Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conceptual Chronicle of Solving Raven's Progressive Matrices Computationally

Matrix reasoning or geometric analogy problems, like those found on the widely used Raven’s Progressive Matrices test of intelligence, have been used as a challenge for machine intelligence since the early work of Evans in the 1960s. While AI research on the RPM has gone through dramatic shifts alongside other AI advances, including a dramatic rise of machine-learning-based approaches in the last five years, many of these studies have progressed in relatively siloed research lines, making it difficult to compare different approaches and judge progress in the field as a whole. This paper intends to provide a framework for understanding the different lines of work in this research field. In particular, we reviewed 50+ computational models for solving RPM or RPM-like problems and collated them into a linear conceptual framework to help researchers navigate across these diverse research paradigms. We also provide instructions on other resources such as problem/data sets and necessary background knowledge of RPM.
Adaptive Learning Rates for Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning (MARL), the learning rates of actors and critic are mostly hand-tuned and fixed. This not only requires heavy tuning but more importantly limits the learning. With adaptive learning rates according to gradient patterns, some optimizers have been proposed for general optimizations, which however do not take into consideration the characteristics of MARL. In this paper, we propose AdaMa to bring adaptive learning rates to cooperative MARL. AdaMa evaluates the contribution of actors’ updates to the improvement of Q-value and adaptively updates the learning rates of actors to the direction of maximally improving the Q-value. AdaMa could also dynamically balance the learning rates between the critic and actors according to their varying effects on the learning. Moreover, AdaMa can incorporate the second-order approximation to capture the contribution of pairwise actors’ updates and thus more accurately updates the learning rates of actors. Empirically, we show that AdaMa could accelerate learning and improve performance in a variety of multi-agent scenarios. More importantly, AdaMa does not require heavy hyperparameter tuning and thus significantly reduces the training cost. The visualizations of learning rates during training clearly explain how and why AdaMa works.
Learning Transferable Representations for Non-stationary Environments

For intelligent agents to become fully autonomous, they need to perceive and adapt to the changes in environmental dynamics. In addition, they need to devise a strategy to acquire new knowledge while retaining the past learned ones. Humans can acquire, retain and transfer knowledge over their lifespan. In a similar vein, intelligent agents are becoming capable of acquiring and transferring knowledge but not retaining it. Towards reaching these goals, we have proposed algorithms that address multiple aspects of machine intelligence, from robot perception, allowing robots to accurately model human intent and predict human motion, to knowledge retention, allowing robots to retain past knowledge without forgetting. Our proposed algorithms have attained state-of-the-art performances for robot perception and overcoming catastrophic forgetting in perception-based tasks. Our current and ongoing work builds upon our completed works to explore knowledge retention in more challenging domains, particularly robot control, and investigate multi-agent collaboration as a precursor for human-robot collaboration.
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games

Self-play (SP) is a popular multi-agent reinforcement learning (MARL) framework for solving competitive games, where each agent optimizes policy by treating others as part of the environment. Despite the empirical successes, the theoretical properties of SP-based methods are limited to two-player zero-sum games. However, for mixed cooperative-competitive games where agents on the same team need to cooperate with each other, we can show a simple counter-example where SP-based methods cannot converge to a global Nash equilibrium (NE) with high probability. Alternatively, Policy-Space Response Oracles (PSRO) is an iterative framework for learning NE, where the best responses w.r. t. previous policies are learned in each iteration. PSRO can be directly extended to mixed cooperative-competitive settings by jointly learning team best responses with all convergence properties unchanged. However, PSRO requires repeatedly training joint policies from scratch till convergence, which makes it hard to scale to complex games. In this work, we develop a novel algorithm, Fictitious Cross-Play (FXP), which inherits the benefits from both frameworks. FXP simultaneously trains an SP-based main policy and a counter population of best response policies. The main policy is trained by fictitious self-play and cross-play against the counter population, while the counter policies are trained as the best responses to the main policy's past versions. We validate our method in matrix games and show that FXP converges to global NEs while SP methods fail. We also conduct experiments in a gridworld domain, where FXP achieves higher Elo ratings and lower exploitabilities than baselines, and a more challenging football game, where FXP defeats SOTA models with over 94% win rate.
User Device Interaction Prediction via Relational Gated Graph Attention Network and Intent-aware Encoder

