Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Exploration Policies with View-based Intrinsic Rewards

Efficient exploration in sparse-reward tasks is one of the biggest challenges in deep reinforcement learning. Common approaches introduce intrinsic rewards to motivate exploration. For example, visitation count and prediction-based curiosity utilize some measures of novelty to drive the agent to visit novel states in the environment. However, in partially-observable environments, these methods can easily be misled by relatively “novel” or noisy observations and get stuck around them. Motivated by humans’ exploration behavior of seeing around the environment to get information and avoid unnecessary actions, we consider enlarging the agent’s view area for efficient knowledge acquisition of the environment. In this work, we propose a novel intrinsic reward combining two components: the view-based bonus for ample view coverage and the classical count-based bonus for novel observation discovery. The resulting method, ViewX, achieves state-of-the-art performance on the 12 most challenging procedurally-generated tasks on MiniGrid. Additionally, ViewX efficiently learns an exploration policy in the task-agnostic setting, which generalizes well to unseen environments. When exploring new environments on MiniGrid and Habitat, our learned policy significantly outperforms the baselines in terms of scene coverage and extrinsic reward.
Eccentric Optimization of Multisensor for SLAM-Integrated Navigation

This article models, first, the eccentric installation of multiple sensors and inertial measurement unit (IMU), both dynamically and physically, based on the simultaneous localization and mapping (SLAM)-integrated navigation in the near-ground environment. Furthermore, the relationship between the estimated acceleration of other sensor locations under the eccentric motion and the actual measurement value of the accelerometer has been analyzed. Most SLAM-integrated navigation fusion algorithms incorporating IMU only account for the transformation between the sensor coordinate systems, but avoid the Coriolis, tangential, and centrifugal forces caused by the eccentric rotation and the sensor installed at different locations. This problem affects the accuracy of the sensor acceleration estimated at different locations and thus affects the accuracy of the SLAM system preintegration and the overall positioning accuracy of SLAM. Second, the eccentric correction optimization and preintegration optimization algorithm have been implemented. Finally, a number of simulation experiments have been carried out to verify the optimal effect of the algorithm. The experimental results show that the positioning accuracy of the overall SLAM navigation algorithm can be effectively improved by eccentric correction optimization, and the root mean squared error (RMSE) of the optimized SLAM navigation algorithms has been reduced in varying degrees in different paths and algorithms. This study will help optimize the positioning accuracy of the SLAM- and IMU-integrated navigation scheme in the future.
Ultimate Strength of Continuous Stiffened Aluminium Plates Under Combined Biaxial Compression and Lateral Pressure

Ultimate compressive strength of welded stiffened aluminium plates under combined biaxial in-plane compression and different levels of lateral pressure is assessed herein. A numerical database of the ultimate strengths for stiffened aluminium plates is generated at first. Then, regression analysis is applied in order to derive the empirical formulations as functions of two parameters, namely the plate slenderness ratio and the column (stiffener) slenderness ratio. The formulae implicitly include the effects of initial imperfections and heat affected zone.
2 2 M ay 2 00 8 Disorder effects in the quantum kagome antiferromagnet ZnCu

Motivated by the recent NMR experiments on ZnCu3(OH)6Cl2, we study the effect of nonmagnetic defects on the antiferromagnetic spin1 2 kagome lattice. We use exact diagonalization methods to study the effect of two such defects on finite size systems. Our results, obtained without adjustable parameters, are in good quantitative agreement with recent O NMR data. They provide support for the experimental interpretation of the presence of defects within the kagome layers due to Zn/Cu substitutions. Our results also show that disorder effects become relevant at lower temperatures, raising questions about the experimental evidence for the absence of an intrinsic spin gap in the kagome 2D layers.
Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing

Deep learning models are increasingly being used in detecting patterns and correlations in medical imaging data such as magnetic resonance imaging. However, conventional methods are incapable of considering the real underlying causal relationships. In the presence of confounders, spurious correlations between data, imaging process, content, and output can occur that allow the network to learn shortcuts instead of the desired causal relationship. This effect is even more prominent in new environments or when using out-of-distribution data since the learning process is primarily focused on correlations and patterns within the data. Hence, wrong conclusions or false diagnoses can be obtained from such confounded models. In this paper, we propose a novel framework, denoted as Mutual Information Minimization Model (MIMM), that predicts the desired causal outcome while simultaneously reducing the influence of present spurious correlations. The input imaging data is encoded into a feature vector that is split into two components to predict the primary task and the presumed spuriously correlated factor separately. We hypothesize that learned mutual information between both feature vector components can be reduced to achieve independence, i.e., confounder-free task prediction. The proposed approach is investigated on five databases: two non-medical benchmark databases (Morpho-MNIST and Fashion-MNIST) to verify the hypothesis and three medical databases (German National Cohort, UK Biobank, and ADNI). The results show that our proposed framework serves as a solution to address the limitations of conventional deep learning models in medical image analysis. By explicitly considering and minimizing spurious correlations, it learns causal relationships which result in more accurate and reliable predictions. The novel contributions in this work are: 1) the separation of features into the prediction of the primary task and the spuriously correlated factor; 2) MIMM targets the preservation of invariance to counterfactuals, prevents shortcut learning, and enables confounder-free network training; and 3) the mutual information minimization addresses heterogeneous data cohorts as usually encountered in the medical domain.
Using Generated LIBS Data as a Base for Neural Network Architecture Development

Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid atomic spectroscopy technique used to measure the concentration of elements in samples. This research paper uses supervised deep learning with a LIBS database provided by National Institute of Standards and Technology (NIST) to find out what kind of neural network architecture can best predict element concentrations from metal alloy samples. Accuracy of predictions was used to evaluate the network architectures. Because collecting and storing real samples of metal alloys with a wide range of element concentrations requires a lot of resources, the network’s ability to predict outside of training set’s range of concentrations was also tested. The goal of the research was to find out if NIST database together with machine learning can be used to reduce the amount of real alloy samples needed for LIBS calibration.
A Framework for the Synthesis of X-Ray Security Inspection Images Based on Generative Adversarial Networks

Object detection of prohibited items in X-ray security inspection is challenging because of serious overlap, disorderly background, and high throughput. In the past few years, a variety of deep learning algorithms have been proposed and achieved satisfactory results. However, the performance of these algorithms relies heavily on the specified datasets. Moreover, establishing a large-scale X-ray image dataset by manually collecting and labeling images is prohibitively expensive and time consuming. In this paper, we propose a text-driven framework for synthesizing X-ray security inspection images based on Generative Adversarial Networks (GAN). First, a conditional GAN is developed to generate natural images of prohibited items from class labels. Second, an improved model based on a pix-to-pix GAN is implemented to convert natural images into X-ray images. Third, another HD pix-to-pixel GAN is responsible for producing high-resolution benign background images, which are subsequently fused with the generated images of prohibited items to create X-ray inspection images. Finally, the proposed method is evaluated using SOTA object detection algorithms, such as YOLO-v5, and achieving 4.6% promotion for $mAP_{0.5}$ and 15.9% promotion for $mAP_{0.5-0.95}$ . The experimental results demonstrate that our image synthesis framework can effectively augment the datasets of prohibited items and improve the detection performance of deep learning algorithms during X-ray security screening.
Improving Functional Coverage of Network-On-Chip Using Differential Evolution

Network-On-Chip (NoC) is a communication subsystem which has evolved as an alternative to bus-based System-On-Chip (SoC) architectures that simplify the physical implementation of the system. The functional verification of the NoC is a very important phase in the design life-cycle of a NoC-based SoC as it impacts the SoC’s behavior and latency. Automating the NoC functional verification process significantly reduce the time to market for such complex SoC designs. The use of differential evolution algorithm to create NoC traffic which obtains maximum functional coverage in less simulation time is one such method of automation.
Auto-generated Plans and Decisions as an Instrument of Crisis Management

