Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE (Digital Academic Repository) Engaging with TV events on Twitter The interrelations between TV consumption, engagement actors, and engagement content

Purpose – The purpose of this paper is to explore the interrelationship between television (TV) consumption (viewing ratings), engagement behaviors of different actors on Twitter (TV programs, media, celebrities and viewers) and the content of engagement behaviors (affective, program-related and social content). Design/methodology/approach – TV ratings and Twitter data were obtained. The content of tweets was analyzed by means of a sentiment analysis. A vector auto regression model was used to understand the interrelationship between tweets of different actors and TV consumption. Findings – First, the results showed a negative interrelationship between TV viewing and viewers ’ tweeting behavior. Second, tweets by celebrities and media exhibited similar patterns and were both affected mostly by the number of tweets by viewers. Finally, the content of tweets matters. Affective tweets positively relate to TV viewing, and program-related and social content positively relates to the number of tweets by viewers. Research limitations/implications – The findings help us understand the online engagement ecosystem and provide insights into drivers of TV consumption and online engagement of different actors. Practical implications – The results indicate that content producers may want to focus on stimulating affective conversations on Twitter to trigger more online and offline engagement. The results also call for rethinking the meaning of TV metrics. Originality/value – While some studies have explored viewer interactions on Twitter, only a few studies have looked at the effects of such interactions on variables outside of social media, such as TV consumption. Moreover, the authors study the interrelations between Twitter interactions with TV consumption, which allows us to examine the effect of online engagement on offline behaviors and vice versa. Finally, the authors take different actors into account when studying real-life online engagement.
Developing Countries And The Multilateral Trading System: From The Gatt To The Uruguay Round And The Future

Introduction The GATT, the ITO, and the WTO: Geneva, 1947 to Marrakesh, 1994 through Havana, 19471948 Developing Countries and the GATT: Havana, 19471948 to Tokyo, 1979 The GATT: Between Tokyo, 1979 and Marrakesh, 1994: A Negotiating History Achievements and Failures of the Uruguay Round: Market Access for Goods Issues Other than Market Access for Goods: Services, TRIMS and TRIPS, Safeguards, Dispute Settlement, and Non-Tariff Measures Subregionalism, Regionalism, and Multilateralism Trade, the Environment, and Labor Standards Making the Best Out of the PostUruguay Round World: Institutional Innovations and Reforms in Developing Countries Cooperation Among the Multilateral Economic Institutions Summary and Conclusions Postscript
Data Augmentation in Machine Learning for Cheating Detection in Large-Scale Assessment: An Illustration with the Blending Ensemble Learning Algorithm

Machine learning methods have been explored for cheating detection in large-scale assessment in recent years. Most of these studies analyzed item responses and response time data. Though a few studies investigated data augmentation in the feature space, data augmentation in machine learning for cheating detection is far beyond thorough investigation. This study explored data augmentation of the feature space for the blending ensemble learning at the meta-model level for cheating detection. Four anomaly detection techniques assigned outlier scores to augment the meta-model’s input data in addition to the most informative features from the original dataset identified by four feature selection methods. The performance of the meta-model with data augmentation was compared with that of each base model and the meta-model without data augmentation. Based on the evaluation criteria, the best-performing meta-model with data augmentation was identified. In general, data augmentation in the blending ensemble learning for cheating detection greatly improved the accuracy of cheating detection compared with other alternative approaches.
Moss crusts mitigate the negative impacts of shrub mortality on nutrient multifunctionality of desert soils

