Miracle-dz/UniFAR-FTUs huggingface dataset:
Ahorros En Impuestos: Una Cuantificación Teórica-Práctica Aplicada (Tax Shields: A Theoretical-Practical Quantification Applied)

Spanish Abstract: Los ahorros en impuestos (AI) o escudos fiscales son subsidios que el gobierno da a las empresas por cada peso gastado y deducible de su impuesto de renta; estos gastos son comunmente los gastos financieros, perdidas en cambio por prestamos en divisas y otros. A causa de esta externalidad se genera un valor adicional para las empresas o proyectos y su patrimonio.Es asi que resulta necesario presentar y utilizar una metodologia precisa que refleje adecuadamente la realidad de las empresas y proyectos en terminos de sus ahorros en impuestos. Con el objetivo de aportar en ese proceso nace el presente documento, para responder, en terminos generales, cual es la importancia de la correcta estimacion de los ahorros en impuestos en la valoracion financiera. De esta manera, se presenta y valida un procedimiento existente para calcular correctamente los ahorros en impuestos segun cada caso que se presente en las empresas y proyectos y ademas, encontrar evidencia empirica que mida la magnitud de que tanto se pierde por hacerlo mal. Para lograr esto se estima la magnitud de las discrepancias generadas con el estudio especifico de una firma hipotetica, ademas se realiza un analisis cuantitativo de los ahorros en impuestos de empresas estadounidenses y colombianas.English Abstract: Tax shields are subsidies that governments provides to companies for every dollar spent and deductible on their income tax; these expenses are commonly financial expenses, exchange losses on loans in foreign currency and others. This externality generates an additional value for companies or projects.I present an existing procedure to calculate correctly the tax shields in companies and projects. To find empirical evidence to measure the magnitude by doing it wrong, I analyze the magnitude of the discrepancies generated by the specific study of a hypothetical firm and also, I inspect the tax shields of US and Colombian companies.
Influence of conservation tillage on soil properties

ABSTRACT: Conservation of soil moisture is one of the major advantages of no-till crop production systems. In a long-term tillage study, higher soil moisture under no-till corn production was observed as compared with that under conventional tillage throughout the growing season. Significantly less evaporation occurred under no-till early in the growing season. This conservation of soil water may carry the no-till crop through short drought periods without severe moisture stresses developing in the plants. However, the extra water conserved under no-till can occasionally be detrimental under conditions in which excessive soil water contributes to denitrification losses. Soil compaction in no-tillage soils was not found to be a problem. Saturated hydraulic conductivity measurements suggest better water movement in no-tillage compared with conventional tillage. After 10 years of continuous no-till corn production, no deterioration of soil physical properties was observed. The most obvious chemical change was the rapid acidification of the soil surface when high nitrogen fertilizer rates were used. Associated with reduced soil pH were increased levels of exchangeable aluminum and manganeses. Exchangeahle calcium was sgnificantly lower for no-till at all soil depths and all nitrogen fertilizer rates compared with conventional tillage. Potassium was concentrated in the 0-to-5-centimeter soil layer under no-till and decreased with depth. Exchangeable magnesium in the 0-to-5-centimeter soil depth declined with increased nitrogen fertilizer and was lower in no-till than in conventional tillage. Organic matter was twice as high in the 0-to-5-centimeter soil depth compared with conventional tillage systems. This modified soil environment affects chemical reactions and the distribution and activity of microbes. It also has a significant effect on the fate of surface-applied nitrogen fertilizer. Methods and time of nitrogen fertilizer application may need some modification to reduce possible nitrogen losses by denitrification or temporary immobilization of nitrogen.
Reconstructing the Vertical Component of Ground Deformation from Ascending ALOS and Descending ENVISAT Datasets—A Case Study in the Cangzhou Area of China

Abstract. Due to the regular revisit periods of SAR platforms and the decorrelation of SAR images, the estimation of ground deformation retrieved from a particular SAR dataset may be of low resolution in both spatial and temporal dimensions. To enhance the spatial and temporal sampling rates in ground deformation estimation, a multiplatform SAR dataset combination strategy is proposed, based on the Multi-Temporal InSAR (MTInSAR) technique and the Same Name target point Pairs (SNPs) combination approach. The combination strategy has been tested and applied in the Cangzhou area, which has suffered from ground subsidence due mainly to groundwater extraction. The derived results show that the deformation monitoring sampling rates can be improved in the space and time dimensions when compared to the results obtained from a single-platform SAR dataset, which makes the multiplatform SAR dataset combination strategy useful for uneven (spatially variable) and nonlinear (temporally variable) deformation detection. Moreover, the results derived from the combination strategy tend to be more reliable and have fewer noise points.
Factors influencing urban consumers on selecting electricity saving home appliances for managing energy consumption in Indonesia

