Miracle-dz/UniFAR-FTUs huggingface dataset:
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding

Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation uncovers the limitations in the language-understanding abilities of neural networks. We demonstrate that these limitations can be overcome by addressing the generalization challenges in a recently-released dataset, gSCAN, which explicitly measures how well a robotic agent is able to interpret novel ideas grounded in vision, e.g., novel pairings of adjectives and nouns. The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing all other parameters and properties to be learned end-to-end with weak supervision. We build a general-purpose mechanism that enables robots to generalize their language understanding to compositional domains. Crucially, our base network has the same state-of-the-art performance as prior work, 97% execution accuracy, while at the same time generalizing its knowledge when prior work does not; for example, achieving 95% accuracy on novel adjective-noun compositions where previous work has 55% average accuracy. Robust language understanding without dramatic failures and without corner causes is critical to building safe and fair robots; we demonstrate the significant role that compositionality can play in achieving that goal.
Antiferromagnetic switching driven by the collective dynamics of a coexisting spin glass

Collective motion of correlated spins is demonstrated as a way to control antiferromagnetic memory in quantum technologies. The theory behind the electrical switching of antiferromagnets is premised on the existence of a well-defined broken symmetry state that can be rotated to encode information. A spin glass is, in many ways, the antithesis of this state, characterized by an ergodic landscape of nearly degenerate magnetic configurations, choosing to freeze into a distribution of these in a manner that is seemingly bereft of information. Here, we show that the coexistence of spin glass and antiferromagnetic order allows a novel mechanism to facilitate the switching of the antiferromagnet Fe1/3 + δNbS2, rooted in the electrically stimulated collective winding of the spin glass. The local texture of the spin glass opens an anisotropic channel of interaction that can be used to rotate the equilibrium orientation of the antiferromagnetic state. Manipulating antiferromagnetic spin textures using a spin glass’ collective dynamics opens the field of antiferromagnetic spintronics to new material platforms with complex magnetic textures.
Quantifying uncertainty and dynamical changes in multi‐species fishing mortality rates, catches and biomass by combining state‐space and size‐based multi‐species models

In marine management, fish stocks are often managed on a stock-by-stock basis using single-species models. Many of these models are based upon statistical techniques and are good at assessing the current state and making short-term predictions; however, as they do not model interactions between stocks, they lack predictive power on longer timescales. Additionally, there are mechanistic multi-species models that represent key biological processes and consider interactions between stocks such as predation and competition for resources. Due to the complexity of these models, they are difficult to fit to data, and so many mechanistic multi-species models depend upon single-species models where they exist, or ad hoc assumptions when they don't, for parameters such as annual fishing mortality. In this paper we demonstrate that by taking a state-space approach, many of the uncertain parameters can be treated dynamically, allowing us to fit, with quantifiable uncertainty, mechanistic multi-species models directly to data. We demonstrate this by fitting uncertain parameters, including annual fishing mortality, of a size-based multi-species model of the Celtic Sea, for species with and without single-species stock-assessments. Consequently, errors in the single-species models no longer propagate through the multi-species model and underlying assumptions are more transparent. Building mechanistic multi-species models that are internally consistent, with quantifiable uncertainty, will improve their credibility and utility for management. This may lead to their uptake by being either used to corroborate single-species models; directly in the advice process to make predictions into the future; or used to provide a new way of managing data-limited stocks.
Offline Meta Reinforcement Learning

Consider the following problem, which we term Offline Meta Reinforcement Learning (OMRL): given the complete training histories of $N$ conventional RL agents, trained on $N$ different tasks, design a learning agent that can quickly maximize reward in a new, unseen task from the same task distribution. In particular, while each conventional RL agent explored and exploited its own different task, the OMRL agent must identify regularities in the data that lead to effective exploration/exploitation in the unseen task. To solve OMRL, we take a Bayesian RL (BRL) view, and seek to learn a Bayes-optimal policy from the offline data. We extend the recently proposed VariBAD BRL algorithm to the off-policy setting, and demonstrate learning of Bayes-optimal exploration strategies from offline data using deep neural networks. Furthermore, when applied to the online meta-RL setting (agent simultaneously collects data and improves its meta-RL policy), our method is significantly more sample efficient than the conventional VariBAD.
A Robot that Counts Like a Child - a Developmental Model of Counting and Pointing