The distribution of a mosaic of biological soil crusts (BSCs) and shrubs is a common landscape surface feature in temperate deserts. With the continued climatic change, the desert shrub experiences varying rates of mortality which has serious negative impacts on soil structure and functions. However, it is not clear whether BSCs, which develop extensively in areas under shrub canopies, can mitigate the eﬀects of shrub mortality on soil nutrient multifunctionality. Therefore, in this study, the Gurbantungut Desert, a typical temperate desert in northern China, was selected as the study area, and the dominant shrubs, Ephedra przewalskii shrub, and the moss crust were used as the study objects. Soil samples were collected from the bare sand and moss crusts under the living shrub and the dead shrub and analyzed to determine their carbon, nitrogen, phosphorus, and potassium contents. The results showed that the shrub mortality reduced the soil moisture content, pH, electric conductivity, and carbon, nitrogen, phosphorus, and potassium contents in the bare sand compared with the bare sand under the living shrub. The presence of the moss crust greatly mitigated the negative impacts of shrub mortality on soil carbon, nitrogen, phosphorus, and potassium contents
Genetic stock composition analysis of the Chinook salmon bycatch samples from the 2016 Gulf of Alaska trawl fisheries

A genetic analysis of samples from the Chinook salmon (Oncorhynchus tshawytscha) Prohibited Species Catch (bycatch) of the 2016 Gulf of Alaska (GOA) trawl fisheries for walleye pollock (Gadus chalcogrammus) and rockfish (Sebastes spp.), was undertaken to determine the stock composition of the sample sets. Samples were genotyped for 43 single nucleotide polymorphism (SNP) DNA markers and results were estimated using the Alaska Department of Fish and Game SNP baseline. In 2016, genetic samples were collected from Chinook salmon taken in the bycatch of the GOA pollock trawl fisheries using a simple random sample protocol with trip being the primary unit. This was the third year for this sampling protocol and which resulted in the largest available genetic sample set to date with 24% of the estimated salmon bycatch from the pollock fishery successfully genotyped. Based on analysis of 4,962 Chinook salmon bycatch samples, British Columbia (42%), West Coast US (40%), and Coastal Southeast Alaska (15%) comprised the largest stock groups, at an estimated 8,602, 8,301, and 3,080 Chinook salmon, respectively, out of 20,589 fish total. In 2016, genetic samples from the bycatch of the GOA rockfish CV fishery were collected by the fishing industry using a census sampling protocol. Based on the genotyping of 493 Chinook salmon bycatch samples collected from this fishery in NMFS Statistical Areas 620 and 630, West Coast US stocks represented the largest stock group (62%) with smaller contributions from British Columbia (27%), Coastal Southeast Alaska (7%), and Northwest GOA (4%) stock groups.
SpeedDETR: Speed-aware Transformers for End-to-end Object Detection

Vision Transformers (ViTs) have continuously achieved new milestones in object detection. However, the considerable computation and memory burden compromise their efficiency and generalization of deployment on resource-constraint devices. Besides, efficient transformer-based detectors designed by existing works can hardly achieve a realistic speedup, especially on multi-core processors (e.g., GPUs). The main issue is that the current literature solely concentrates on building algorithms with minimal computation, oblivious that the practical latency can also be affected by the memory access cost and the degree of parallelism . Therefore, we propose SpeedDETR, a novel speed-aware transformer for end-to-end object detectors, achieving high-speed inference on multiple devices. Specifically, we de-sign a latency prediction model which can directly and accurately estimate the network latency by analyzing network properties , hardware memory access pattern , and degree of parallelism . Following the effective local-to-global visual modeling process and the guidance of the latency prediction model, we build our hardware-oriented architecture design and develop a new family of SpeedDETR. Experiments on the MS COCO dataset show SpeedDETR outperforms current DETR-based methods by 1.5% ∼ 9.2% AP with 1.09 ×∼ 3.6 × speedup on Tesla V100. Even acceptable speed inference can be achieved on edge GPUs, i.e., 4 FPS for NVIDIA JETSON TX2 (1.4 ×∼ 4 × faster than other counterparts), 1 FPS for NVIDIA NANO (1.5 ×∼ 6.7 × faster). Codes release SpeedDETR.
Selective Search for Object Recognition