Abstract This study aims to analyze vital indicators for urban consumer’s behavior regarding electricity saving appliances (ESA) to reduce energy consumption at home in Indonesia. The studied appliances are limited to two, refrigerator and air conditioner, as these appliances are the most dominant in consuming electricity energy. There are two equation models proposed for each appliance by using consumers’ perceptions (general and specific perceptions) and external (social environment, weather, and mass media) group variables, usage and habitual behaviors. Using stepwise regression and statistical approaches for 232 surveyed household consumers in Makassar city, the adjusted R2 value for four composed models is between 52% and 59%. Study findings reveal that specific perception significantly influences the usage of both appliances including the habit of consumers for energy saving. Among the examined external factors, the variable of mass media has significance in almost all models. Implications for policymakers suggest applying a priority approach in promoting energy saving to consumers with low specific perception based on the aspect of installed electric power capacity at home, and giving more information via mass media about attributes of ESA including introducing intervention strategies is essential to support sustainable electricity consumption from the consumer’s side.
Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking

Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife. Outlier measurements can severely corrupt estimated locations. This article describes a new suite of location estimation algorithms for such systems. The new algorithms detect and discard outlier time-of-arrival observations, which can be caused by non-line-of-sight propagation, radio interference, clock glitches, or an overestimation of the signal-to-noise ratio. The new algorithms also detect cases in which two locations are equally consistent with measurements and can usually select the correct one. The new algorithms can also infer approximate altitude information from a digital elevation map to improve location estimates close to one of the sensors. Finally, the new algorithms approximate the covariance matrix of location estimates in a simpler and more reliable way than the baseline algorithm. Extensive testing on real-world data involving mobile transmitters attached to wild animals demonstrates the efficacy of the new algorithms. Performance testing also shows that the new algorithms are fast and that they can easily cope with high-throughput real-time loads.
Analysis of Three-dimensional Geometrical Pore Parameters From Rock Weathering

Abstract Weathering produces porosity that influences rocks’ hydrologic properties and thus affects ecosystem processes. The objective of this research was to quantify three-dimensional geometrical pore parameters and characterize microstructure differences as rock weathers. Three granodiorite rock fragments (clasts) and two soil clods sampled from a moraine chronosequence in the eastern Sierra Nevada, California, were evaluated. Surface ages of the parent moraines, ranging from 78 to 120 ka, were used to approximate clast weathering. Samples were scanned at a volume element (voxel) size of 6.9 × 104 &mgr;m3 at the High-Resolution X-ray computed tomography (CT) facility at the University of Texas, Austin. Images were analyzed with the Three-dimensional Medial Axis Rock (3DMA) software (CT-3DMA). Porosity estimated by bulk density and CT methods indicated excellent agreement between the two methods. Characteristic coordination numbers increased with weathering, and the soil had the highest value; means for 78-ka rock and 100-ka rock and soil were 2.49, 2.68, and 4.32. The difference between rock treatments was not significant, but tended to increase with age. The difference between soil and rock was significant. The characteristic path length varied between 1.20 and 1.46 but was not different among rock age. The mean value for the soil was 0.86, and the difference between rock and soil was significant. Pore path tortuosity ranged from a high of 1.35 in the 78-ka rock to a low of 1.31 in the soil, with significant differences between rock and soil. Results suggest that rocks imaged with X-ray CT and analyzed in 3DMA will help quantify microstructural differences in pore geometry related to rock weathering.
Business school and museum partnership to change student perceptions on leadership

Universities and museums often operate on different educational paradigms and partnerships between museums and universities have been relatively unexplored in some disciplines, making it difficult to generate new and innovative patterns of learning, research and scholarship. The day-to-day complexities of such inter-disciplinary partnership often remain hidden with much of the collaborative work conducted by individuals without formal university support. In this paper, we advocate for a more systematic and sustained partnership between university museums and educators and present a case study of a partnership between an on-campus museum and a business school to teach students about responsible leadership. We outline the benefits and challenges of such interdisciplinary partnership and present a case study where we explore Object-Based Learning (OBL) as the central element of this collaboration.
Special Section: Data-Model Comparison Volcanism and the Little Ice Age

and early 19 th centuries. The LIA period also provides climate scientists with an opportunity to test their models against a time interval that experienced both significant volcanism and (perhaps) solar insolation variations. Such studies provide information on the ability of models to simulate climates and also provide a valuable backdrop to the subsequent 20 th century warming that
Neural network-based regression for heat transfer and fluid flow over in-line cylinder arrays with random pitch distances at low Reynolds number

