Prior work on plant species classification predominantly focuses on building models from isolated plant attributes. Hence, there is a need for tools that can assist in species identification in the natural world. We present a novel and robust two-fold approach capable of identifying trees in a real-world natural setting. Further, we leverage unlabelled data through deep semi-supervised learning and demonstrate superior performance to supervised learning. Our single-GPU implementation for feature recognition uses minimal annotated data and achieves accuracies of 93.96% and 93.11% for leaves and bark, respectively. Further, we extract feature-specific datasets of 50 species by employing this technique. Finally, our semi-supervised species classification method attains 94.04% top-5 accuracy for leaves and 83.04% top-5 accuracy for bark.

Recently, there have been tremendous research outcomes in the fields of speech recognition and natural language processing. This is due to the well-developed multi-layers deep learning paradigms such as wav2vec2.0, Wav2vecU, WavBERT, and HuBERT that provide better representation learning and high information capturing. Such paradigms run on hundreds of unlabeled data, then fine-tuned on a small dataset for specific tasks. This paper introduces a deep learning constructed emotional recognition model for Arabic speech dialogues. The developed model employs the state of the art audio representations include wav2vec2.0 and HuBERT. The experiment and performance results of our model overcome the previous known outcomes.

Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their performance for many molecular tasks. However, this information is infeasible to compute at the scale required by several real-world applications. We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. Using methods from self-supervised learning, we maximize the mutual information between 3D summary vectors and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still produces implicit 3D information and can use it to improve downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces. The understanding of molecular and quantum chemistry is a rapidly growing area for deep learning with models having direct real-world impacts in quantum chemistry (Dral, 2020), protein structure prediction (Jumper et al., 2021), materials science (Schmidt et al., 2019), and drug discovery (Stokes et al., 2020). In particular, for the task of molecular property prediction, GNNs have had great success (Yang et al., 2019). GNNs operate on the molecular graph by updating each atom's representation based on the atoms connected to it via covalent bonds. However, these models reason poorly about other important interatomic forces that depend on the atoms' relative positions in space. Previous works showed that using the atoms' 3D coordinates in space improves the accuracy of molecular property prediction (Schütt et al., 2017; Klicpera et al., 2020b; Liu et al., 2021; Klicpera et al., 2021). However, using classical molecular dynamics simulations to explicitly compute a molecule's geometry before predicting its properties is computationally intractable for many real-world applications. Even recent Machine Learning (ML) methods for conformation generation (Xu et al., 2021b; Shi et al., 2021; Ganea et al., 2021) are still too slow for large-scale applications. A GNN is pre-trained by maximizing the mutual information (MI) between its embedding of a 2D molecular graph and a representation capturing the 3D information that is produced by a separate network.

We propose an automaton model which is a combination of symbolic and register automata, i.e., we enrich symbolic automata with memory. We call such automata Symbolic Register Automata (SRA). SRA extend the expressive power of symbolic automata, by allowing Boolean formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers. SRA also extend register automata, by allowing arbitrary Boolean formulas, besides equality predicates. We study the closure properties of SRA under union, intersection, concatenation, Kleene closure, complement and determinization and show that SRA, contrary to symbolic automata, are not in general closed under complement and they are not determinizable. However, they are closed under these operations when a window operator, quintessential in Complex Event Recognition, is used. We show how SRA can be used in Complex Event Recognition in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics, and that allows for a systematic treatment of such automata. We also show how the behavior of SRA, as they consume streams of events, can be given a probabilistic description with the help of prediction suffix trees. This allows us to go one step beyond Complex Event Recognition to Complex Event Forecasting, where, besides detecting complex patterns, we can also efficiently forecast their occurrence.

In cooperative bandits, a framework that captures essential features of collective sequential decision making, agents can minimize group regret, and thereby improve performance, by leveraging shared information. However, sharing information can be costly, which motivates developing policies that minimize group regret while also reducing the number of messages communicated by agents. Existing cooperative bandit algorithms obtain optimal performance when agents share information with their neighbors at \textit{every time step}, i.e., full communication. This requires $\Theta(T)$ number of messages, where $T$ is the time horizon of the decision making process. We propose \textit{ComEx}, a novel cost-effective communication protocol in which the group achieves the same order of performance as full communication while communicating only $O(\log T)$ number of messages. Our key step is developing a method to identify and only communicate the information crucial to achieving optimal performance. Further we propose novel algorithms for several benchmark cooperative bandit frameworks and show that our algorithms obtain \textit{state-of-the-art} performance while consistently incurring a significantly smaller communication cost than existing algorithms.

