Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems

Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly complex dynamics, which makes their full-scale simulation computationally slow. In this paper, we show how to decompose large networked systems of many agents into multiple local components such that we can build separate simulators that run independently and in parallel. To monitor the influence that the different local components exert on one another, each of these simulators is equipped with a learned model that is periodically trained on real trajectories. Our empirical results reveal that distributing the simulation among different processes not only makes it possible to train large multi-agent systems in just a few hours but also helps mitigate the negative effects of simultaneous learning.
Polarized Color Image Denoising using Pocoformer

Polarized color photography provides both visual textures and object surficial information in one single snapshot. However, the use of the directional polarizing filter array causes extremely lower photon count and SNR compared to conventional color imaging. Thus, the feature essentially leads to unpleasant noisy images and destroys polarization analysis performance. It is a challenge for traditional image processing pipelines owing to the fact that the physical constraints exerted implicitly in the channels are excessively complicated. To address this issue, we propose a learning-based approach to simultaneously restore clean signals and precise polarization information. A real-world polarized color image dataset of paired raw short-exposed noisy and long-exposed reference images are captured to support the learning-based pipeline. Moreover, we embrace the development of vision Transformer and propose a hybrid transformer model for the Polarized Color image denoising, namely PoCoformer, for a better restoration performance. Abundant experiments demonstrate the effectiveness of proposed method and key factors that affect results are analyzed.
Thirty Milliseconds in the Life of a Supercooled Liquid

We combine the swap Monte Carlo algorithm to long multi-CPU molecular dynamics simulations to analyse the equilibrium relaxation dynamics of model supercooled liquids over a time window covering ten orders of magnitude for temperatures down to the experimental glass transition temperature $T_g$. The analysis of \rev{several} time correlation functions coupled to spatio-temporal resolution of particle motion allow us to elucidate the nature of the equilibrium dynamics in deeply supercooled liquids. We find that structural relaxation starts at early times in rare localised regions characterised by a waiting time distribution that develops a power law near $T_g$. At longer times, relaxation events accumulate with increasing probability in these regions as $T_g$ is approached. This accumulation leads to a power-law growth of the linear extension of relaxed domains with time with a large, temperature-dependent dynamic exponent. Past the average relaxation time, unrelaxed domains slowly shrink with time due to relaxation events happening at their boundaries. Our results provide a complete microscopic description of the particle motion responsible for key experimental signatures of glassy dynamics, from the shape and temperature evolution of relaxation spectra to the core features of dynamic heterogeneity. They also provide a microscopic basis to understand the emergence of dynamic facilitation in deeply supercooled liquids and allow us to critically reassess theoretical descriptions of the glass transition.
Deep Learning and Symbolic Regression for Discovering Parametric Equations

Symbolic regression is a machine learning technique that can learn the equations governing data and thus has the potential to transform scientific discovery. However, symbolic regression is still limited in the complexity and dimensionality of the systems that it can analyze. Deep learning, on the other hand, has transformed machine learning in its ability to analyze extremely complex and high-dimensional datasets. We propose a neural network architecture to extend symbolic regression to parametric systems where some coefficient may vary, but the structure of the underlying governing equation remains constant. We demonstrate our method on various analytic expressions and partial differential equations (PDEs) with varying coefficients and show that it extrapolates well outside of the training domain. The proposed neural-network-based architecture can also be enhanced by integrating with other deep learning architectures such that it can analyze high-dimensional data while being trained end-to-end. To this end, we demonstrate the scalability of our architecture by incorporating a convolutional encoder to analyze 1-D images of varying spring systems.
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.
EE-TCAM: An Energy-Efficient SRAM-Based TCAM on FPGA

