Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

For autonomous vehicles integrating onto road-ways with human traffic participants, it requires understanding and adapting to the participants’ intention by responding in predictable ways. This paper proposes a reinforcement learning based negotiation-aware motion planning framework, which adopts RL to adjust the driving style of the planner by dynamically modifying the prediction horizon length of the motion planner in real time adaptively. The framework models the interaction between the autonomous vehicle and other traffic participants as a Markov Decision Process. A temporal sequence of occupancy grid maps are taken as inputs for RL module to embed an implicit intention reasoning. Curriculum learning is employed to enhance the training efficiency and the robustness of the algorithm. We applied our method to narrow lane navigation in both simulation and real world to demonstrate that the proposed method outperforms the common alternative due to its advantage in alleviating the social dilemma problem with proper negotiation skills.
Crowd Counting Via Perspective-Guided Fractional-Dilation Convolution

Crowd counting is critical for numerous video surveillance scenarios. One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect. To address this issue, this paper proposes a novel convolution neural network-based crowd counting method, termed Perspective-guided Fractional-Dilation Network (PFDNet). By modeling the continuous scale variations, the proposed PFDNet is able to select the proper fractional-dilation kernels for adapting to different spatial locations. It significantly improves the flexibility of the state-of-the-arts that only consider the discrete representative scales. In addition, by avoiding the multi-scale or multi-column architecture that used in other methods, it is computationally more efficient. In practice, the proposed PFDNet is constructed by stacking multiple Perspective-guided Fractional-Dilation Convolutions (PFC) on a VGG16-BN backbone. By introducing a novel generalized dilation convolution operation, the PFC can handle fractional dilation ratios in the spatial domain under the guidance of perspective annotations, achieving continuous scales modeling of pedestrians. To deal with the problem of unavailable perspective information in some cases, we further introduce an effective perspective estimation branch to the proposed PFDNet, which can be trained in either supervised or weakly-supervised setting once the branch has been pre-trained. Extensive experiments show that the proposed PFDNet outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, WorldExpo’10, UCF-QNRF, UCF_CC_50 and TRANCOS dataset, achieving MAE 53.8, 6.5, 6.8, 84.3205. 8, and 3.06 respectively.
Spacetime Hall-MHD Turbulence at Sub-ion Scales: Structures or Waves?

Spatiotemporal properties of two-dimensional (2D) Hall-magnetohydrodynamic (MHD) turbulence at intermediate plasma β = 2 are studied by means of Fast Iterative Filtering (FIF), a new technique for the decomposition of nonstationary nonlinear signals. Results show that the magnetic energy at sub-ion scales is concentrated in perturbations with frequencies smaller than the ion-cyclotron (IC) frequency and with polarization properties that are incompatible with both kinetic Alfvén waves (KAWs) and IC waves. At higher frequencies, we clearly identify signatures of both whistler waves and KAWs; however, their energetic contribution to the magnetic power spectrum is negligible. We conclude that the dynamics of 2D Hall-MHD turbulence at sub-ion scales is mainly driven by localized intermittent structures, with no significant contribution of wavelike fluctuations.
Imitation by Predicting Observations

Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to observe behavior in the real-world, the underlying actions may not be accessible. We present a new method for imitation solely from observations that achieves comparable performance to experts on challenging continuous control tasks while also exhibiting robustness in the presence of observations unrelated to the task. Our method, which we call FORM (for"Future Observation Reward Model") is derived from an inverse RL objective and imitates using a model of expert behavior learned by generative modelling of the expert's observations, without needing ground truth actions. We show that FORM performs comparably to a strong baseline IRL method (GAIL) on the DeepMind Control Suite benchmark, while outperforming GAIL in the presence of task-irrelevant features.
EUROPEAN LONGITUDE PRIZES. 4: THOMAS AXE'S IMPOSSIBLE TERMS

Although governments across Europe had realised the need to incentivise the development of practically viable longitude solutions as early as the late-sixteenth century, the English government was late to the party. An sense of urgency among the scientific community and maritime navigators led to the establishment of a number of longitude awards by private donors. The first private British award was bequeathed in 1691 by Thomas Axe, parish clerk of Ottery St. Mary (Devon). Despite the absence of an expenses component and the onerous and costly nature of its terms and conditions, the Axe prize attracted a number of optimistic claimants. Although the award was never disbursed, it may have contributed to the instigation of the government-supported monetary reward associated with the British Longitude Act of 1714. It is likely that the conditions governing the British"Longitude Prize", specifically the required accuracy and the need for sea trials and of disclosure of a successful method's theoretical principles, can be traced back at least in part to the Axe Prize requirements.
Quantum belief function