In this paper, a novel neuro-robotics model capable of counting real items is introduced. The model allows us to investigate the interaction between embodiment and numerical cognition. This is composed of a deep neural network capable of image processing and sequential tasks performance, and a robotic platform providing the embodiment - the iCub humanoid robot. The network is trained using images from the robot's cameras and proprioceptive signals from its joints. The trained model is able to count a set of items and at the same time points to them. We investigate the influence of pointing on the counting process and compare our results with those from studies with children. Several training approaches are presented in this paper all of them uses pre-training routine allowing the network to gain the ability of pointing and number recitation (from 1 to 10) prior to counting training. The impact of the counted set size and distance to the objects are investigated. The obtained results on counting performance show similarities with those from human studies.
Autoencoding With a Classifier System

Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost. Conditional computation, sparsity, and model pruning techniques can reduce these costs while maintaining performance. Learning classifier systems (LCSs) are a framework for adaptively subdividing input spaces into an ensemble of simpler local approximations that together cover the domain. LCS perform conditional computation through the use of a population of individual gating/guarding components, each associated with a local approximation. This article explores the use of an LCS to adaptively decompose the input domain into a collection of small autoencoders, where local solutions of different complexity may emerge. In addition to the benefits in convergence time and computational cost, it is shown possible to reduce the code size as well as the resulting decoder computational cost when compared with the global model equivalent.
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks

The problem of session-based recommendation aims to predict user next actions based on session histories. Previous methods models session histories into sequences and estimate user latent features by RNN and GNN methods to make recommendations. However under massive-scale and complicated financial recommendation scenarios with both virtual and real commodities , such methods are not sufficient to represent accurate user latent features and neglect the long-term characteristics of users. To take long-term preference and dynamic interests into account, we propose a novel method, i.e. User-Based Embeddings Recommendation with Graph Neural Network, UBER-GNN for brevity. UBER-GNN takes advantage of structured data to generate longterm user preferences, and transfers session sequences into graphs to generate graph-based dynamic interests. The final user latent feature is then represented as the composition of the long-term preferences and the dynamic interests using attention mechanism. Extensive experiments conducted on real Ping An scenario show that UBER-GNN outperforms the state-of-the-art session-based recommendation methods.
Coral reef habitats and their influence on reef assemblages

[Extract:] Coral reefs often characterise the immediate subtidal environment in tropical regions. Although any hard substratum can support corals, more generally the hard base (that may be 1–2 km thick on some reefs) consists mostly of dead corals. This base is generated by the death and assimilation of the carbonates fixed by coral polyps into the hard substratum and a veneer of live corals grows on this base (Chapter 9). The Great Barrier Reef (GBR) is characterised by a mosaic of reefs of differing morphologies (Chapter 2) that stretch on a north– south axis for over 2000 km and in places up to 300 km wide. The architecture of individual reefs is largely based on a sculptured rock base and habitat-forming coral of different species that can vary depending on exposure to the predominant winds (i.e. windward and leeward sides of reefs). A key to understanding patterns of the diversity in species richness and diversity of functional groups of organisms lies in their habitat preferences and what is driving habitat heterogeneity
HooliGAN: Robust, High Quality Neural Vocoding

Recent developments in generative models have shown that deep learning combined with traditional digital signal processing (DSP) techniques could successfully generate convincing violin samples [1], that source-excitation combined with WaveNet yields high-quality vocoders [2, 3] and that generative adversarial network (GAN) training can improve naturalness [4, 5]. By combining the ideas in these models we introduce HooliGAN, a robust vocoder that has state of the art results, finetunes very well to smaller datasets (<30 minutes of speechdata) and generates audio at 2.2MHz on GPU and 35kHz on CPU. We also show a simple modification to Tacotron-basedmodels that allows seamless integration with HooliGAN. Results from our listening tests show the proposed model's ability to consistently output high-quality audio with a variety of datasets, big and small. We provide samples at the following demo page: this https URL
Forensic undergraduate cohort; job readiness curricula

ABSTRACT A self- and peer-assessment strategy was implemented to support learning, teaching and assessment of undergraduate forensic science students’ capacity to work in a team. This strategy aimed to fill the gap observed in teamwork assessment, which historically focuses on the project outcome rather than an individual’s performance within a team. The online assessment tool, SPARKPLUS, was chosen as the platform to provide students with the teamwork skills criteria and levels of achievement they were required to self and peer assess against, during forensic investigation assessment team tasks. This work-integrated learning involved three components: (1) processing a mock crime scene; (2) laboratory analysis of the evidence collected; and (3) development and presentation of a police brief of evidence. A comparison of the distribution of the self- and peer-assessment ratings from students in second- and third-year cohorts shows that this approach improves assessment of teamwork.
EDUCATION PORTAL FOR INFORMATION STORAGE, SHARING AND COLLABORATION