Ternary content-addressable memories (TCAMs) are used to design high-speed search engines. TCAM is implemented on application-specific integrated circuit (native TCAMs) and field-programmable gate array (FPGA) (static random-access memory (SRAM)-based TCAMs) platforms but both have the drawback of high power consumption. This paper presents a pre-classifier-based architecture for an energy-efficient SRAM-based TCAM. The first classification stage divides the TCAM table into several sub-tables of balanced size. The second SRAM-based implementation stage maps each of the resultant TCAM sub-tables to a separate row of configured SRAM blocks in the architecture. The proposed architecture selectively activates at most one row of SRAM blocks for each incoming TCAM word. Compared with the existing SRAM-based TCAM designs on FPGAs, the proposed design consumes significantly reduced energy as it activates a part of SRAM memory used for lookup rather than the entire SRAM memory as in the previous schemes. We implemented the proposed approach sample designs of size 512 × 36 on Xilinx Virtex-6 FPGA. The experimental results showed that the proposed design achieved at least three times lower power consumption per performance than other SRAM-based TCAM architectures.
A Study on the Resonator Plating Method of Ceramic Resonator Filter

5th generation telecommunication is expected to be served with Internet of Things (IoT). And those services would cause the architecture of mobile networks. Consequently the distribution of base station should be changed toward dense deployment. In that environment the part for the base station should be altered totally considering the size and output. This paper deals with the method to reduce the filter that would be used in the small base station and introduces the optimization method for conductive coating of the ceramic resonator
100 years of mathematical cosmology: Models, theories and problems, Part B

We continue our overview of mathematical cosmology with a survey of the third and fourth periods of the development of the subject. The first Part includes the first two periods and is published separately. The third period (1980–2000) continues here with brief descriptions of the main ideas of inflation, the multiverse, quantum, Kaluza–Klein, and string cosmologies, wormholes and baby universes, cosmological stability and modified gravity. The last period, which ends today, includes various more advanced topics such as M-theoretic cosmology, braneworlds, the landscape, topological issues, the measure problem, genericity, dynamical singularities and dark energy. We emphasize certain threads that run throughout the whole period of development of theoretical cosmology and underline their importance in the overall structure of the field. We end this outline with an inclusion of the abstracts of all papers contributed to the second part of the Philosophical Transactions of the Royal Society A, theme issue ‘The future of mathematical cosmology’. This article is part of the theme issue ‘The future of mathematical cosmology, Volume 2’.
Adaptive Driving Assistant Model (ADAM) for Advising Drivers of Autonomous Vehicles

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.
Industrial Control Systems Security via Runtime Enforcement

With the advent of Industry 4.0, industrial facilities and critical infrastructures are transforming into an ecosystem of heterogeneous physical and cyber components, such as programmable logic controllers, increasingly interconnected and therefore exposed to cyber-physical attacks, i.e., security breaches in cyberspace that may adversely affect the physical processes underlying industrial control systems. In this article, we propose a formal approach based on runtime enforcement to ensure specification compliance in networks of controllers, possibly compromised by colluding malware that may locally tamper with actuator commands, sensor readings, and inter-controller communications. Our approach relies on an ad-hoc sub-class of Ligatti et al.’s edit automata to enforce controllers represented in Hennessy and Regan’s Timed Process Language. We define a synthesis algorithm that, given an alphabet 𝒫 of observable actions and a timed correctness property e, returns a monitor that enforces the property e during the execution of any (potentially corrupted) controller with alphabet 𝒫, and complying with the property e. Our monitors do mitigation by correcting and suppressing incorrect actions of corrupted controllers and by generating actions in full autonomy when the controller under scrutiny is not able to do so in a correct manner. Besides classical requirements, such as transparency and soundness, the proposed enforcement enjoys deadlock- and diverge-freedom of monitored controllers, together with scalability when dealing with networks of controllers. Finally, we test the proposed enforcement mechanism on a non-trivial case study, taken from the context of industrial water treatment systems, in which the controllers are injected with different malware with different malicious goals.
A very simple analog control for QSW-ZVS source/sink buck converter with seamless mode transition