This paper evaluates the selective search algorithm implemented by J.R. R. Uijlings et al. The selective search algorithm addresses the problem of object recognition. In particular the selective search has emphasis on the inherit hierarchical structure of images. This is done by combining segmentation for object recognition with exhaustive search. The advantage of exhaustive search is that is aims to capture all object locations, and the advantage of segmentation is that it uses image structure to guide the search for object locations. The selective search results in a small set of data-driven, class-independent, high quality locations. The results of selective search have been outstanding with exceptional scores across the Pascal Image challenges. This paper evaluates external potential challenges where the algorithm may fail to recognize an object. These instances may include camouflaged object, which may be obvious to a human but not so much to the selective search algorithm. Keywords—Object recognition, selective search, segmentation, exhaustive search, hierarchical image structure.
Nutritional management in an elderly man with esophageal and gastric necrosis after caustic soda ingestion: a case report

: The ingestion of corrosive industrial chemical agents, such as caustic soda, that are mostly used for household cleaning, usually occurs accidentally or for suicidal purposes. Multiple protocols are based on documented success in preventing impending complications. In this study, we present a case of a 70-year-old man who swallowed caustic soda in a suicide attempt, causing a development of strong esophageal and gastric necrosis with subsequent gastrectomy and digiunostomy. Initially, the recommended nutritional approach was via percutaneous endoscopic jejunostomy by a polymer and high-caloric formula, with an elevated content of soluble fiber. After 5 months, the medical team removed the percutaneous endoscopic jejunostomy and the patient switched from enteral to oral nutrition. In this step, it was decided to introduce two oral, high-caloric supplements: an energy supplement in powder, based on maltodextrin, immediately soluble in foods or in hot/cold drinks and a high-energy and protein drink, enriched with arginine, vitamin C, zinc, and antioxidants. Oral administration (per os) was well tolerated by consuming homogenized food mixed in water. After 1 month, the patient was discharged from the hospital and was able to eat a regular meal.
Sequence Stratigraphy of the Late Ordovician (Katian), Maysvillian Stage of the Cincinnati Arch, Indiana, Kentucky, and Ohio, U.S.A

61 Specific Aims 62 Background: MS, Milankovich Cycles, Cincinnatian 62 Cincinnatian Series 64 Methods 67 Outcrop Descriptions 68 Correlation and Interpretation using MS 72 Conclusions 81 References 83 Chapter 4: Third Order Sequence Stratigraphy of the Cincinnatian, Maysvillian Stage: Not all Unconformities are Sequence Boundaries 89 Introduction 89 The Current Sequence Stratigraphic Model 90 Inconsistancies in the Current Model 91 A Reinterpretation of Cincinnatian Sequences 92 Discussion 106 Conclusions 110 References 112
Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization gap on unseen testing adversaries, deemed as the adversarially robust generalization problem . Despite the preliminary understandings devoted on adversarially robust generalization, little is known from the architectural perspective. Thus, this paper tries to bridge the gap by systematically examining the most representative architectures ( e.g. , Vision Transformers and CNNs). In particular, we ﬁrst comprehensively evaluated 20 adversarially trained architectures on ImageNette and CIFAR-10 datasets towards several adversaries (multiple (cid:96) p -norm adversarial attacks), and found that Vision Transformers ( e.g. , PVT, CoAt-Net) often yield better adversarially robust generalization. To further understand what architectural ingredients favor adversarially robust generalization, we delve into several key building blocks and revealed the fact via the lens of Rademacher complexity that the higher weight sparsity contributes signiﬁcantly towards the better adversarially robust generalization of Vision Transformers, which can be often achieved by attention layers. Our extensive studies discovered the close relationship between architectural design and adversarially robust generalization, and instantiated several important insights. We hope our ﬁndings could help to better understand the mechanism towards designing robust deep learning architectures.
Explorations in Economic History