Auto-generated plans and decisions are generated without human input. Machine learning algorithms or other automated decision-making systems develop them based on present criteria and rules. An automated system might create a reaction plan for a natural disaster or cyberattack. The system may assess historical occurrences, discover patterns and trends, and create a reaction plan based on specified parameters. An organisation may employ an automated decision-making technology to choose resources or actions in a crisis. The tool may evaluate data and make a judgement automatically using specified rules and criteria. In urgent circumstances, auto-generated strategies and choices may help. They may also help businesses react consistently to emergencies using objective criteria and data analysis, decreasing human error. This work aims to provide an overview of crisis management and the role of auto-generated plans and decisions in its deployment. The case study of one company is presented to illustrate the usage of auto-generated plans and decisions, taking care that they shouldn’t replace human decision-making. To be current and successful, they should be evaluated and updated often.
Physiological and Socio-Behavioral Determinants of Viral Video User Engagement

The use of social media platforms like TikTok, Facebook, and YouTube to consume videos has seen significant growth in recent years. Some videos can generate high levels of engagement - views, likes, shares, or comments at a rapid pace, making them “viral. ” However, understanding what causes these videos to become viral or predicting virality is still largely a mystery. This study aimed to uncover if biometrics-based emotion and arousal data, such as facial muscle and skin conductance data, could predict user engagement and contribute to video virality. The experiment used 64 participants, who watched 13 videos and had their facial expressions and galvanic skin response (GSR) data recorded throughout the viewing experience. The study then used an XGBoost classifier and 42 collected features, including physiological data and socio-behavioral responses, to predict user engagement (willingness to like, share, or comment) with over 80% accuracy. The results indicate that a combination of facial expression, GSR, and socio-behavioral data can accurately distinguish between high and low user engagement without needing to ask viewers anything about the videos or analyze video content. This study elevates the role of viewers’ physiological and subconscious responses to video content across the viewing experience.
The Fermi Galactic Center GeV Excess and Implications for Dark Matter

The region around the Galactic Center (GC) is now well established to be brighter at energies of a few GeV than what is expected from conventional models of diffuse gamma-ray emission and catalogs of known gamma-ray sources. We study the GeV excess using 6.5 yr of data from the Fermi Large Area Telescope. We characterize the uncertainty of the GC excess spectrum and morphology due to uncertainties in cosmic-ray source distributions and propagation, uncertainties in the distribution of interstellar gas in the Milky Way, and uncertainties due to a potential contribution from the Fermi bubbles. We also evaluate uncertainties in the excess properties due to resolved point sources of gamma rays. The GC is of particular interest, as it would be expected to have the brightest signal from annihilation of weakly interacting massive dark matter (DM) particles. However, control regions along the Galactic plane, where a DM signal is not expected, show excesses of similar amplitude relative to the local background. Based on the magnitude of the systematic uncertainties, we conservatively report upper limits for the annihilation cross-section as a function of particle mass and annihilation channel.
Sliding Mode Control of a Four-Leg Dynamic Voltage Restorer in a Natural Reference Frame

This article focuses on designing a sliding mode control (SMC) for a dynamic voltage restorer (DVR) using a four-leg voltage source converter and its output filters in the natural reference frame. The coupling of filter-capacitor voltages through the control inputs of the DVR system leads to cross-coupling in the dynamics of conventional sliding variables with respect to four control input variables, making the design of SMC using Lyapunov stability criteria difficult. To overcome this, a new sliding variable for each converter leg is proposed, which is a function of the respective filter-capacitor voltage and a fictitious voltage that depends on the converter neutral-point voltage (NPV). This allows control of both the compensator voltages and the converter NPV using the proposed scheme. This article also presents the calculation of the optimum sliding coefficient to maximize the sliding existence region for a four-leg DVR. The performance of the proposed control scheme is experimentally validated under various operating conditions using a laboratory prototype of a four-leg DVR.
Synthetic Beam Scanning and Super-Resolution Coincidence Imaging Based on Randomly Excited Antenna Array