Today the Internet has provided great opportunities for enterprise integration among internal departments, external partners and other service providers. Web services based systems have recently found their way into many applications such as e-commerce, corporate portals and e-learning. Construction of new services or introducing new functions to existing services requires composition of web services. Current approaches to service composition often require major programming effort; this is time consuming and requires considerable developer expertise. Planning for Web portal implementation should be seen as a process of building an infrastructure and foundation for the future. In this paper, we explore and identify the requirements for a basic portal framework in the design, implementation and deployment of an Educational Web Portal Service at the School of Health Professions (SHP), University of Puerto Rico. We also discuss the benefits of this platform and some of the tools provided by the portal environment to fulfill the organization needs and the lessons learned during the architecture design and software implementation.
Tumour necrosis factor‐α‐induced expression of intercellular adhesion molecule‐1 on human eosinophilic leukaemia EoL‐1 cells is mediated by the activation of nuclear factor‐κB pathway

Background Intercellular adhesion molecule‐1 (ICAM‐1) has been shown to mediate the adhesion and migration of eosinophils to the site of allergic inflammation. However, molecular mechanisms regulating the expression of ICAM‐1 in eosinophils are still being elucidated. We investigated the effect of tumour necrosis factor‐alpha (TNF‐α) on ICAM‐1 expression of eosinophils.
An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction

Mobile learning is undergoing rapid evolution. While early generations of mobile learning tended to propose activities that were carefully crafted by educators and technologists, learners are increasingly motivated by their personal learning needs, including those arising from greater mobility and frequent travel. At the same time, it is often argued that mobile devices are particularly suited to supporting social contacts and collaborative learning-claims that have obvious relevance for language learning. A review of publications reporting mobile-assisted language learning (MALL) was undertaken to discover how far mobile devices are being used to support social contact and collaborative learning. In particular, we were interested in speaking and listening practice and in the possibilities for both synchronous and asynchronous interaction in the context of online and distance learning. We reflect on how mobile language learning has developed to date and suggest directions for the future.
Hypoxia Inducible Factor-1 as a Therapeutic Target in Cerebral Ischemia

Cerebral ischemia is the third leading cause of death in industrialized countries and an important health system problem with no efficient treatment to date. The reduction in oxygen and glucose supply triggers a cascade of events such as excitotoxicity, oxidative stress, inflammation, apoptosis, and an adjustment of the gene expression program. The hy- poxia inducible factor-1 (HIF-1) is a transcription factor that mediates the adaptive responses to the reduction in oxygen availability. HIF-1 activation in hypoxic conditions promotes the survival of cells through the induction of adaptive genes involved in glucose and energy production, glucose transport, cell proliferation, cell survival, iron homeostasis, erythro- poiesis, angiogenesis and vascular reactivity. In recent years, biomedical research has focused on the oxygen regulatedsubunit of HIF-1 as a potential therapy in medical events that involve hypoxic conditions. In this review we summarize current knowledge on the use of HIF-1� as a therapeutic target against cerebral ischemia through two different strategies: 1) the use of ischemic preconditioning to in- duce the HIF-1 pathway regulation, and 2) the pharmacological inhibition of prolyl and asparaginyl hydroxylases. Both strategies appear as attractive options for protection in ischemia in which signaling pathway activation could contribute to the establishment of tolerance in brain.
Models of interference and their consequences for the spatial distribution of ideal and free predators

1. In order to predict the spatial distribution of 'ideal' and 'free' predators, one needs to know how food intake rate of an individual predator is related to characteristics of the population of the prey as well as the predators themselves. 2. Surprisingly, a systematic theoretical investigation of models for the basic case, where both prey and predators are best characterized by their 'standing stock' density, is lacking. In these models intake rate is supposed to decrease with increasing predator density as a result of interference among predators, instead of immediate consumption of the prey. 3. This paper compares the various ways applied so far of incorporating interference in Holling's functional response model. It is shown that the different models of interference result in qualitatively different predictions for 'ideal' and 'free' predators on: (i) the form of the aggregative response; (ii) the trajectory of the aggregative response as prey is depleted; and (iii) the change in the aggregative response following an influx of predators. This sheds doubt on the general relevance of any one of these models, particularly if the mathematical formulation of interference is phenomenological and merely based on convention, instead of being derived from the underlying mechanism of interactions between predators. 4. Our results underline the need for detailed knowledge about the components of the predation process in order to arrive at predictions for a specific case.
Carotid Webs: An Unusual Presentation of Fibromuscular Dysplasia