A simple, analog, control circuit is proposed for seamless transition between source and sink modes in a Quasi-Square-Wave Zero Voltage Switching (QSW-ZVS) source/sink buck converter. The inductor current is controlled by a variable-width hysteretic current mode control. The upper and lower bounds of the hysteretic band are clamped to ensure QSW-ZVS operation with a single current command from the control loop and independently from the power flow direction. This enables the control of any PWM converter able to operate in QSW-ZVS with a single control loop. If the proposed circuit is used, using a complex multi-mode or look-up-table based digital control is no longer required, simplifying the implementation and lowering the cost of the converter. A 50 W buck converter is built to demonstrate the proposed control circuit and experimental measurements are shown to verify its correct operation.
Performance Limitation Analysis of Highly-Digital Time-Based Closed-Loop Sensor-to-Digital Converter Architectures

This brief presents the theoretical and comparative analysis of two major time-based architectures for sensor interfaces. Both use a voltage-controlled oscillator (VCO) to achieve a highly-digital scalable implementation. The first architecture is based on a phase-locked loop, while the second one is count-based. Both systems are closed loop to efficiently mitigate the VCO nonlinearity. They show inherent first-order quantization noise shaping thanks to the use of an oscillator and phase detection. The two systems having a different working principle leads to different VCO requirements in terms of gain linearity (V-to-f or V-to-T linearity). Formulas are derived to predict the maximum SQNR for both architectures. Equations for the achievable maximum SNR taking into account the VCO phase noise are also derived, since this is the limit to the SNR in practical implementations. State-variable-based simulation results are presented, confirming the theoretical analysis and emphasizing the different design trade-offs and practical considerations.
Oligofructose restores postprandial short‐chain fatty acid levels during high‐fat feeding

Obesity is associated with consumption of a Western diet low in dietary fiber, while prebiotics reduce body weight. Fiber induces short‐chain fatty acid (SCFA) production, and SCFA administration is beneficial to host metabolic homeostasis. However, the role of endogenous SCFA signaling in the development of obesity is contentious. Therefore, the primary objective of this study is to evaluate the postprandial time course of SCFA production and uptake in healthy (chow‐fed), Western diet‐fed (high‐fat diet [HFD]) obese, and oligofructose‐treated HFD‐fed (HFD + OFS) rats.
mcera5: Driving microclimate models with ERA5 global gridded climate data

Microclimate models predict temperature and other meteorological variables at scales relevant to individual organisms. The broad application of microclimate models requires gridded macroclimatic variables as input. However, the spatial and temporal resolution of such inputs can be a limiting factor on the accuracy of microclimate predictions. Due to its fine resolution and accuracy, the ERA5 reanalysis dataset is emerging as the favoured resource for global historical weather and climate data and has great potential for aiding microclimate modelling. Here we describe mcera5, an R language package that provides convenient access to, and wrangling of, the ERA5 climate datasets for use in microclimate models. Through this package, we provide functions to query ERA5 data for desired spatial and temporal extents, to correct for spatial biases and process outputs for easy interpretation by ecologists, thereby allowing faster and more accurate microclimate predictions. By validating with empirical observations from multiple biomes globally, we demonstrate that the use of ERA5 climate forcing via mcera5 improves the prediction accuracy of soil moisture, air temperature and relative humidity as compared to forcing with other globally available data and offers comparable performance when predicting soil temperatures. Through the provision of fine‐resolution ERA5 data, the mcera5 package fits into an ecosystem of tools for modelling microclimate in a spatio‐temporally explicit fashion, advancing our ability to efficiently predict microclimate for any place on Earth for the past, present or future. The package also provides convenient access to ERA5 datasets for a range of other applications.
Concrete crack segmentation based on convolution–deconvolution feature fusion with holistically nested networks