Persecution, pogroms, and genocide have plagued humanity for centuries, costing millions of lives and haunting survivors. Economists and economic historians have recently made new contributions to the understanding of these phenomena. We provide a novel conceptual framework which highlights the inter-relationship between the intensity of persecution and migration patterns across dozens of historical episodes. Using this framework as a lens, we survey the growing literature on the causes and consequences of persecution, pogroms, and genocide. Finally, we discuss gaps in the literature and take several tentative steps towards explaining the diﬀerences in survival rates of European Jews in the 20 th century.
Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification

Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re- IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM11Codes will publicly available at https://github.com/BoomShakaY/AIM-CCReID. , is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods.
Glaciochemical Investigations as a Tool for Determining the Spatial and Seasonal Variation of Snow Accumulation in the Central Karakoram, Northern Pakistan

Between 70 and 80% of the total annual run-off from the Upper Indus Basin originates from heavy snowfall and glacierized basins at elevations greater than 3500 m a.s. l. However, very little is known concerning the mountainous headwaters of the Indus. This is especially true with respect to the amount of snowfall in the major source area, the high Karakoram. Recent studies of high-altitude alpine glaciers indicate that geochemical dating techniques can accurately and confidently identify seasonal and annual stratigraphy within snow pits and ice cores, and thus can be used to determine the seasonal and annual rate of snow accumulation. In addition, chemical records can usually be employed to determine sources of precipitation. Six snow pits, each 5–10 m deep, were investigated in the accumulation zones of the Biafo and Khurdopin Glacier basins. Both accumulation zones are characterized by broad, open basins separated by steep, narrow ridges in which direct precipitation is the dominant form of nourishment. Seasonal stratigraphy is delineated through an analysis of the seasonal variation in the chemical and physical characteristics of the snow-pack. Annual snow accumulation in the Biafo Glacier basin ranges from 0.9 to 1.9 m water equivalent; maximum accumulation occurs in the elevation band 4900–5400 m a.s. l. Roughly one-third of this snow accumulation occurs during the summer.
Deep Dive into Gradients: Better Optimization for 3D Object Detection with Gradient-Corrected IoU Supervision

Intersection-over-Union (IoU) is the most popular metric to evaluate regression performance in 3D object detection. Recently, there are also some methods applying IoU to the optimization of 3D bounding box regression. However, we demonstrate through experiments and mathematical proof that the 3D IoU loss suffers from abnormal gradient w.r. t. angular error and object scale, which further leads to slow convergence and suboptimal regression process, respectively. In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression. Specifically, a gradient correction strategy is designed to endow 3D IoU loss with a reasonable gradient. It ensures that the model converges quickly in the early stage of training, and helps to achieve fine-grained refinement of bounding boxes in the later stage. To solve suboptimal regression of 3D IoU loss for objects at different scales, we introduce a gradient rescaling strategy to adaptively optimize the step size. Finally, we integrate GCIoU Loss into multiple models to achieve stable performance gains and faster model convergence. Experiments on KITTI dataset demonstrate superiority of the proposed method. The code is available at https://github.com/ming71/GCIoU-loss.
Southern Region Small Fruit Consortium

Blackberries have become an increasingly important specialty crop in recent years. They provide consumers with a healthy fruit containing many antioxidants. In the US, blackberry production costs are rising due to the labor-intensive nature of managing and harvesting fresh market fruit. New growing systems are of interest to try to mitigate production costs. This research examined the impact of two plant growth regulators (PGRs), prohexadione calcium (P-Ca) and gibberellic acid (GA 3 ), on vegetative growth of three blackberry cultivars with standard height, semi-dwarf, and a dwarf plant architecture (‘Natchez’, ‘Sweet-Ark™ Ponca’
Strategic Classification with Unknown User Manipulations

