Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the Diets of Breeding Bald Eagles on the Channel Islands: A Multi-Proxy Approach

A 3-decade long restoration program has recently re-established a resident breeding bald eagle population on the Channel Islands off southern California. The population is expanding and now breeds on four of the eight islands in the archipelago. To examine the diversity of prey utilized and the proportion of biomass consumed, we identified the remains of prey collected from bald eagle nests and used stable isotope analysis to quantify the diet composition of breeding bald eagles. Over two consecutive breeding seasons (2010-11), we collected more than 6,500 prey remains from recently active bald eagle nests on Santa Rosa (2 nests), Santa Catalina (9 nests), Santa Cruz (4 nests), and West Anacapa (1 nest) islands. Over 80 percent of these prey remains were identified and contained nearly 600 individual animals from at least 60 family, genera, or species. Based on the minimum number of individuals (MNI), eagles on Santa Catalina Island consume a higher proportion of marine fish (55%) than their counterparts on the Northern Channel Islands (42%). In contrast, eagles on the Northern Channel Islands consume a higher proportion of seabirds (54%) than eagles on Santa Catalina Island (36%). Carbon (δ 13 C) and nitrogen (δ 15 N) stable isotope analysis of bald eagles and putative prey largely agreed with nest prey results. Stable isotope mixing models show that eagles on Santa Catalina Island consumed ~60% marine fish and 2530% seabirds, while eagles on the Northern Channel Islands consumed equal proportions (~40-45%) of these two prey types. As expected, terrestrial resource use was generally low with the exception of the Middle Ranch nest on Santa Catalina Island, where both adults and chicks largely consume terrestrial prey in the form of ground squirrels and freshwater fish sourced from a local reservoir. We suggest that a combination of natural and anthropogenic factors are responsible for the regional differences in bald eagle diet on the Channel Islands. Specifically, bald eagle interactions with an active recreational fishery off of Santa Catalina Island may enable access to marine fish species that are not available to eagles on the Northern Channel Islands. Furthermore, the availability of seabirds is far greater on the Northern Channel Islands in comparison to Santa Catalina Island. In addition to quantifying the diet of recovering bald eagles, our study shows how human activities can influence the diet of a generalist top predator in an ecologically unique system that has been impacted by a combination of human activities over the past century.
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform – Supplementary Material –

