Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Science as a Route to AI for Middle- and High-School Students

The Bootstrap Project's Data Science curriculum has trained about 100 teachers who are using it around the country. It is specifically designed to aid adoption at a wide range of institutions. It emphasizes valuable curricular goals by drawing on both the education literature and on prior experience with other computing outreach projects. It embraces "three P's" of data-oriented thinking: the promise, pitfalls, and perils. This paper briefly describes the curriculum's design, content, and outcomes, and explains its value on the road to AI curricula.
Retinoic acid acts as a morphogen in freshwater sponges

Summary The effect of retinoic acid [RA] on the morphogenesis of freshwater sponges (Spongilla lacustris, Ephydatia fluviatilis, Ephydatia muelleri) was studied. At a concentration of 2 μM, RA had no overt effect on pinacocytes and choanocytes but had already altered mesohyl cell movement and adhesion after 1 h treatment. It interfered with morphogenesis of sponges developing both from larvae and gemmules, inhibiting the formation of the aquiferous system, and promoting formation of reduction bodies. The same was observed in treatment of functional sponges with RA. We conclude that RA induces modifications in mesohyl cell adhesion and interaction, analogous to that observed in induction of reduction bodies formation or gemmulogenesis.
Creating a Multimodal Dataset of Images and Text to Study Abusive Language

In order to study online hate speech, the availability of datasets containing the linguistic phenomena of interest are of crucial importance. However, when it comes to specific target groups, for example teenagers, collecting such data may be problematic due to issues with consent and privacy restrictions. Furthermore, while text-only datasets of this kind have been widely used, limitations set by image-based social media platforms like Instagram make it difficult for researchers to experiment with multimodal hate speech data. We therefore developed CREENDER, an annotation tool that has been used in school classes to create a multimodal dataset of images and abusive comments, which we make freely available under Apache 2.0 license. The corpus, with Italian comments, has been analysed from different perspectives, to investigate whether the subject of the images plays a role in triggering a comment. We find that users judge the same images in different ways, although the presence of a person in the picture increases the probability to get an offensive comment.
Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning*

Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55 ± 1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use.
Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change

The choice of hyper-parameters affects the performance of neural models. While much previous research (Sutskever et al., 2013; Duchi et al., 2011; Kingma and Ba, 2015) focuses on accelerating convergence and reducing the effects of the learning rate, comparatively few papers concentrate on the effect of batch size. In this paper, we analyze how increasing batch size affects gradient direction, and propose to evaluate the stability of gradients with their angle change. Based on our observations, the angle change of gradient direction first tends to stabilize (i.e. gradually decrease) while accumulating mini-batches, and then starts to fluctuate. We propose to automatically and dynamically determine batch sizes by accumulating gradients of mini-batches and performing an optimization step at just the time when the direction of gradients starts to fluctuate. To improve the efficiency of our approach for large models, we propose a sampling approach to select gradients of parameters sensitive to the batch size. Our approach dynamically determines proper and efficient batch sizes during training. In our experiments on the WMT 14 English to German and English to French tasks, our approach improves the Transformer with a fixed 25k batch size by +0.73 and +0.82 BLEU respectively.
Stratifying herbivore fisheries by habitat to avoid ecosystem overfishing of coral reefs