With the booming of smart home market, intelligent Internet of Things (IoT) devices have been increasingly more involved in home life. To improve the user experience of smart home, some prior works have explored how to use time series analysis technology for predicting the interaction between users and devices. However, existing solutions have inferior User Device Interaction (UDI) prediction accuracy, as they fail to consider the complex heterogeneous device transitions, multiple intents of a user and multi-level period-icity of user behaviors. In this paper, we present DeepUDI, a novel approach for accurate UDI prediction. First, we propose Relational Gated Graph Attention Network (RGGAT) to learn embedding of device and device control while considering complex heterogeneous temporal transitions. Second, we propose Intent-aware Encoder (IAE) to encode multiple intents of users via capsule networks. Third, we design a Historical Attention Mechanism (HAM) to capture the multi-level periodicity by aggregating the current sequence and the historical sequence representations through the attention mechanism. Comprehensive experiments on four real-world datasets show that DeepUDI consistently outperforms state-of-the-art baselines and also offers highly interpretable results.
Developing a Multiple-EDT-Supervision Interface

With increasing automation beyond a professional level and into private and public life, one can assume that the ironies of automation explained by Bainbridge have been experienced by most of the members of our hybrid society – one where interaction between humans and embodied digital technology (EDT) becomes the agenda. In order to realise the full potential of hybrid teams we propose taking a new perspective, where EDTs do not operate on a fixed level of automation but become cognisant of their supervisor’s capabilities through a shared interface. This enables mutual perception and thus the dynamical adjustment between joint or autonomous activity.
Stigmergy-based, Dual-Layer Coverage of Unknown Regions

We present algorithms for uniformly covering an unknown indoor region with a swarm of simple, anonymous and autonomous mobile agents. The exploration of such regions is made difficult by the lack of a common global reference frame, severe degradation of radio-frequency communication, and ground obstacles. We propose addressing these challenges by using airborne agents, such as Micro Air Vehicles, in dual capacity, both as mobile explorers and, once they land, as beacons that help other agents navigate the region. The algorithms we propose are designed for a swarm of identical ant-like agents with local sensing capabilities. The agents enter the region, which is represented as a graph, over time from one or more entry points and are required to occupy all of its vertices. Unlike many works in this area, we consider the task of informing an outside operator with limited information that the coverage mission is complete. Even with this additional requirement we show, both through simulations and mathematical proofs, that the dual role concept results in linear-time termination, while also improving many well-known algorithms in the literature in terms of energy use.
Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance

ACM Reference Format: M. Cairo, G. Doerksen, B. Eldaphonse, J. Günther, N. Kummer, J. Maretzki, G. Mohaar, P. Mousavi, S. Murphy, L. Petrich, Sahir, J. Sheikh, T. Syed, and M. E. Taylor, Bevin Eldaphonse, Payam Mousavi, Sahir, Sheikh Jubair, Matthew E. Taylor∗, Graham Doerksen, Nikolai Kummer, Jordan Maretzki, Gurpreet Mohaar, Sean Murphy, Johannes Günther†, Laura Petrich†, and Talat Syed†. 2023. Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance. In Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, United Kingdom, May 29 – June 2, 2023, IFAAMAS, 3 pages.
Impact of polyphenols on heart failure and cardiac hypertrophy: clinical effects and molecular mechanisms

Polyphenols are abundant in regular diets and possess antioxidant, anti-inflammatory, anti-cancer, neuroprotective, and cardioprotective effects. Regarding the inadequacy of the current treatments in preventing cardiac remodeling following cardiovascular diseases, attention has been focused on improving cardiac function with potential alternatives such as polyphenols. The following online databases were searched for relevant orginial published from 2000 to 2023: EMBASE, MEDLINE, and Web of Science databases. The search strategy aimed to assess the effects of polyphenols on heart failure and keywords were “heart failure” and “polyphenols” and “cardiac hypertrophy” and “molecular mechanisms”. Our results indicated polyphenols are repeatedly indicated to regulate various heart failure-related vital molecules and signaling pathways, such as inactivating fibrotic and hypertrophic factors, preventing mitochondrial dysfunction and free radical production, the underlying causes of apoptosis, and also improving lipid profile and cellular metabolism. In the current study, we aimed to review the most recent literature and investigations on the underlying mechanism of actions of different polyphenols subclasses in cardiac hypertrophy and heart failure to provide deep insight into novel mechanistic treatments and direct future studies in this context. Moreover, due to polyphenols' low bioavailability from conventional oral and intravenous administration routes, in this study, we have also investigated the currently accessible nano-drug delivery methods to optimize the treatment outcomes by providing sufficient drug delivery, targeted therapy, and less off-target effects, as desired by precision medicine standards.
Low extracellular vesicle concentrations predict survival in patients with heart failure