The supplementary material contains: (1) Detailed quantitative results for the proposed methods, showing per-class semantic segmentation IOU on the PASCAL VOC 2012 test set. (2) Qualitative edge detection and semantic segmentation results on additional images. 1. Detailed quantitative image segmentation results We provide per-class semantic segmentation IOU on the PASCAL VOC 2012 test set. We compare with the DeepLabLargeFOV and DeepLab-CRF-LargeFOV baselines. In Tab. 1 we show performance of models that have only been pretrained on the Imagenet 2012 image classification task [5], while in Tab. 2 we show performance of models that have also been pretrained on the MS-COCO 2014 semantic segmentation task [3]. Method mean bkg aero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tv DeepLab-LargeFOV [1] 65.1 90.7 74.7 34.0 74.3 57.1 62.0 82.6 75.5 79.1 26.2 65.7 55.8 73.0 68.0 78.6 76.2 50.6 73.9 45.5 66.6 57.1 DeepLab-CRF-LargeFOV [1] 70.3 92.6 83.5 36.6 82.5 62.3 66.5 85.4 78.5 83.7 30.4 72.9 60.4 78.5 75.5 82.1 79.7 58.2 82.0 48.8 73.7 63.3 DT-SE 67.8 91.7 78.8 33.5 78.7 60.6 64.5 84.5 77.4 81.3 29.0 69.1 59.4 76.1 70.8 80.6 77.9 53.4 77.9 46.0 70.1 62.5 DT-EdgeNet 69.0 92.1 79.8 34.8 79.6 61.3 67.0 85.0 78.5 83.2 30.2 70.3 58.9 77.9 72.3 82.3 79.5 55.0 79.8 47.9 70.8 62.5 DT-EdgeNet + DenseCRF 71.2 92.8 83.6 35.8 82.4 63.1 68.9 86.2 79.6 84.7 31.8 74.2 61.1 79.6 76.6 83.2 80.9 58.3 82.6 49.1 74.8 65.1 Table 1. Segmentation IOU on the PASCAL VOC 2012 test set, using the trainval set for training. Model only pretrained on the Imagenet image classification task. Method mean bkg aero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tv DeepLab-COCO-LargeFOV [4] 68.9 92.1 81.6 35.4 81.4 60.1 65.9 84.3 79.3 81.8 28.4 71.2 59.0 75.3 72.6 81.5 80.1 53.5 78.8 50.8 72.7 60.3 DeepLab-CRF-COCO-LargeFOV [4] 72.7 93.4 89.1 38.3 88.1 63.3 69.7 87.1 83.1 85.0 29.3 76.5 56.5 79.8 77.9 85.8 82.4 57.4 84.3 54.9 80.5 64.1 DT-SE 70.7 92.6 83.8 35.0 85.5 61.9 67.6 85.4 80.3 84.4 30.2 73.6 60.4 77.8 74.8 82.3 81.0 54.9 81.2 52.3 75.5 64.1 DT-EdgeNet 71.7 93.0 85.6 36.0 86.4 63.0 69.3 86.0 81.2 85.9 30.7 75.1 60.8 79.3 76.1 83.2 82.0 56.2 82.8 53.3 75.9 64.4 DT-EdgeNet + DenseCRF 73.6 93.5 88.3 37.0 89.8 63.6 70.3 87.3 82.0 87.6 31.1 79.0 61.9 81.6 80.4 84.5 83.3 58.4 86.1 55.9 78.2 65.4 DeepLab-CRF-Attention-DT 76.3 94.3 93.2 41.7 88.0 61.7 74.9 92.9 84.5 90.4 33.0 82.8 63.2 84.5 85.0 87.2 85.7 60.5 87.7 57.8 84.3 68.2 Table 2. Segmentation IOU on the PASCAL VOC 2012 test set, using the trainval set for training. Model pretrained on both the Imagenet image classification task and the MS-COCO semantic segmentation task. 2. Qualitative edge detection and image segmentation results We show additional edge detection and semantic segmentation results on PASCAL VOC 2012 val set in Figs. 1 and 2. We compare results obtained with the proposed domain transform when using our learned EdgeNet edges vs. the SE edges of [2].
Theory and Practice of Logic Programming

Logic programming provides a high-level view of programming, giving implementers a vast latitude into what techniques to explore to achieve the best performance for logic programs. Towards obtaining maximum performance, one of the holy grails of logic programming has been to design computational models that could be executed efficiently and that would allow both for a reduction of the search space and for exploiting all the available parallelism in the application. These goals have motivated the design of the Extended Andorra Model (EAM), a model where goals that do not constrain nondeterministic goals can execute first. In this work, we present and evaluate the Basic design for EAM, a system that builds upon David H. D. Warren’s original EAM with Implicit Control. We provide a complete description and implementation of the Basic design for EAM System as a set of rewrite and control rules. We present the major data structures and execution algorithms that are required for efficient execution, and evaluate system performance. A detailed performance study of our system is included. Our results show that the system achieves acceptable base performance and that a number of applications benefit from the advanced search inherent to the EAM.
Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances

Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vision, machine learning, robotic navigation, embedded systems, automotive electronics and safety critical software. In this paper, we survey the list of vision based advanced driver assistance systems with a consistent terminology and propose a taxonomy. We also propose an abstract model in an attempt to formalize a top-down view of application development to scale towards autonomous driving system.
Optimizing mixed sample analysis as a step to comprehensive desease screening: a pilot study