The problem of ecosystem overfishing has mostly focused on the function of forage fish as prey for apex predators. Here, I consider another ecosystem function, herbivory, that affects habitat quality. Parrotfish are an important fishery in many parts of the Caribbean and the dominant herbivorous fish on its coral reefs. Herbivory helps to control macroalgae which compete with coral and can impede reef resilience if allowed to bloom. Thus, long-term maintenance of reef habitat quality, which underpins fisheries, requires sufficient parrotfish stock. Ecosystem models predict that reductions in parrotfish grazing could have deleterious impacts on reef habitat yet the determination of ecologically sustainable levels of parrotfish harvest remains elusive. An initial solution to this dilemma is proposed for areas where an outright ban on herbivore exploitation is considered infeasible. Fisheries management has tended to consider coral reefs as a single habitat such that regulations apply evenly throughout exploitable areas. But reef habitats are not equally susceptible to ecosystem overfishing and some do not appear to have a strong requirement for parrotfish grazing. One habitat, Orbicella reef, has a high dependence on herbivory, whereas the state of another dominant habitat - gorgonian plain - appears to be driven by environmental factors (e.g. wave exposure). Ecosystem-based fisheries management could be improved by restricting parrotfish harvest on Orbicella reefs yet allowing exploitation on gorgonian plain. Management could then focus on achieving a sustainable yield on gorgonian plains without the added complexity of estimating catch levels that avoid ecosystem overfishing.
From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks

Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. In this work, we propose a deep learning technique for 3D object reconstruction from a single image. Contrary to recent works that either use 3D supervision or multi-view supervision, we use only single view images with no pose information during training as well. This makes our approach more practical requiring only an image collection of an object category and the corresponding silhouettes. We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision. A key novelty of the proposed technique is to impose 3D geometric reasoning into predicted 3D point clouds by rotating them with randomly sampled poses and then enforcing cycle consistency on both 3D reconstructions and poses. In addition, using single-view supervision allows us to do test-time optimization on a given test image. Experiments on the synthetic ShapeNet and real-world Pix3D datasets demonstrate that our approach, despite using less supervision, can achieve competitive performance compared to pose-supervised and multi-view supervised approaches.
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9× less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss.
Facial Action Unit Detection on ICU Data for Pain Assessment

Current day pain assessment methods rely on patient self-report or by an observer like the Intensive Care Unit (ICU) nurses. Patient self-report is subjective to the individual and suffers due to poor recall. Pain assessment by manual observation is limited by the number of administrations per day and staff workload. Previous studies showed the feasibility of automatic pain assessment by detecting Facial Action Units (AUs). Pain is observed to be associated with certain facial action units (AUs). This method of pain assessment can overcome the pitfalls of present-day pain assessment techniques. All the previous studies are limited to controlled environment data. In this study, we evaluated the performance of OpenFace an open-source facial behavior analysis tool and AU R-CNN on the real-world ICU data. Presence of assisted breathing devices, variable lighting of ICUs, patient orientation with respect to camera significantly affected the performance of the models, although these showed the state-of-the-art results in facial behavior analysis tasks. In this study, we show the need for automated pain assessment system which is trained on real-world ICU data for clinically acceptable pain assessment system.
Generalized Planning With Deep Reinforcement Learning

A hallmark of intelligence is the ability to deduce general principles from examples, which are correct beyond the range of those observed. Generalized Planning deals with finding such principles for a class of planning problems, so that principles discovered using small instances of a domain can be used to solve much larger instances of the same domain. In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on.
Fluctuations of extreme eigenvalues of sparse Erd\H{o}s-R\'enyi graphs

We consider a class of sparse random matrices which includes the adjacency matrix of the ErdősRényi graph G(N, p). We show that if N 6 Np 6 N1/3−ε then all nontrivial eigenvalues away from 0 have asymptotically Gaussian fluctuations. These fluctuations are governed by a single random variable, which has the interpretation of the total degree of the graph. This extends the result [19] on the fluctuations of the extreme eigenvalues from Np > N2/9+ε down to the optimal scale Np > N. The main technical achievement of our proof is a rigidity bound of accuracy N−1/2−ε(Np)−1/2 for the extreme eigenvalues, which avoids the (Np)−1-expansions from [9, 19, 24]. Our result is the last missing piece, added to [8, 12, 19, 24], of a complete description of the eigenvalue fluctuations of sparse random matrices for Np > N.
Breaking RSA Security With A Low Noise D-Wave 2000Q Quantum Annealer: Computational Times, Limitations And Prospects

