numerical aspects of the MC2 model. The performance of the model is illustrated through a sample of results obtained on a wide range of physical problems.
Edinburgh Research Explorer Observation of the $B^+ \to D^{*-}K^+\pi^+$ decay

The B þ → D (cid:1) − K þ π þ decay potentially provides an excellent way to investigate charm meson spectroscopy. The decay is searched for in a sample of proton-proton collision data collected with the LHCb detector at center-of-mass energies of 7 and 8 TeV, corresponding to an integrated luminosity of 3 fb − 1 . A clear signal is observed, and the ratio of its branching fraction to that of the B þ → D (cid:1) − π þ π þ normalization channel is measured to be B ð B þ → D (cid:1) − K þ π þ Þ = B ð B þ → D − π þ þ 39 (cid:3) 27 (cid:3) 10 − 2 ; where the first uncertainty is statistical and the second is systematic. This is the first observation of the B þ → D (cid:1) − K þ π þ decay.
Diversification: towards a new paradigm for Africa’s development

This paper is articulated around the following chapters. After this introduction, the first chapter will underscore Africa’s development stakes. This analysis will enable us to tackle the problem of modernizing and diversifying the productive fabric of African economies. The second chapter will be devoted to the recent renewal of the diversification problem. It will enable us to underscore the new concerns and the development of the debate on this issue since the failure of the experiences of the 1970’s. The third chapter will be devoted to the presentation and the discussion of diversification gauging tools. The fourth chapter will present facts connected to the diversification of African economies. In this chapter, we took a comparative approach that would allow us to put into perspective the different sub-regional experiences as well as link Africa’s economic history in comparison to that of Asia and Latin America. In the fifth chapter, we will abandon the descriptive approach in favour of analytical elements and attempt to determine factors justifying diversification. This approach will be reflected upon in detail in the sixth chapter where we will focus our attention on the relation between diversification and economic growth. Finally, in the last chapter we will look into formulating economic policy recommendations for African countries in the area of diversification.
Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey

Building autonomous machines that can explore open-ended environments, discover possible interactions and autonomously build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by autonomous and intrinsically motivated learning agents that can generate, select and learn to solve their own problems. In recent years, we have seen a convergence of developmental approaches, and developmental robotics in particular, with deep reinforcement learning (RL) methods, forming the new domain of developmental machine learning. Within this new domain, we review here a set of methods where deep RL algorithms are trained to tackle the developmental robotics problem of the autonomous acquisition of open-ended repertoires of skills. Intrinsically motivated goal-conditioned RL algorithms train agents to learn to represent, generate and pursue their own goals. The self-generation of goals requires the learning of compact goal encodings as well as their associated goal-achievement functions, which results in new challenges compared to traditional RL algorithms designed to tackle pre-defined sets of goals using external reward signals. This paper proposes a typology of these methods at the intersection of deep RL and developmental approaches, surveys recent approaches and discusses future avenues.
Effect of yogic package on flexibility and muscular endurance of sports college athlete

-The purpose of the study was to find out the effect of the yogic package on flexibility and muscular endurance on sports college athlete. For this purpose, 50 subjects were selected by random sampling technique and after the pre-test, all the subjects were scattered into two equal groups randomly i.e. experimental group and control group (n=25 in each group). The experimental group underwent eight weeks of yogic package program whereas the control group was not given any kind of training. Pre-test and post-test were applied to get the data from both groups. The difference between groups was analyzed by applying a t-test for the significant differences at 0.05 levels. The findings of the test showed the significant value of the t-ratio for selected variables in the experimental group. The results were found significant. The findings of the study revealed that the eight weeks yogic practice program is very beneficial to increase the flexibility and muscular endurance of sports college athlete.
Finding Generalization Measures by Contrasting Signal and Noise

Generalization is one of the most fundamental challenges in deep learning, aiming to predict model performances on unseen data. Empirically, such predictions usually rely on a validation set, while recent works showed that an unlabeled validation set also works. Without validation sets, it is extremely difficult to obtain non-vacuous generalization bounds, which leads to a weaker task of finding generalization measures that monotonically relate to generalization error. In this paper, we propose a new generalization measure REF Complexity (RElative Fitting degree between signal and noise), motivated by the intuition that a given model-algorithm pair may generalize well if it fits signal ( e.g. , true labels) fast while fitting noise ( e.g. , random labels) slowly. Empirically, REF Complexity monotonically relates to test accuracy in real-world datasets without accessing additional validation sets, achieving − 0 . 988 correlation on CIFAR-10 and − 0 . 960 correlation on CIFAR-100. We further theoretically verify the utility of REF Complexity under three different cases, including convex and smooth regimes with stochastic gradient descent, smooth regimes (not necessarily convex) with stochastic gradient Langevin dynamics, and linear regimes with gradient descent. The code is available at https://github.com/96208 6838/REF-complexity .
Risk-balanced territory design optimization for a Micro finance institution

