Terrestrial vegetation is a critical component of global biogeochemical cycles and provides important ecosystem services to support human life [1]. Given its importance, it is essential to know the spatial-temporal variations of vegetation [2]. These variations are due to several determining factors such as global climate variability, climate gradients, and anthropogenic factors such as Land Use and Land Cover Change (LULCC). The diversity in climatic conditions and vegetation types pose different obstacles to monitoring and classifying land cover using remote sensing. Mexico is considered one of the mega-diverse countries on the planet due to its location in a transition zone between Nearctic and Neotropic regions making it more difficult for land use classification and monitoring. The anthropogenic factors, could be a trigger for deforestation and forest degradation [3] and have a severe impact on the global carbon cycle, soil erosion, hydrological cycles, and in general, affect on the ecosystem services that sustain society [4]. As a result, timely land cover monitoring and classification are of crucial importance for assessing gradual degradation-ecosystem processes. Furthermore, it is important to be in line with the United Nations Sustainable Development Goals (SDGs) specifically SDG 15 concerning "Life on Land" [5].

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

This paper presents an empirical investigation of the relation between decision speed and decision quality for a real-world setting of cognitively-demanding decisions in which the timing of decisions is endogenous: professional chess. Move-by-move data provide exceptionally detailed and precise information about decision times and decision quality, based on a comparison of actual decisions to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results reveal that faster decisions are associated with better performance. The findings are consistent with the predictions of procedural decision models like drift-diffusion-models in which decision makers sequentially acquire information about decision alternatives with uncertain valuations.

Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to endow sequential change-point detection (CPD) techniques with a graph representation learning substrate based on the versatile Random Dot Product Graph (RDPG) model. We consider efficient, online updates of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal RDPG. This reference distribution is inferred via spectral embeddings of the first few graphs in the sequence. We characterize the distribution of this running statistic to select thresholds that guarantee error-rate control, and under simplifying approximations we offer insights on the algorithm's detection resolution and delay. The end result is a lightweight online CPD algorithm, that is also explainable by virtue of the well-appreciated interpretability of RDPG embeddings. This is in stark contrast with most existing graph CPD approaches, which either rely on extensive computation, or they store and process the entire observed time series. An apparent limitation of the RDPG model is its suitability for undirected and unweighted graphs only, a gap we aim to close here to broaden the scope of the CPD framework. Unlike previous proposals, our non-parametric RDPG model for weighted graphs does not require a priori specification of the weights' distribution to perform inference and estimation. This network modeling contribution is of independent interest beyond CPD. We offer an open-source implementation of the novel online CPD algorithm for weighted and direct graphs, whose effectiveness and efficiency are demonstrated via (reproducible) synthetic and real network data experiments.

Woflow, a data infrastructure company, raised $7.3 million in Series A funding to continue developing its automated approach to bring offline data online. The company helps customers with antiquated inventory systems power their merchant onboarding data, like restaurant menus and images, with APIs to structure data in a way that when someone's food order requests "no mustard," it is recognized properly, Woflow co-founder and CEO Jordan Nemrow told TechCrunch. Nemrow and Will Bewley founded the San Francisco-based company in 2017. "In the background, machine learning models and artificial intelligence-powered humans in the loop do the structuring for our customers, which include food delivery, e-commerce and point-of-sale," Nemrow added. "Restaurants usually deal with having offline data, but time equals money, and if there is incorrect data, there can be some financial reimbursement. We are the de facto solution for that."

Artificial intelligence (AI) may soon know more about you than you think. A startup called Hume AI claims to use algorithms to measure emotions from facial, vocal, and verbal expressions. It's one of a growing number of companies that purport to read human emotions using computers. But some experts say that the concept raises privacy issues. "Whoever controls these systems and platforms are going to have a lot of information on individuals," Bob Bilbruck, a tech startup advisor, told Lifewire in an email interview.

Even though transformers Vaswani et al. [2017], Devlin et al. [2019] have become the state-of-the-art and at par with humans for several natural language processing (NLP) tasks, their applications in vision has been severely limited by their quadratic complexity with respect to sequence length. Even low resolution images, when unrolled, become long 1D sequences of tens of thousands of pixels, and impose a large computational and memory burden on a GPU. A transformer, being a general architecture without an inductive prior, also requires a large number of training images for giving good generalization compared to convolutional models. It also needs extra architectural changes, including the addition of positional embeddings, to gather the positional information of various image pixels. This demand for large amount of data and GPU resources is not suitable for resource-constrained scenarios where data and GPU capabilities are limited, such as green or edge computing Khan et al. [2021]. On the other hand, CNNs have the inductive priors, such as translational equivariance due to convolutional weight sharing and partial scale invariance due to pooling, to handle 2D images which enables them to learn from smaller datasets with less computational expenditure. But, they fail to capture long range dependencies compared to transformers and require deeper networks with several layers to increase their receptive fields. Combining the efficiency and inductive priors of CNNs with the long range information capturing ability of attention can create better architectures that are suitable for computer vision applications.

Keeping track of and managing Self-Admitted Technical Debts (SATDs) is important for maintaining a healthy software project. Current active-learning SATD recognition tool involves manual inspection of 24% of the test comments on average to reach 90% of the recall. Among all the test comments, about 5% are SATDs. The human experts are then required to read almost a quintuple of the SATD comments which indicates the inefficiency of the tool. Plus, human experts are still prone to error: 95% of the false-positive labels from previous work were actually true positives. To solve the above problems, we propose DebtFree, a two-mode framework based on unsupervised learning for identifying SATDs. In mode1, when the existing training data is unlabeled, DebtFree starts with an unsupervised learner to automatically pseudo-label the programming comments in the training data. In contrast, in mode2 where labels are available with the corresponding training data, DebtFree starts with a pre-processor that identifies the highly prone SATDs from the test dataset. Then, our machine learning model is employed to assist human experts in manually identifying the remaining SATDs. Our experiments on 10 software projects show that both models yield a statistically significant improvement in effectiveness over the state-of-the-art automated and semi-automated models. Specifically, DebtFree can reduce the labeling effort by 99% in mode1 (unlabeled training data), and up to 63% in mode2 (labeled training data) while improving the current active learner's F1 relatively to almost 100%.

Among the most common types of skin cancer are basal cell carcinoma, squamous cell carcinoma and melanoma. According to the who (2018), currently, between 2 and 3 million non-melanoma skin cancers and 132.000 melanoma skin cancer occur every year in the world. Melanoma is by far the most dangerous form of skin cancer, causing more than 75% of all skin cancer deaths (Allen, 2016). Early diagnosis of the disease plays an important role in reducing the mortality rate with a chance of cure greater than 90% (SBD, 2018). The diagnosis of pigmented skin lesions (PSLs) can be made by invasive and non-invasive methods. One of the most common non-invasive methods was presented by Soyer et al. (1987). The method allows the visualization of morphological structures not visible to the naked eye with the use of an instrument called dermatoscope. When compared to the clinical diagnosis, the use of dermatoscope by experts makes the diagnosis of PSLs easier, increasing by 10-27% the diagnostic sensitivity (Mayer et al., 1997).

Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) and named entity normalization (NEN). We propose a novel method of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a transformer model pretrained on a large EHR dataset. Our model is trained end-to-end in an distantly supervised manner using targets automatically extracted from medical knowledge base. We show that our model learns to generalize for entities that are present frequently enough, achieving human-level classification quality for most frequent entities. Our work demonstrates that medical entity extraction can be done end-to-end without human supervision and with human quality given the availability of a large enough amount of unlabeled EHR and a medical knowledge base.