Nowadays, there is no good scheme for simultaneously achieving super-resolution imaging within a coherent beamwidth and beam scanning in microwave radars. In this article, a synthetic beam scanning method based on a randomly excited antenna array, which possesses the ability of super-resolution coincidence imaging, is proposed. The beamwidth of the randomly excited array is comparable to that of the conventional phased array with the same size, and its steering angle can be adjusted by modulating the excitation signals. First, the optimized covariance matrix of the proposed array to uniformly bunch the spatial radiation energy in a specific angle range is obtained by means of the sequential quadratic programming (SQP) algorithm. Accordingly, the partially correlated excitation signals restricted by the optimized covariance matrix are determined. Then, a secondary weighted modulation method is proposed to steer the main beam to the direction of interest. The excitation signals are weighted by the conjugated radiation fields in the direction of interest. Furthermore, the super-resolution ability of the proposed coincidence imaging system is analyzed based on the first-order statistical characteristics of the radiation fields. Simulations and experiments demonstrate that synthetic beam scanning can be realized by controlling the excitation signals. Besides, a ten times super-resolution coincidence image can be reconstructed within a coherent beamwidth (3-dB beamwidth) when the signal-to-noise ratio (SNR) is greater than 15 dB, assisted by the fast Bayesian learning (FBL) algorithm.
All-trans Retinoic Acid Inhibits Hepatitis B Virus Replication by Downregulating HBx Levels via Siah-1-Mediated Proteasomal Degradation

All-trans retinoic acid (ATRA), the most biologically active metabolite of vitamin A, is known to abolish the potential of HBx to downregulate the levels of p14, p16, and p21 and to stimulate cell growth during hepatitis B virus (HBV) infection, contributing to its chemopreventive and therapeutic effects against HBV-associated hepatocellular carcinoma. Here, we demonstrated that ATRA antagonizes HBx to inhibit HBV replication. For this effect, ATRA individually or in combination with HBx upregulated p53 levels, resulting in upregulation of seven in absentia homolog 1 (Siah-1) levels. Siah-1, an E3 ligase, induces ubiquitination and proteasomal degradation of HBx in the presence of ATRA. The ability of ATRA to induce Siah-1-mediated HBx degradation and the subsequent inhibition of HBV replication was proven in an in vitro HBV replication model. The effects of ATRA became invalid when either p53 or Siah-1 was knocked down by a specific shRNA, providing direct evidence for the role of p53 and Siah-1 in the negative regulation of HBV replication by ATRA.
Graphical modeling for dynamic safety hints generalisation for Safe Deep Reinforcement Learning Agents

A major challenge for Deep Reinforcement Learning (DRL) research is to maintain robustness and safety, which is highly important in real-life applications.There is a need to explore AI models/algorithms to utilise system dynamics and their associated reward distribution (assessing risk/opportunities trade-off) to develop algorithms for data-driven Markov Decision Process formulation and safe action sets. Causality modelling is a well-established logical technique which can help identify the effects of interactions of complex evolving systems involving several entities. In this paper, we propose using knowledge graphs which help DRL agents incorporate safety aspects of actions and entities in their decision making process. We dynamically generate safety hints for DRL agents in a text based game environment us-ing a safety concept-net. Our experiments show that DRL agents with safety-hints perform better on safety-based games than agents without them.
INSTRUCTION-FINETUNED FOUNDATION MODELS FOR MULTIMODAL WEB NAVIGATION