In many human-centric applications for Machine Learning instances will adapt to a classifier after its deployment. The field of strategic classification deals with this issue by aiming for a classifier that balances the trade-off between correctness and robustness to manipulation. This task is made harder if the underlying manipulation structure (i.e. the set of manipulations available at every instance) is unknown to the learner. We propose a novel batch-learning setting in which we use unlabeled data from previous rounds to estimate the manipulation structure. We show that in this batch-learning setting it is possible to learn a close to optimal classifier in terms of the strategic loss even without knowing the feasible manipulations beforehand. In line with recent advances in the strategic classification literature, we do not assume a best-response from agents but only require that observed manipulations are feasible.
Effects of Soil Amendments and Other Practices on the Success of the Virginia Department of Transportation's Non-Tidal Wetland Mitigation Efforts

Construction of created wetlands to mitigate for highway impacts requires more than $100,000 per ha of impacts. A detailed study of soil, hydrology, and vegetation at 10 recently constructed non-tidal mitigation sites indicates excessive soil compaction and a lack of organic matter continue to limit mitigation success. Detailed hydrologic studies at two mitigation sites (Charles City and Sands Bottom) point out significant differences in their hydrologic regime vs. adjacent natural wetlands related to soil reconstruction procedures. Results from two compost amendment experiments at Charles City indicate that approximately 100 Mg/ha of organic amendment is optimal for reconstructing hydric soil conditions when natural organic enriched soil materials cannot be returned. Overall mitigation success would improve from (1) utilization of appropriate organic amendments, (2) tillage/ripping protocols at all sites to meet target density specifications, and (3) reconstruction of a soil-geologic profile that is similar in texture and permeability to natural wetland soils. These reconstruction guidelines will help ensure that the Virginia Department of Transportation complies with existing mitigation regulations in the most cost-effective manner.
ODS: Test-Time Adaptation in the Presence of Open-World Data Shift

Test-time adaptation (TTA) adapts a source model to the distribution shift in testing data without using any source data. There have been plenty of algorithms concentrated on covariate shift in the last decade, i.e., D t ( X ) , the distribution of the test data is different from the source data. Nonetheless, in real application scenarios, it is necessary to consider the inﬂuence of label distribution shift, i.e., both D t ( X ) and D t ( Y ) are shifted, which has not been sufﬁciently explored yet. To remedy this, we study a new problem setup, namely, TTA with Open-world Data Shift (AODS). The goal of AODS is simultaneously adapting a model to covariate and label distribution shifts in the test phase. In this paper, we ﬁrst analyze the relationship between classi-ﬁcation error and distribution shifts. Motivated by this, we hence propose a new framework, namely O DS , which decouples the mixed distribution shift and then addresses covariate and label distribution shifts accordingly. We conduct experiments on multiple benchmarks with different types of shifts, and the results demonstrate the superior performance of our method against the state of the arts. Moreover, O DS is suitable for many TTA algorithms.
Revisiting the Measurement of Plant Available Water in Soilless Substrates

Significance to the Industry: In the nursery industry, irrigation management is the key cultural practice that determines the extent of nutrient runoff. We are testing soil moisture capacitance sensors (Ech20 series; Decagon Devices, Pullman, WA) to monitor and control irrigation scheduling in soilless substrates. By comparing these results of this research with past published studies on water availability in soilless substrates, we intend to ensure that we can precisely integrate these and other environmental sensors into wireless networks for realtime management. Eventually, these sensor networks may revolutionize our approach to water and nutrient management, making more efficient use of resources by reducing water consumption and nutrient runoff. Nature of Work: Previous studies on water desorption curves [2, 3] showed definitive differences between soil and soilless substrates in the availability of water for roots. Soilless substrates, which in most cases have larger particle sizes, tend to release more water at much lower pressures than in most soils. Plant Available Water (PAW) is defined as the amount of water accessible to the plant, and this is affected by the physical properties of the substrate. Some researchers propose that soil moisture availability varies from 0 to about 1.5 MPa (wilting point). However, in soilless substrates, plant wilt begins at substrate