The RSA cryptosystem could be easily broken with large scale general purpose quantum computers running Shor's factorization algorithm. Being such devices still in their infancy, a quantum annealing approach to integer factorization has recently gained attention. In this work, we analyzed the most promising strategies for RSA hacking via quantum annealing with an extensive study of the low noise D-Wave 2000Q computational times, current hardware limitations and challenges for future developments.
A Probabilistic Analysis of Regional Mercury Impacts on Wildlife

We investigate the uncertainties associated with modeling the potential health effects on piscivorous animals of mercury released to the atmosphere. The multimedia modeling system combines an atmospheric fate and transport model, an aquatic cycling model, and a terrestrial food web model. First, the modeling system is used to calculate point values of the animals' hazard quotients (i.e., measures of toxic dose). Next, we use a simplified version of the modeling system to conduct a probabilistic analysis for the Great Lakes region that takes into account input uncertainty, variability, and uncertainty and variability combined. The use of two different software packages for the combined uncertainty/variability analysis led to similar results except for high values (>90th percentile) where some differences were evident. A sensitivity study was performed on the combined uncertainty and variability analysis. Regional variability caused more than 70% of the variance in the results, with the fish bioaccumulation factor accounting for the majority of the variability. The major sources of uncertainty were the speciation of the mercury emissions, the lake pH, and the sediment burial rate.
LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery

Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable the precise assessment and can significantly speed up the process of change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However, there is still a lack of aerial datasets that were made for the segmentation, covering rural area with resolution of tens centimeters per pixel, manual fine labels and highly publicly important environmental instances like buildings, woods or water. Here we introduce this http URL (Land Cover from Aerial Imagery) dataset for semantic segmentation. We collected images of 216.27 sq. km rural areas across Poland, a country in Central Europe, 39.51 sq. km with resolution 50 cm per pixel and 176.76 sq. km with resolution 25 cm per pixel and manually fine annotated three following classes of objects: buildings, woodlands, and water. Additionally, we report simple benchmark results, achieving 90.18% of mean intersection over union on the test set. It proves that the automatic mapping of land cover is possible with relatively small, cost efficient, RGB only dataset. The dataset is publicly available at this http URL
Using Machine Learning to Emulate Agent-Based Simulations

In this paper, we evaluate the performance of multiple machine-learning methods in the emulation of agent-based models (ABMs). ABMs are a popular methodology for modelling complex systems composed of multiple interacting processes. The analysis of ABM outputs is often not straightforward, as the relationships between input parameters can be non-linear or even chaotic, and each individual model run can require significant CPU time. Statistical emulation, in which a statistical model of the ABM is constructed to allow for more in-depth model analysis, has proven valuable for some applications. Here we compare multiple machine-learning methods for ABM emulation in order to determine the approaches best-suited to replicating the complex and non-linear behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and support vector machines outperform Gaussian process emulators, currently the most commonly used method for the emulation of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times for these emulators were considerably longer than for any other method. We propose that users of complex ABMs would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for their models as well as reducing CPU time consumption when calibrating and analysing the simulation.
Parameters Estimation from the 21 cm signal using Variational Inference

Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA) and Square Kilometre Array (SKA) are intended to measure the 21cm signal over a wide range of redshifts, representing an incredible opportunity in advancing our understanding about the nature of cosmic Reionization. At the same time these kind of experiments will present new challenges in processing the extensive amount of data generated, calling for the development of automated methods capable of precisely estimating physical parameters and their uncertainties. In this paper we employ Variational Inference, and in particular Bayesian Neural Networks, as an alternative to MCMC in 21 cm observations to report credible estimations for cosmological and astrophysical parameters and assess the correlations among them.
Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.
Online Convex Optimization With Binary Constraints