Finding an arrangement, leading to a higher heat transfer and lower pressure drop, is crucial in the design of heat exchangers. Previous studies have primarily focused on regular arrangements with uniform pitch distances, which lack applicability to general configurations. In this study, we proposed a new procedure of a flow-learned building block (FLBB) to predict heat transfer in an in-line cylinder array with random pitch distances using a neural network-based regression analysis with a systematic data generation process. As a first step, we demonstrated the FLBB’s capability to predict the heat transfer and pressure drop in in-line cylinder arrays with random pitch distances at low Reynolds numbers from 1 to 100 for air ( $ Pr = \textrm{0}\textrm{.71} $ Pr=0.71). Subsequently, a high-order FLBB approach was proposed to address the spatial interdependency between neighbouring cylinders, particularly in scenarios where vortex shedding occurs in the wake of cylinders at increased Reynolds numbers. The high-order FLBB approach was then shown to successfully describe various flow and temperature patterns using cylinder arrays with random pitch distances. The proposed procedure exhibited remarkable efficiency, requiring only about 1 s. Furthermore, the FLBB was successfully extended to various flow regimes, even encompassing unseen Reynolds numbers from 1 to 100.
Oncofetal reprogramming in tumor development and progression: novel insights into cancer therapy

Abstract Emerging evidence indicates that cancer cells can mimic characteristics of embryonic development, promoting their development and progression. Cancer cells share features with embryonic development, characterized by robust proliferation and differentiation regulated by signaling pathways such as Wnt, Notch, hedgehog, and Hippo signaling. In certain phase, these cells also mimic embryonic diapause and fertilized egg implantation to evade treatments or immune elimination and promote metastasis. Additionally, the upregulation of ATP‐binding cassette (ABC) transporters, including multidrug resistance protein 1 (MDR1), multidrug resistance‐associated protein 1 (MRP1), and breast cancer‐resistant protein (BCRP), in drug‐resistant cancer cells, analogous to their role in placental development, may facilitate chemotherapy efflux, further resulting in treatment resistance. In this review, we concentrate on the underlying mechanisms that contribute to tumor development and progression from the perspective of embryonic development, encompassing the dysregulation of developmental signaling pathways, the emergence of dormant cancer cells, immune microenvironment remodeling, and the hyperactivation of ABC transporters. Furthermore, we synthesize and emphasize the connections between cancer hallmarks and embryonic development, offering novel insights for the development of innovative cancer treatment strategies.
A Rational Technique for Evaluating Proposed Expansions of Craney Island in the Elizabeth River, Virginia

A rational technique was developed for evaluating the expansion of an existing dredged material disposal site at Craney Island. The technique was based on analyzing the results from a three-dimensional numerical model, HEM-3D, applied in the James/Elizabeth River system. The criteria used for the evaluation consisted o f ( l ) physical parameters (tidal elevation, velocity, salinity and sedimentation potential), (2) flushing capacity (using flux calculation) and (3) special features (e.g., the front and eddy circulation). From a numerical modeling point of view, what the dredged material site expansion does is to modify the coastal shoreline and, through it, introduce the perturbations into an otherwise natural estuarine environment. The key tasks tor the evaluation are thus to measure these perturbations quantitatively, and make the appropriate comparisons between expansion options and the Base Case covering the variability in time and in space. In the Craney Island expansion project, we first address the spatial variability by conducting a global analysis, comprised of measuring root mean square and average differences throughout a broad area, and then conduct a local analysis for the effects within a more localized area. For temporal variability, the tidally induced quasi-periodic process was used in the global and local analysis first, and then the event-driven, episodic process was analyzed using a 6-month real time simulation. Upon implementation, the GI! 5 tools proved to be essential. The "spatial joint" under Geoprocessing and in-housedeveloped Avenue programs were extensively used, which enhance the efficiency and accuracy to the results significantly. Our overall objective is to achieve an environmental evaluation in an efficient, comprehensive, and objective manner. 1 Assistant Professor, Virginia Institute of Marine Science. Gloucester Point, VA 23062; Te1:804-684-7215; email: wang@vims.edu 2. Professor, Virginia Institute of Marine Science. Te1:804-684-7272~ email: boon@vims.edu 3. Graduate student, Virginia Institute of Marine Sc~'~i,ce. Te1:804-684-7703; email: mmchen@vims.edu
Scene matching between a quantitative map and a qualitative hand drawn sketch