Background Heart disease is of worldwide importance due to high morbidity and mortality. Extracellular vesicle (EV) concentration and size represent novel diagnostic and prognostic biomarkers, e.g. in patients with liver cancer, but data on their prognostic relevance in heart disease are lacking. Here, we investigated the role of EV concentration, size and zeta potential in patients with heart disease. Methods Vesicle size distribution, concentration and zeta potential were measured by nanoparticle tracking analysis (NTA) in 28 intensive care unit (ICU) and 20 standard care (SC) patients and 20 healthy controls. Results Patients with any disease had a lower zeta potential compared to the healthy controls. Vesicle size (X50) was significantly higher in ICU patients (245 nm) with heart disease as compared to those patients with heart disease receiving standard care (195 nm), or healthy controls (215 nm) (p = 0.001). Notably, EV concentration was lower in ICU patients with heart disease (4.68 × 1010 particles/ml) compared to SC patients with heart disease (7,62 × 1010 particles/ml) and healthy controls (1.50 × 1011 particles/ml) (p = 0.002). Extracellular vesicle concentration is prognostic for overall survival in patients with heart disease. Overall survival is significantly reduced when the vesicle concentration is below 5.55 × 1010 particles/ml. Median overall survival was only 140 days in patients with vesicle concentrations below 5.55 × 1010 particles/ml compared to 211 days in patients with vesicle concentrations above 5.55 × 1010 particles/ml (p = 0.032). Summary Concentration of EVs is a novel prognostic marker in ICU and SC patients with heart disease.
Hemoporfin-mediated photodynamic therapy with general anesthesia showed superior efficacy in the treatment of port-wine stains: a retrospective evaluation

Background Hemoporfin-mediated photodynamic therapy (PDT) is an effective treatment for port-wine stains (PWS), and pain is the main adverse effect of this therapy. General anesthesia is commonly used for pain management during PDT, but the effect of general anesthetics on the subsequent treatment efficacy of PDT in PWS has not been reported. Objectives To assess the use of general anesthesia combined with PDT compared with PDT alone in 207 PWS patients, and to provide further safety and efficacy data on this combined therapy. Methods Propensity score matching (PSM) was used at a 2:1 ratio to create a general anesthetic group (n = 138) and a highly comparable nonanesthetic group (n = 69). The clinical outcomes were evaluated, and the treatment reactions and adverse effects were recorded after one treatment with PDT. Results After matching, there was no significant difference in the demographic data of the patients in the two groups (p > 0.05), while the treatment efficacy was significantly higher in the general anesthetic group than in the nonanesthetic group (76.81 vs. 56.52%, p < 0.05). Moreover, logistic regression analysis confirmed that patients receiving general anesthesia showed an association with a good response to PDT (OR = 3.06; 95% CI, 1.57–6.00; p = 0.0011). Purpura lasted longer in the general anesthetic group, but the other treatment reactions and adverse effects were similar in the two groups (p > 0.05). No serious systemic adverse reactions were observed. Conclusion We recommend this combined therapy, which is associated with painless, as a high efficacy treatment option for PWS patients, especially for patients with a poor response to multiple PDT alone treatments.
Combined treatment with Sigma1R and A2AR agonists fails to inhibit cocaine self-administration despite causing strong antagonistic accumbal A2AR-D2R complex interactions: the potential role of astrocytes

Previous studies have indicated that acute treatment with the monoamine stabilizer OSU-6162 (5 mg/kg), which has a high affinity for Sigma1R, significantly increased the density of accumbal shell D2R-Sigma1R and A2AR-D2R heteroreceptor complexes following cocaine self-administration. Ex vivo studies using the A2AR agonist CGS21680 also suggested the existence of enhanced antagonistic accumbal A2AR-D2R allosteric interactions after treatment with OSU-6162 during cocaine self-administration. However, a 3-day treatment with OSU-6162 (5 mg/kg) failed to alter the behavioral effects of cocaine self-administration. To test these results and the relevance of OSU-6162 (2.5 mg/kg) and/or A2AR (0.05 mg/kg) agonist interactions, we administered low doses of receptor agonists during cocaine self-administration and assessed their neurochemical and behavioral effects. No effects were observed on cocaine self-administration; however, marked and highly significant increases using the proximity ligation assay (PLA) were induced by the co-treatment on the density of the A2AR-D2R heterocomplexes in the nucleus accumbens shell. Significant decreases in the affinity of the D2R high- and low-affinity agonist binding sites were also observed. Thus, in low doses, the highly significant neurochemical effects observed upon cotreatment with an A2AR agonist and a Sigma1R ligand on the A2AR-D2R heterocomplexes and their enhancement of allosteric inhibition of D2R high-affinity binding are not linked to the modulation of cocaine self-administration. The explanation may be related to an increased release of ATP and adenosine from astrocytes in the nucleus accumbens shell in cocaine self-administration. This can lead to increased activation of the A1R protomer in a putative A1R-A2AR-D2R complex that modulates glutamate release in the presynaptic glutamate synapse. We hypothesized that the integration of changes in presynaptic glutamate release and postjunctional heteroreceptor complex signaling, where D2R plays a key role, result in no changes in the firing of the GABA anti-reward neurons, resulting in no reduction in cocaine self-administration in the present experiments.
Microscopic polyangiitis initially presenting with idiopathic pulmonary fibrosis: a case report