Background: Lynch Syndrome (LS) is an autosomal dominant hereditary syndrome associated with a diverse range of cancer types. Despite being one of the most prevalent hereditary cancer syndromes, the detection of LS remains challenging due to the absence of well-defined diagnostic criteria which would be able to select all patients who should undergo testing for LS and the limitations of existing screening methods. The implementation of an efficient screening program capable of accurately detecting the majority of LS cases remains a topic of continuous discussion in the scientific literature, with recent studies emphasizing the significance of a universal screening program. Methods: Our study aimed to develop and optimize a cost-effective universal screening method for detecting mutation in the mismatch repair (MMR) genes through mixed sample analysis. We tested five approaches in terms of the use of biological material and the analysis of mixed samples. Results: Each approach successfully detected a specific Lynch-associated pathogenic variant in mixed in the pooled samples with frequency 5.00%, with the lowest allelic fraction recorded at 3.04%. Approach 2, which involved isolating DNA from each patient individually, demonstrated the highest average allelic fraction (7.04%). However, considering financial and time requirements, approach 1, where DNA was isolated only after mixing aliquots of whole blood, proved to be the most favorable. Conclusion: The findings of our study present a promising opportunity to improve LS detection. The identification of LS not only has the potential to prevent cancer-related morbidity and mortality but also facilitates continued progress in understanding the primary prevention of cancer.
Bayesian model averaging using k-best bayesian network structures

Bayesian networks are being widely used in various data mining tasks for probabilistic inference and causual modeling [Pearl (2000), Spirtes et al. (2001)]. Learning the best Bayesian network structure is known to be NP-hard [Chickering (1996)]. Also, learning the single best Bayesian network structure does not always give a good approximation of the actual underlying structure. This is because in many domains, the number of high-scoring models is usually large. In this thesis, we propose that learning the top-k Bayesian network structures and model averaging over these k networks gives a better approximation of the underlying model. The posterior probability of any hypotheses of interest is computed by averaging over the topk Bayesian network models. The proposed techniques are applied to flow cytometric data to make causal inferences in human cellular signaling networks. The causal inferences made about the human T-cell protein signaling model by this method is compared with inferences made by various other learning techniques which were proposed earlier [Sachs et al. (2005), Koch et al. (2009)]. We also study and compare the classification accuracy of the top-k networks to that of the single MAP network. In summary, this thesis describes: 1. Algorithm for learning the top-k Bayesian network structures. 2. Model averaging based on the top-k networks. 3. Experimental results on the posterior probabilities of the top-k networks. 4. How the top-k Bayesian networks can be applied to learn protein signaling networks with Results of top-k model averaging on the CYTO data. 5. Results of Classification Accuracy of the top-k networks.
Liste commentée des poissons de l'Amazonie bolivienne

ANNOTATED CHECKLIST OF THE FISHES OF THE BOLIVIAN AMAZONIAN BASIN After a brief review of the bolivian amazonian basin, of the prospected biotopes, the ftshing effort and of the old and new works, the authors present an annotated checklist of the collected ftshes. A total of 389 species are listed. Differences between the four main tributaries of the Upper Madeira river (Beni, Madre de Dios, Mamore and Guapore rivers) are noted. In the Mamore basin which has been the most intensively sampled, 327 species are listed, where Siluriforms (45 %) and Characoidei (39 %) are predominant. In this basin the authors identified a lowland area and an andean area above 200 m, the latter divided into three sub-areas according to the ftsh communities. KEYSWORDS :Fishes Freshwaters Inventory Biogeography Amazonia Bolivia South America. (1) Hydrobiologisie ORSTOM, Laboratoire d’lchfyologie, MNHN, 43, rue Cuvier, 75231 Paris cedexFrance. (2) Hydrobiologiste ORSTOM, 12, rue de L’Esperance, 33480 Talence France. (3) Hydrobiologisfe, 24, rue Kleber, 24000 Perigueux-France. Rev. Hydrobiol. trop. 24 (1) : 61-76 (1991~. 62 L. LAUZANNE, G. LOUBENS, B. LE GUENNEC
HARQ and AMC: Friends or Foes?