Carotid webs are abnormal luminal projections at the carotid bulb associated with blood flow stasis, artery dissection, and subsequent complications. Carotid webs are considered to be a rare variant of fibromuscular dysplasia (FMD). Young individuals with symptomatic carotid webs are found to be associated with ischemic stroke. The incidence of the carotid web is low, and it is rarely reported. Only 150 cases of FMD have been reported so far. FMD is a non-inflammatory and non-atherosclerotic arteriopathy. The most common arterial beds involved are renal and extracranial carotids. Presentation varies depending on the location of the arterial bed involved and disease severity. Clinical presentations range from minor headaches to severe headaches, resistant hypertension, acute coronary syndrome, transient ischemic attack, and in some cases, stroke. Diagnosis can be made through non-invasive methods, such as computed tomographic angiography, magnetic resonance angiography, or duplex ultrasonography or invasive imaging methods like catheter-based angiography. Treatment of FMD varies with disease presentation and its location. Asymptomatic carotid or vertebral arteries FMD should be monitored clinically and prescribed aspirin 81 mg daily for primary stroke prevention. Endovascular and surgical therapy with stents or coils is reserved for patients with aneurysms. We present a rare and interesting case of a 54-year-old female who presented with acute ischemic stroke in the setting of right carotid artery web, right internal carotid artery (ICA) thrombus with dissection, and possible pseudoaneurysm.
Treating Alzheimer’s Disease (AD) with Light and Sound

Normal human brain functions through the complex acquisition, coordination, integration and processing of multiple and different afferent inputs and amongst the most prominent and important of these are the environmentally/externally-sourced input of light and sound. Together visual and acoustic stimulation accounts for about ~85% of the afferent input to the human brain. The natural generation of gamma oscillations in the brain between 25 Hz and 140 Hz from this coordinated and integrated neural activity are thought to underlie multiple brain functions as diverse as attention, behavior, cognitive processing and memory. These natural oscillations, sometimes referred to as biological rhythms or biorhythms, may be significantly disrupted as we age and especially so in insidious, age-related, progressive and ultimately lethal neurological disorders such as Alzheimer’s Disease (AD) and frontotemporal dementia (FTD). The recent research work by Li-Huei Tsai’s research group at MIT on combined acoustic and visual gamma oscillation entrainment and its significantly positive effects on the cognitive functions of neural cell networks raises the intriguing possibility that artificial visual and acoustic stimulation of the brain can be successfully exploited to preserve both neural connectivity, neuronal densities and synaptic signalling function and elicit significant neuroprotection against progressive, age-related inflammatory and degenerative neuropathology [1].
Loss of ecologically important genetic variation in late generation hybrids reveals links between adaptation and speciation

Adaptation to contrasting environments occurs when advantageous alleles accumulate in each population, but it remains largely unknown whether these same advantageous alleles create genetic incompatibilities that can cause intrinsic reproductive isolation leading to speciation. Identifying alleles that underlie both adaptation and reproductive isolation is further complicated by factors such as dominance and genetic interactions among loci, which can affect both processes differently and obscure potential links between adaptation and speciation. Here, we use a combination of field and glasshouse experiments to explore the connection between adaptation and speciation while accounting for dominance and genetic interactions. We created a hybrid population with equal contributions from four contrasting ecotypes of Senecio lautus (Asteraceae), which produced hybrid genomes both before (F1 hybrid generation) and after (F4 hybrid generation) recombination among the parental ecotypes. In the glasshouse, plants in the second generation (F2 hybrid generation) showed reduced fitness as a loss of fertility. However, fertility was recovered in subsequent generations, suggesting that genetic variation underlying the fitness reduction was lost in subsequent generations. To quantify the effects of losing genetic variation at the F2 generation on the fitness of later generation hybrids, we used a reciprocal transplant to test for fitness differences between parental ecotypes, and F1 and F4 hybrids in all four parental habitats. Compared to the parental ecotypes and F1 hybrids, variance in F4 hybrid fitness was lower, and lowest in habitats that showed stronger native‐ecotype advantage, suggesting that stronger natural selection for the native ecotype reduced fitness variation in the F4 hybrids. Fitness trade‐offs that were present in the parental ecotypes and F1 hybrids were absent in the F4 hybrid. Together, these results suggest that the genetic variation lost after the F2 generation was likely associated with both adaptation and intrinsic reproductive isolation among ecotypes from contrasting habitats.