We propose an instruction-aligned multimodal agent for autonomous web navigation – i.e., sequential decision making tasks employing a computer interface. Our approach is based on supervised finetuning of vision and language foundation models on a large corpus of web data consisting of webpage screenshots and HTML. Specifically, we use vision transformers on sequences of web page screenshots to extract patch-level image features. These features are concatenated with embedding of tokens in HTML documents. Using an instruction-finetuned large language model, we jointly encode both vision and HTML modalities and decode web actions such as click and type. We show that our method outperforms previous approaches by a significant margin, even in handling out-of-distribution HTML and compositional tasks. On the MiniWoB benchmark, we improve previous approaches using only HTML input by more than 17.7%, even surpassing the performance of RL-finetuned models. On the recent WebShop benchmark, our 3-billion-parameter model achieves superior performance to the existing state-ofthe-art PaLM-540B. We also collect 347K gold demonstrations using our trained models, 29 times larger than prior work, and make them available to promote future research in this area. We believe that our work is a step towards building capable and generalist decision making agents for computer interface.
Pembrolizumab-Induced Fatal Myasthenia, Myocarditis, and Myositis in a Patient with Metastatic Melanoma: Autopsy, Histological, and Immunohistochemical Findings—A Case Report and Literature Review

Immune checkpoint inhibitors (ICIs) represent a major advance in cancer treatment. The lowered immune tolerance induced by ICIs brought to light a series of immune-related adverse events (irAEs). Pembrolizumab belongs to the ICI class and is a humanized IgG4 anti-PD-1 antibody that blocks the interaction between PD-1 and PD-L1. The ICI-related irAEs involving various organ systems and myocarditis are uncommon (incidence of 0.04% to 1.14%), but they are associated with a high reported mortality. Unlike idiopathic inflammatory myositis, ICI-related myositis has been reported to frequently co-occur with myocarditis. The triad of myasthenia, myositis, and myocarditis must not be underestimated as they can rapidly deteriorate, leading to death. Herein we report a case of a patient with metastatic melanoma who fatally developed myasthenia gravis, myocarditis, and myositis, after a single cycle of pembrolizumab. Considering evidence from the literature review, autopsy, histological, and immunohistochemical investigations on heart and skeletal muscle are presented and discussed, also from a medical–legal perspective.
Soft Robotic End-Effectors in the Wild: A Case Study of a Soft Manipulator for Green Bell Pepper Harvesting

In this case study we present a proof of concept design of a soft robotic end-effector for green pepper harvesting. We test our robotic system in a standard double-row cropping system under real-world conditions without making modifications to the pepper plants or the environment. We show that soft manipulators can reliably grasp peppers without bruising the fruit and provide an in-depth analysis of common types of failures we observe. Finally, we discuss future improvements to our robotic system.
A Novel Ex Vivo Model to Study Therapeutic Treatments for Myelin Repair following Ischemic Damage

Stroke is a major reason for persistent disability due to insufficient treatment strategies beyond reperfusion, leading to oligodendrocyte death and axon demyelination, persistent inflammation and astrogliosis in peri-infarct areas. After injury, oligodendroglial precursor cells (OPCs) have been shown to compensate for myelin loss and prevent axonal loss through the replacement of lost oligodendrocytes, an inefficient process leaving axons chronically demyelinated. Phenotypic screening approaches in demyelinating paradigms revealed substances that promote myelin repair. We established an ex vivo adult organotypic coronal slice culture (OCSC) system to study repair after stroke in a resource-efficient way. Post-photothrombotic OCSCs can be manipulated for 8 d by exposure to pharmacologically active substances testing remyelination activity. OCSCs were isolated from a NG2-CreERT2-td-Tomato knock-in transgenic mouse line to analyze oligodendroglial fate/differentiation and kinetics. Parbendazole boosted differentiation of NG2+ cells and stabilized oligodendroglial fate reflected by altered expression of associated markers PDGFR-α, CC1, BCAS1 and Sox10 and GFAP. In vitro scratch assay and chemical ischemia confirmed the observed effects upon parbendazole treatment. Adult OCSCs represent a fast, reproducible, and quantifiable model to study OPC differentiation competence after stroke. Pharmacological stimulation by means of parbendazole promoted OPC differentiation.