To ensure reliable communications in randomly varying and error-prone channels, wireless systems use adaptive modulation and coding (AMC) as well as hybrid ARQ (HARQ). In order to elucidate their compatibility and interaction, we compare the throughput provided by AMC, HARQ, and their combination (AMC-HARQ) under two operational conditions: in slow- and fast block-fading channels. Considering both incremental redundancy HARQ and repetition redundancy HARQ, we optimize the rate-decision regions for AMC/HARQ and compare them in terms of attainable throughput. Under a fairly general model of the channel variation and the decoding functions, we conclude that: 1) adding HARQ on top of AMC may be counterproductive in the high average signal-to-noise ratio regime for fast fading channels and 2) HARQ is useful for slow fading channels, but it provides moderate throughput gains. We provide explanations for these results which allow us to propose paths to improve AMC-HARQ systems.
The Effect of a Wordless, Animated, Social Media Video Intervention on COVID-19 Prevention: Online Randomized Controlled Trial

Background Innovative approaches to the dissemination of evidence-based COVID-19 health messages are urgently needed to counter social media misinformation about the pandemic. To this end, we designed a short, wordless, animated global health communication video (the CoVideo), which was rapidly distributed through social media channels to an international audience. Objective The objectives of this study were to (1) establish the CoVideo’s effectiveness in improving COVID-19 prevention knowledge, and (2) establish the CoVideo’s effectiveness in increasing behavioral intent toward COVID-19 prevention. Methods In May and June 2020, we enrolled 15,163 online participants from the United States, Mexico, the United Kingdom, Germany, and Spain. We randomized participants to (1) the CoVideo arm, (2) an attention placebo control (APC) arm, and (3) a do-nothing arm, and presented 18 knowledge questions about preventive COVID-19 behaviors, which was our first primary endpoint. To measure behavioral intent, our second primary endpoint, we randomized participants in each arm to five list experiments. Results Globally, the video intervention was viewed 1.2 million times within the first 10 days of its release and more than 15 million times within the first 4 months. Knowledge in the CoVideo arm was significantly higher (mean 16.95, 95% CI 16.91-16.99) than in the do-nothing (mean 16.86, 95% CI 16.83-16.90; P<.001) arm. We observed high baseline levels of behavioral intent to perform many of the preventive behaviors featured in the video intervention. We were only able to detect a statistically significant impact of the CoVideo on one of the five preventive behaviors. Conclusions Despite high baseline levels, the intervention was effective at boosting knowledge of COVID-19 prevention. We were only able to capture a measurable change in behavioral intent toward one of the five COVID-19 preventive behaviors examined in this study. The global reach of this health communication intervention and the high voluntary engagement of trial participants highlight several innovative features that could inform the design and dissemination of public health messages. Short, wordless, animated videos, distributed by health authorities via social media, may be an effective pathway for rapid global health communication during health crises. Trial Registration German Clinical Trials Register DRKS00021582; https://tinyurl.com/6r4zkbbn International Registered Report Identifier (IRRID) RR2-10.1186/s13063-020-04942-7
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion — Supplemental Material

Figure B. The detailed architecture of the searched MS cell for multi-scale fusion. We also notice some interesting observations as follows: 1) From Fig.A, we observe that the numbers of operations connected to RGB features (12 operations) are more than those connected to depth features (7 operations), which indicates that the connections to RGB and depth features are asymmetric. 2) The MM cell tends to select many attention operations
USENIX Association Proceedings of the General Track : 2004 USENIX Annual Technical Conference

In this paper, we report on experience in building and deploying an operational Internet broadcast system based on Overlay Multicast. In over a year, the system has been providing a cost-effective alternative for Internet broadcast, used by over 4000 users spread across multiple continents in home, academic and commercial environments. Technical conferences and special interest groups are the early adopters. Our experience confirms that Overlay Multicast can be easily deployed and can provide reasonably good application performance. The experience has led us to identify first-order issues that are guiding our future efforts and are of importance to any Overlay Multicast protocol or system. Our key contributions are (i) enabling a real Overlay Multicast application and strengthening the case for overlays as a viable architecture for enabling group communication applications on the Internet, (ii) the details in engineering and operating a fully functional streaming system, addressing a wide range of real-world issues that are not typically considered in protocol design studies, and (iii) the data, analysis methodology, and experience that we are able to report given our unique standpoint.
Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation

—Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the “sim-to-real gap”. We present a method for continuous improvement of modeling and control after deploying the robot to a dynamically-changing target environment. We develop a differentiable physics simulation framework that performs on-line system identiﬁcation and optimal control simultaneously, using the incoming observations from the target environment in real time. To ensure robust system identiﬁcation against noisy observations, we devise an algorithm to assess the conﬁdence of our estimated parameters, using numerical analysis of the dynamic equations. To ensure real-time optimal control, we adaptively schedule the optimization window in the future so that the optimized actions can be replenished faster than they are consumed, while staying as up-to-date with new sensor information as possible. The constant re-planning based on a constantly improved model allows the robot to swiftly adapt to the changing environment and utilize real-world data in the most sample-efﬁcient way. Thanks to a fast differentiable physics simulator, the optimization for both system identiﬁcation and control can be solved efﬁciently for robots operating in real time. We demonstrate our method on a set of examples in simulation and show that our results are favorable compared to baseline methods.
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics

dle part), given a room suite that contains a double sofa and a stool, the goal is to add a single sofa to current suite. We first utilize the trained GAE to perform primary-ranking, then employs the trained GBDT-LR model to re-rank the selected candidates. GBDT-LR is a module of FSC. Specifically, if we have n single sofas in the furniture pool, we can obtain n room suite candidates. For each candidate, we adopt GAE to compute its compatibility score. Then we recompute the compatibility scores of the Top50 candidates via GBDT-LR and re-rank them. As stated in the main paper, GAE takes visual feature vectors (extracted from VEN of FSC) of objects, GBDT-LR additionally considers objects’ attributes.
Sri Lankan seaweeds: Methodologies and field guide to the dominant species

Seaweeds are macroalgae that are important primary producers in coastal zones. The many species belong to different and unrelated groups of organisms, classified as the red, green and brown algae. These colours are of relevance, because they reflect their different photosynthetic pigments. Nice colour photographs of seaweeds in these different groups are included in the present work. Professor Coppejans and his team, however, have, amongst others, also elaborated on survey methodologies, seaweed communities, seasonality and zonation. These important chapters are richly illustrated and as such provide an apt entry point to marine phycological research. These chapters are of great importance in capacity building in research on seaweeds. For more experienced phycologists the descriptions and photographs of the different species form certainly the backbone of the book. Next to generously illustrated taxonomic information, this work also provides additional insight on ecology and distribution of the treated taxa.
Seiches in a Narrow Lake, Uniformly Stratified in Three Layers

summary This paper gives a theoretical analysis of the longitudinal seiches (or standing waves) in a narrow thermally stratified lake. A mathematical model of the lake is postulated consisting of three horizontal layers of water, density varying uniformly with depth in each. The model gives a good representation of the stratification encountered in nature, and leads to a relatively simple analytical solution of the hydrodynamical equations. The theory is applied to find the periods and modes of ordinary and internal seiches in the northern basin of Lake Windermere. The results show that in addition to seiche oscillations centred at thermo-cline level (predicted by a model of the lake consisting of two horizontal homogeneous layers of water) there are modes of oscillation in which the vertical displacement of water is greatest in the lower depths of the lake below the thermo-cline. It seems likely that the presence of such modes could explain the vertical oscillations of temperature, of large amplitude and complex wave form, observed in those depths. Further investigations are required to test this hypothesis.
J un 2 01 5 A geometric alternative to Nesterov ’ s accelerated gradient descent

We propose a new method for unconstrained optimization of a s mooth and strongly convex function, which attains the optimal rate of convergence of N esterov’s accelerated gradient descent. The new algorithm has a simple geometric interpret ation, loosely inspired by the ellipsoid method. We provide some numerical evidence that t he new method can be superior to Nesterov’s accelerated gradient descent.
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Supplementary Materials

View selection For most few-view Realistic Synthetic experiments, we randomly subsample 8 of the available 100 training renders. Views are not manually selected. However, to compare the ability of NeRF and DietNeRF to extrapolate to unseen regions, we manually selected 14 of the 100 views mostly showing the right side of the Lego scene. For DTU experiments where we fine-tune pixelNeRF [12], we use the same source view as [12]. This viewpoint was manually selected and is shared across all 15 scenes.