Usual interstitial pneumonia is the most common type of microscopic polyangiitis (MPA)-associated interstitial lung disease, and patients may initially present with isolated pulmonary fibrosis, which often leads to a misdiagnosis of idiopathic pulmonary fibrosis (IPF). Here, we describe a patient who developed fever of unknown origin, microscopic hematuria and renal insufficiency, who then tested positive for antineutrophil cytoplasmic antibody (ANCA) and was diagnosed with MPA after receiving antifibrotic medication for IPF (original diagnosis) for almost 10 years. The patient's symptoms were ameliorated after administration of additional glucocorticoids and immunosuppressants.
Characteristics of changes in the functional status of the brain before and after 1,000 m all-out paddling for different levels of dragon boat athletes

Purposes Dragon boat is a traditional sport in China, but the brain function characteristics of dragon boat athletes are still unclear. Our purpose is to explore the changing characteristics of brain function of dragon boat athletes at different levels before and after exercise by monitoring the changes of EEG power spectrum and microstate of athletes before and after rowing. Methods Twenty-four expert dragon boat athletes and 25 novice dragon boat athletes were selected as test subjects to perform the 1,000 m all-out paddling exercise on a dragon boat dynamometer. Their resting EEG data was collected pre- and post-exercise, and the EEG data was pre-processed and then analyzed using power spectrum and microstate based on Matlab software. Results Post-Exercise, the Heart Rate peak (HR peak), Percentage of Heart Rate max (HR max), Rating of Perceived Exertion (RPE), and Exercise duration of the novice group were significantly higher than expert group (p < 0.01). Pre-exercise, the power spectral density values in the δ, α1, α2, and β1 bands were significantly higher in the expert group compared to the novice group (p < 0.05). Post-exercise, the power spectral density values in the δ, θ, and α1 bands were significantly lower in the expert group compared to the novice group (p < 0.05), the power spectral density values of α2, β1, and β2 bands were significantly higher (p < 0.05). The results of microstate analysis showed that the duration and contribution of microstate class D were significantly higher in the pre-exercise expert group compared to the novice group (p < 0.05), the transition probabilities of A → D, C → D, and D → A were significantly higher (p < 0.05). Post-exercise, the duration, and contribution of microstate class C in the expert group decreased significantly compared to the novice group (p < 0.05), the occurrence of microstate classes A and D were significantly higher (p < 0.05), the transition probability of A → B was significantly higher (p < 0.05), and the transition probabilities of C → D and D → C were significantly lower (p < 0.05). Conclusion The functional brain state of dragon boat athletes was characterized by expert athletes with closer synaptic connections of brain neurons and higher activation of the dorsal attention network in the resting state pre-exercise. There still had higher activation of cortical neurons after paddling exercise. Expert athletes can better adapt to acute full-speed oar training.
Comparative analysis of transcriptional changes in zebrafish cep290 and bbs2 mutants by RNA-seq reveals upregulation of inflammatory and stress-related pathways

Acute injury to the adult zebrafish retina triggers the release of pro-inflammatory cytokines and growth factors that stimulate multiple gene regulatory networks, which ultimately stimulate Müller glia to proliferate and regenerate neurons. In contrast, zebrafish carrying mutations in cep290 or bbs2 undergo progressive loss of cone photoreceptors and exhibit signs of microglia activation and inflammation, but the mutants fail to stimulate a regeneration response. To identify transcriptional changes that occur in zebrafish mutants undergoing progressive photoreceptor degeneration, RNA-seq transcriptional profiling was performed on cep290−/− and bbs2−/− retinas. The PANTHER Classification System was used to identify biological processes and signaling pathways that were differentially expressed between mutants and wild-type siblings during degeneration. As expected, genes associated with phototransduction were downregulated in cep290 and bbs2 mutants compared to wild-type siblings. Although both cep290 and bbs2 mutants undergo proliferation of rod precursors in response to retinal degeneration, the process of negatively regulating proliferation is enriched for upregulated genes, and this negative regulation may restrict proliferation of Müller glia and inhibit regeneration. A total of 815 differentially expressed genes (DEGs) were shared by cep290 and bbs2 retinas. Genes in pathways associated with inflammation, apoptosis, stress response, and PDGF signaling were overrepresented. Identifying the genes and biological pathways that are common in zebrafish models of inherited retinal degeneration provides a foundation for future studies on the mechanisms that regulate cell death as well as processes that prohibit Müller cell reprogramming or proliferation in a model capable of retinal regeneration. The pathways will provide targets for future interventions that may promote successful regeneration of lost photoreceptors.