We consider online optimization with binary decision variables and convex loss functions. We design a new algorithm, binary online gradient descent (bOGD) and bound its expected dynamic regret. We provide a regret bound that holds for any time horizon and a specialized bound for finite time horizons. First, we present the regret as the sum of the relaxed, continuous round optimum tracking error, and the rounding error of our update in which the former asymptomatically decreases with time under certain conditions. Then, we derive a finite-time bound that is sublinear in time and linear in the cumulative variation of the relaxed, continuous round optima. We apply bOGD to demand response with thermostatically controlled loads, in which binary constraints model discrete on/off settings. We also model uncertainty and varying load availability, which depend on temperature deadbands, lockout of cooling units and manual overrides. We test the performance of bOGD in several simulations based on demand response. The simulations corroborate that the use of randomization in bOGD does not significantly degrade performance while making the problem more tractable.
Orphan GRB Afterglow Searches with the Pan-STARRS1 COSMOS Survey

We present the result of a search for orphan gamma-ray burst (GRB) afterglows in the Pan-STARRS1 (PS1) COSMOS survey. There is extensive theoretical and observational evidence suggesting that GRBs are collimated jets; the direct observation of orphan GRB afterglows would further support this model. An optimal survey strategy is designed by coupling the PS1 survey with the Subaru/Hyper-Suprime-Cam (HSC) survey. The PS1 COSMOS survey, one of the survey fields in the PS1 Medium Deep Survey (PS1/MDS), searches a field of 7 deg2 from 2011 December to 2014 January, reaching a limiting magnitude R ∼ 23. The dense cadence of PS1/MDS is crucial for identifying transients, and the deep magnitude reached by the HSC survey (R ∼ 26) is important for evaluating potential GRB hosts. A transient classification method is employed to select potential orphan GRB afterglow candidates. After a thorough analysis of the transient and host galaxy properties, we conclude that there are no candidates in this survey field. The null result implies that the consideration of jet structures is essential for further orphan GRB afterglow surveys.
Read Mapping Near Non-Volatile Memory.

DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS)revolutionized genomic research. As a result, modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel. The sequenced DNA fragments, representing the output of NGS platforms, are termed reads. Besides genomic variations, NGS imperfections induce noise in reads. Mapping each read to (the most similar portion of) a reference genome of the same species, i.e., read mapping, is a common critical first step in a diverse set of emerging bioinformatics applications. Mapping represents a search-heavy memory-intensive similarity matching problem, therefore, can greatly benefit from near-memory processing. Intuition suggests using fast associative search enabled by Ternary Content Addressable Memory (TCAM) by construction. However, the excessive energy consumption and lack of support for similarity matching (under NGS and genomic variation induced noise) renders direct application of TCAM infeasible, irrespective of volatility, where only non-volatile TCAM can accommodate the large memory footprint in an area-efficient way. This paper introduces GeNVoM, a scalable, energy-efficient and high-throughput solution. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, GeNVoM rethinks the algorithm and non-volatile TCAM-based accelerator design together from the ground up. Thereby GeNVoM can improve the throughput by up to 113.5 times (3.6); the energy consumption, by up to 210.9 times (1.36), when compared to a GPU (accelerator) baseline, which represents one of the highest-throughput implementations known.
Tolerance to Hairy Chinch Bug Feeding in Kentucky Bluegrass

Seventeen Kentucky bluegrass, Poa pratensis L. , cultivars were evaluated for tolerance to feeding by adult hairy chinch bugs, Blissus leucopterus hirtus Montandon. Adults were confined on one month-old plants within a 10 cm diam × 20.3 cm high cylindrical plastic cage for 17–19 days. Cages were divided longitudinally so that 1/2 of the plants in each pot were infested. Tolerance was evaluated as differences between infested and uninfested plants for height of regrowth, dry weight, yield of clippings, root length and weight, plant survival, tillering, and % dry matter. Regrowth, yield of clippings, root length, and plant survival were significantly reduced and % dry matter significantly increased in almost all cases by adult feeding. Dry weight, root weight, and tillering of plants were not significantly changed by feeding. Significant differences were found in tolerance among Kentucky bluegrass cultivars. Differences in cultivar regrowth, yield, and % dry matter were the most useful criteria for measuring tolerance.