Micro finance institutions (MFIs) play an important role in emerging economies as part of programs that aim to reduce income inequality and poverty. A territory design that balances the risk of branches is important for the profitability and long-term sustainability of a MFI. In order to address such particular business needs, this paper proposes a novel risk-balanced territory planning model for a MFI. The proposed mixed integer programming model lets the MFI choose the location of the branches to be designated as territory centers and allocate the customers to these centers with respect to planning criteria such as the total workload, monetary amount of loans and profit allocation while balancing the territory risk. This model is solved using a branch and cut based hybrid-heuristic framework. We discuss the impact of the risk balancing and merits of the proposed model.
Of Small Steps and Big Leaps: Collaborators Impact on Confronters’ Ability to Influence Corporate Responsibility

In their efforts to influence corporate responsibility, nonprofit organizations (NPOs) are increasingly focusing directly on companies – either by entering into collaborative relationships with, or by exerting public pressure on them. While both approaches can be instrumental for NPOs with respect to influencing corporate responsibility, the way in which these two approaches interact has not been extensively researched. Drawing on institutional change and social movement theory as well as nonprofit-business collaboration literature, this qualitative study sheds further light on how more confrontational NPOs experience the value and disvalue of collaborative approaches by more collaborative NPOs with respect to their own ability to influence corporate responsibility – i.e. how they experience ‘collaborator effects’. The results indicate that collaborative approaches can support confrontational ones in re-institutionalization efforts surrounding corporate responsibility, but have a negative bearing on their efforts at de-institutionalization. The type of NPO involved in collaborative efforts with companies exerts an important influence on perceived collaborator effects.
CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data

Community detection is a fundamental task in network analysis. Learning underlying network structures has brought deep insights into the understanding of complex systems. While many methods have focused on clustering nodes into blocks, few accounts for the fact that interactions may exhibit edge-level clustering, which we call categories. Real network data often arise via a series of interactions. Interactions in complex systems can often be clustered into different categories and node-level community structures that depend on the category. In this paper, we introduce a category-and-block edge exchangeable model (CataBEEM) to study interaction networks with joint latent interaction-level category and node-level community structures. In particular, the proposed method models the network from the interaction process perspective and allows the incorporation of prior knowledge from auxiliary interaction-wise information. We derive an efficient variational inference algorithm that can be applied to networks consisting of millions of interactions and provide the theoretical bound of the misspecification rate. We demonstrate the effectiveness of our method in various simulation settings and apply the method to TalkLife data, a large-scale online peer-to-peer support network. We show CataBEEM detects more temporally consistent community structures and has better predictions than other methods.
Opponent-Limited Online Search for Imperfect Information Games

In recent years, online search has been playing an increasingly important role in imperfect information games (IIGs). Previous online search is known as common-knowledge subgame solving, which has to consider all the states in a common-knowledge closure. This is only computationally tolerable for medium size games, such as poker. To handle larger games, order-1 Knowledge-Limited Subgame Solving (1-KLSS) only considers the states in a knowledge-limited closure, which results in a much smaller subgame. However, 1-KLSS is unsafe. In this paper, we first extend 1-KLSS to Safe-1-KLSS and prove its safeness. To make Safe-1-KLSS applicable to even larger games, we propose Opponent-Limited Subgame Solving (OLSS) to limit how the opponent reaches a subgame and how it acts in the subgame. Limiting the opponent’s strategy dramatically reduces the subgame size and improves the efficiency of subgame solving while still preserving some safety in the limit. Experiments in medium size poker show that Safe-1-KLSS and OLSS are orders of magnitude faster than previous common-knowledge subgame solving. Also, OLSS significantly improves the online performance in a two-player Mahjong game, whose game size prohibits the use of previous common-knowledge subgame-solving methods.
Combining Higher-Order Wavelets and Discontinuity meshing: A Compact Representation for Radiosity

The radiosity method is used for global illumination simulation in diffuse scenes, or as an intermediate step in other methods. Radiosity computations using Higher-Order wavelets achieve a compact representation of the illumination on many parts of the scene, but are more expensive near discontinuities, such as shadow boundaries. Other methods use a mesh, based on the set of discontinuities of the illumination function. The complexity of this set of discontinuities has so far proven prohibitive for large scenes, mostly because of the difficulty to robustly manage a geometrically complex set of triangles. In this paper, we present a method for computing radiosity that uses higher-order wavelet functions as a basis, and introduces discontinuities only when they simplify the resulting mesh. The result is displayed directly, without post-processing.
Determination of ethylene regulated changes in anthocyanin composition during ripening in blueberry

Name, Mailing and Email Address of Principal Investigator(s): Principal Investigator CoPrincipal Investigator CoPrincipal Investigator Savithri Nambeesan Anish Malladi Renée Holland Asst. Research Scientist/ Faculty Associate Professor Area Blueberry Agent University of Georgia/ CAES University of Georgia Extension-Bacon County Department of Horticulture Department of Horticulture University of Georgia 1111 Miller Plant Sciences 1111 Miller Plant Sciences 203 S. Dixon St., Suite 3 Athens, GA 30602 Athens, GA 30602 Alma, GA 31510 Email: sunamb@uga.edu Email: malladi@uga.edu Email: reneemh@uga.edu Phone: 706-542-0777 Phone: 706-542-0783 Phone: 912-632-5601
An Introduction to Probability Theory and Its Applications, Vol. 2

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