Patent offices and courts around the world are being asked to tackle a similar question: can an artificial intelligence system qualify as an inventor for a patent? A test case making its way through several countries--from Saudi Arabia to Australia to Brazil--has spurred debate about advancements in artificial intelligence technology and questions about whether patent laws need to be revised to recognize machines as inventors. A judge in the U.S. District Court for the Eastern District of Virginia recently ruled that, under current U.S. law, AI can't be listed as an inventor on a patent. The ruling was in line with what U.S., British, and EU patent officials have concluded. The push to recognize AI as an inventor comes from Ryan Abbott, a University of Surrey law professor, and Stephen Thaler, a computer scientist from Missouri.

Ukraine said it will build a factory to produce Turkish armed drones that Kyiv previously bought to use against pro-Russian separatists in the east, a deal that might upset Kyiv's adversary Moscow. "A land plot on which the factory will be built has already been chosen," Ukrainian Foreign Minister Dmytro Kuleba said at a news conference on Thursday with Turkish counterpart Mevlut Cavusoglu in the western Ukrainian city of Lviv. "There were a number of obstacles to the implementation [of this project] but all of them have been removed," he added, without providing further details. Pleased to welcome my Turkish colleague and friend @MevlutCavusoglu in Lviv and expand our diplomatic geography. Cavusoglu did not speak specifically about the subject but stressed that Kyiv and Ankara were "in the process of strengthening their relations in many sectors", including defence.

Pangolins, also known as scaly anteaters, are currently the most trafficked mammal species. Criminals can be resourceful and unrelenting in their efforts to find a way around obstacles. Wildlife traffickers are no exception. Today's trade in wildlife and wildlife products has shifted from physical markets to online marketplaces where traffickers apply e-commerce business models and use encrypted messages in an attempt to evade detection by law enforcement. While the move towards online platforms started several years before the Covid-19 pandemic, the restrictions imposed to contain the virus accelerated this digital transformation.

Earlier this month, a federal district court issued the first judicial decision in the country addressing whether an AI system can be an "inventor" under U.S. patent law. The decision was rendered by the U.S. District Court for the Eastern District of Virginia in Thaler v. Hirshfeld on appeal from the U.S. Patent and Trademark Office's (USPTO) decision that refused to allow Thaler's two patent applications to proceed because he listed DABUS (an AI machine) as the inventor. Thaler filed the applications in 2018--one for an invention used to contain food and the other for a flashing beacon for attracting attention in emergencies. In statements filed in support of the applications, Thaler listed DABUS as the inventor, claiming that he had acquired the right to the grant of the patents by "ownership of the creativity machine." In affirming the USPTO's denial of the applications, the court held that based on the plain statutory language of the U.S. Patent Act and Federal Circuit authority, an AI machine cannot be an inventor because an inventor must be an "individual," which under common interpretation and court precedent means a natural person. The court stated that Thaler's argument was based on policy considerations and the purpose of the patent clause of the U.S. Constitution, and that the decision to expand the scope of inventorship is squarely within the authority of Congress.

Pruning is a model compression method that removes redundant parameters in deep neural networks (DNNs) while maintaining accuracy. Most available filter pruning methods require complex treatments such as iterative pruning, features statistics/ranking, or additional optimization designs in the training process. In this paper, we propose a simple and effective regularization strategy from a new perspective of evolution of features, which we call feature flow regularization (FFR), for improving structured sparsity and filter pruning in DNNs. Specifically, FFR imposes controls on the gradient and curvature of feature flow along the neural network, which implicitly increases the sparsity of the parameters. The principle behind FFR is that coherent and smooth evolution of features will lead to an efficient network that avoids redundant parameters. The high structured sparsity obtained from FFR enables us to prune filters effectively. Experiments with VGGNets, ResNets on CIFAR-10/100, and Tiny ImageNet datasets demonstrate that FFR can significantly improve both unstructured and structured sparsity. Our pruning results in terms of reduction of parameters and FLOPs are comparable to or even better than those of state-of-the-art pruning methods.