The goal of this work is to perform scene matching between a sketched map and the scene depicted by the sketch, e.g., as represented by a precise physical map or an occupancy grid map (OGM) built by a robot. In this thesis, a novel method known as the Evolutionary Algorithm for Scene Matching (EASM) has been proposed for accomplishing this task. The method makes use of spatial relations and an evolutionary algorithm for accomplishing this task. The spatial relations between different objects in a scene are captured using the histogram of forces method and then an evolutionary algorithm is used to find the best histogram relational map. This histogram relational map is then translated into an object mapping. Experiments were run using two different scenes and several hand drawn sketches of each scene, which were collected during a user study. Scene matching was performed between a quantitative map and the qualitative sketches. Scene matching was also performed between occupancy grid maps and the sketches. A comparison was made between the proposed approach and the Fuzzy Sequential Nearest Neighbor (FSNN) approach. Experiments were also run for cases where the sketches and the maps had unequal number of objects.
StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter

Text-to-video (T2V) models have shown remarkable capabilities in generating diverse videos. However, they struggle to produce user-desired stylized videos due to (i) text's inherent clumsiness in expressing specific styles and (ii) the generally degraded style fidelity. To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image. Considering the scarcity of stylized video datasets, we propose to first train a style control adapter using style-rich image datasets, then transfer the learned stylization ability to video generation through a tailor-made finetuning paradigm. To promote content-style disentanglement, we remove style descriptions from the text prompt and extract style information solely from the reference image using a decoupling learning strategy. Additionally, we design a scale-adaptive fusion module to balance the influences of text-based content features and image-based style features, which helps generalization across various text and style combinations. StyleCrafter efficiently generates high-quality stylized videos that align with the content of the texts and resemble the style of the reference images. Experiments demonstrate that our approach is more flexible and efficient than existing competitors.
Automating Continual Learning

General-purpose learning systems should improve themselves in open-ended fashion in ever-changing environments. Conventional learning algorithms for neural networks, however, suffer from catastrophic forgetting (CF) -- previously acquired skills are forgotten when a new task is learned. Instead of hand-crafting new algorithms for avoiding CF, we propose Automated Continual Learning (ACL) to train self-referential neural networks to meta-learn their own in-context continual (meta-)learning algorithms. ACL encodes all desiderata -- good performance on both old and new tasks -- into its meta-learning objectives. Our experiments demonstrate that ACL effectively solves"in-context catastrophic forgetting"; our ACL-learned algorithms outperform hand-crafted ones, e.g., on the Split-MNIST benchmark in the replay-free setting, and enables continual learning of diverse tasks consisting of multiple few-shot and standard image classification datasets.
Presentation Attack Detection using Convolutional Neural Networks and Local Binary Patterns

The use of biometrics to authenticate users and control access to secure areas has become extremely popular in recent years, and biometric access control systems are frequently used by both governments and private corporations. However, these systems may represent risks to security when deployed without considering the possibility of biometric presentation attacks (also known as spoofing). Presentation attacks are a serious threat because they do not require significant time, expense, or skill to carry out while remaining effective against many biometric systems in use today. This research compares three different software-based methods for facial and iris presentation attack detection in images. The first method uses Inception-v3, a pre-trained deep Convolutional Neural Network (CNN) made by Google for the ImageNet challenge, which is retrained for this problem. The second uses a shallow CNN based on a modified Spoofnet architecture, which is trained normally. The third is a texture-based method using Local Binary Patterns (LBP). The datasets used are the ATVS-FIr dataset, which contains real and fake iris images, and the CASIA Face Anti-Spoofing Dataset, which contains real images as well as warped photos, cut photos, and video replay presentation attacks. We also present a third set of results, based on cropped versions of the CASIA images.
The role of interface design on prompt-mediated creativity in Generative AI

Generative AI for the creation of images is becoming a staple in the toolkit of digital artists and visual designers. The interaction with these systems is mediated by \emph{prompting}, a process in which users write a short text to describe the desired image's content and style. The study of prompts offers an unprecedented opportunity to gain insight into the process of human creativity. Yet, our understanding of how people use them remains limited. We analyze more than 145,000 prompts from the logs of two Generative AI platforms (Stable Diffusion and Pick-a-Pic) to shed light on how people \emph{explore} new concepts over time, and how their exploration might be influenced by different design choices in human-computer interfaces to Generative AI. We find that users exhibit a tendency towards exploration of new topics over exploitation of concepts visited previously. However, a comparative analysis of the two platforms, which differ both in scope and functionalities, reveals some stark differences. Features diverting user focus from prompting and providing instead shortcuts for quickly generating image variants are associated with a considerable reduction in both exploration of novel concepts and detail in the submitted prompts. These results carry direct implications for the design of human interfaces to Generative AI and raise new questions regarding how the process of prompting should be aided in ways that best support creativity.