Automatic crack detection on concrete surfaces has become increasingly important for the health diagnosis of concrete structures to prevent possible malfunctions or accidents. In this paper, a concrete crack segmentation network based on convolution–deconvolution feature fusion with holistically nested networks is proposed. The proposed network adopts an encoder–decoder structure and uses VGG‐16 as the basic feature extraction network. First, considering the problem that the VGG‐16 network can extract redundant features in the encoding stage, based on the channel attention mechanism, the channel spatial correlation and global information are used to emphasize crack features to remove redundant features. Second, through the convolution–deconvolution feature fusion module, the deep semantic information of the deconvolution is effectively fused with the shallow features of convolution, which effectively improves the semantic crack feature information extracted at each stage of the VGG‐16 network. Finally, based on a multiscale supervised learning mechanism, holistically nested networks are used to fuse the prediction results from different scales, which enhances the network's ability to express linear topological structures and improves the accuracy of crack segmentation. Through a large number of experiments on the Bridge_Crack_Image_Data dataset and CFD dataset, we demonstrate that compared with other deep networks, the proposed network not only achieves better segmentation results for cracks of different widths but is also more robust.
Nutrition for precision health: The time is now

Precision nutrition has emerged as a boiling area of nutrition research, with a particular focus on revealing the individual variability in response to diets that is determined mainly by the complex interactions of dietary factors with the multi‐tiered “omics” makeups. Reproducible findings from the observational studies and diet intervention trials have lent preliminary but consistent evidence to support the fundamental role of gene–diet interactions in determining the individual variability in health outcomes including obesity and weight loss. Recent investigations suggest that the abundance and diversity of the gut microbiome may also modify the dietary effects; however, considerable instability in the results from the microbiome research has been noted. In addition, growing studies suggest that a complicated multiomics algorithm would be developed by incorporating the genome, epigenome, metabolome, proteome, and microbiome in predicting the individual variability in response to diets. Moreover, precision nutrition would also scrutinize the role of biological (circadian) rhythm in determining the individual variability of dietary effects. The evidence gathered from precision nutrition research will be the basis for constructing precision health dietary recommendations, which hold great promise to help individuals and their health care providers create precise and effective diet plans for precision health in the future.
The magnocellular division of the human medial geniculate nucleus preferentially responds to auditory transients

The medial geniculate nucleus (MGN), the auditory relay in the thalamus, is composed of three anatomical subdivisions: the ventral, dorsal and medial or magnocellular division. The functional differences among these nuclei have not been studied in humans, and in particular, the function of the magnocellular division is poorly understood in mammals in general. We anatomically segmented the MGN using proton-density-weighted magnetic resonance images (MRI) and measured the functional responses of the MGN to sustained and transient sounds, using functional MRI (fMRI). We observed that voxels in the ventromedial portion of the MGN, corresponding to the magnocellular division, exhibited a strong preference to transient sounds, whereas the remainder of the MGN showed no preference between sustained and transient sounds. We concluded that the magnocellular neurons in the MGN parallel the magnocellular neurons in its visual counterpart, the lateral geniculate nucleus (LGN), and constitute an information stream specialized for encoding stimuli dynamics. Significance statement The medial geniculate nucleus is the auditory relay in the thalamus. It is composed of three anatomical divisions, of which the function of the magnocellular division is poorly understood. We show, using functional magnetic resonance imaging (fMRI) in humans, that the magnocellular neurons are strongly activated by transient auditory stimuli, similar to the magnocellular neurons in the lateral geniculate nucleus, the visual thalamic relay, which are rapidly adapting and specialized to encode visual transients. These results confirm that the auditory system represents stimuli using parallel information streams, employing similar encoding strategies as in other sensory modalities.
Proteome-wide microarray-based screening of PAR-binding proteins

Poly(ADP-ribose) (PAR) plays a crucial role in intracellular signaling and scaffolding through covalent modification or non-covalent binding to target proteins. The non- covalent binding PARylome has not been extensively characterized. Here we performed a PAR-binding screen using a human protein microarray that covers most of the human proteome to characterize the non-covalent binding PARylome. A total of 356 PAR- binding proteins were identified. The PAR-binding PARylome suggests that PAR- binding regulates a variety of biological processes beyond well-characterized DNA damage signaling and DNA repair. Proteins that may be reprogrammed by PAR-binding include signaling molecules, transcription factors, nucleic acid binding proteins, calcium binding proteins, ligases, oxidoreductases, enzymes, transferases, hydrolases, and receptors. The global database of PAR-binding proteins that we established will be a valuable tool for further in-depth analysis of the role of PARylation in a wide range of biological contexts.