The belief function in Dempster Shafer evidence theory can express more information than the traditional Bayesian distribution. It is widely used in approximate reasoning, decision-making and information fusion. However, its power exponential explosion characteristics leads to the extremely high computational complexity when handling large amounts of elements in classic computers. In order to solve the problem, we encode the basic belief assignment (BBA) into quantum states, which makes each qubit correspond to control an element. Besides the high eﬃciency, this quantum expression is very conducive to measure the similarity between two BBAs, and the measuring quantum algorithm we come up with has exponential acceleration theoretically compared to the corresponding classical algorithm. In addition, we simulate our quantum version of BBA on Qiskit platform, which ensures the rationality of our algorithm experimentally. We believe our results will shed some light on utilizing the characteristic of quantum computation to handle belief function more conveniently.
Self-organized criticality in neural networks

We demonstrate, both analytically and numerically, that learning dynamics of neural networks is generically attracted towards a self-organized critical state. The effect can be modeled with quartic interactions between non-trainable variables (e.g. states of neurons) and trainable variables (e.g. weight matrix). Non-trainable variables are rapidly driven towards stochastic equilibrium and trainable variables are slowly driven towards learning equilibrium described by a scale-invariant distribution on a wide range of scales. Our results suggest that the scale invariance observed in many physical and biological systems might be due to some kind of learning dynamics and support the claim that the universe might be a neural network.
Management of Resource at the Network Edge for Federated Learning

Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge. Some of the problems of this management, such as discovery of resources, deployment, load balancing, migration, and energy efficiency will be discussed in the paper.
Probabilistic Trace Alignment

Alignments provide sophisticated diagnostics that pinpoint deviations in a trace with respect to a process model. Alignment-based approaches for conformance checking have so far used crisp process models as a reference. Recent probabilistic conformance checking approaches check the degree of conformance of an event log as a whole with respect to a stochastic process model, without providing alignments. For the first time, we introduce a conformance checking approach based on trace alignments using stochastic Workflow nets. This requires to handle the two possibly contrasting forces of the cost of the alignment on the one hand and the likelihood of the model trace with respect to which the alignment is computed on the other.
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review

A state-of-the-art systematic review on XAI applied to Prognostic and Health Management (PHM) of industrial asset is presented. This work provides an overview of the general trend of XAI in PHM, answers the question of accuracy versus explainability, the extent of human involvement, the explanation assessment and uncertainty quantification in PHM-XAI domain. Research articles associated with the subject, from 2015 to 2021 were selected from five known databases following PRISMA guidelines. Data was then extracted from the selected articles and examined. Several findings were synthesized. Firstly, while the discipline is still young, the analysis indicated the growing acceptance of XAI in PHM domain. Secondly, XAI functions as a double edge sword, where it is assimilated as a tool to execute PHM tasks as well as a mean of explanation, particularly in diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Thirdly, the review showed that PHM-XAI papers produce either good or excellent result in general, suggesting that PHM performance is unaffected by XAI. Fourthly, human role, evaluation metrics and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater for PHM need are urgently needed.Finally, most case study featured on the accepted articles are based on real, industrial data, indicating that the available PHM-XAI blends are fit to solve complex,real-world challenges, increasing the confidence in AI adoption in the industry.
Halo cluster shapes: insights from simulated galaxies and ICL with prospects for weak lensing applications

We present a detailed study of the shapes and alignments of different galaxy cluster components using hydrodynamical simulations. We compute shape parameters from the dark matter (DM) distribution, the galaxy members and the intracluster light (ICL). We assess how well the DM cluster shape can be constrained by means of the identified galaxy member positions and the ICL. Further, we address the dilution factor introduced when estimating the cluster elongation using weak-lensing stacking techniques, which arises due to the misalignment between the total surface mass distribution and the distribution of luminous tracers. The dilution is computed considering the alignment between the DM and the brightest cluster galaxy, the galaxy members and the ICL. Our study shows that distributions of galaxy members and ICL are less spherical than the DM component, although both are well aligned with the semimajor axis of the latter. We find that the distribution of galaxy members hosted in more concentrated subhaloes is more elongated than the distribution of the DM. Moreover, these galaxies are better aligned with the DM component compared to the distribution of galaxies hosted in less concentrated subhaloes. We conclude that the positions of galaxy members can be used as suitable tracers to estimate the cluster surface density orientation, even when a low number of members is considered. Our results provide useful information for interpreting the constraints on the shapes of galaxy clusters in observational studies.
GitQ- Towards Using Badges as Visual Cues for GitHub Projects

GitHub hosts millions of software repositories, facilitating developers to contribute to many projects in multiple ways. Most of the information about the repositories is text-based in the form of stars, forks, commits, and so on. However, developers willing to contribute to projects on GitHub often find it challenging to select appropriate projects to contribute to or reuse due to the large number of repositories present on GitHub. Further, obtaining this required information often becomes a tedious process, as one has to carefully mine information hidden inside the repository. To alleviate the effort intensive mining procedures, researchers have proposed npmbadges to outline information relating to build status of a project. However, these badges are static and limit their usage to package dependency and build details. Adding visual cues such as badges, to the repositories might reduce the search space for developers. Hence, we present GitQ, to auto-matically augment GitHub repositories with badges representing information about source code and project maintenance. Presenting GitQ as a browser plugin to GitHub could make it easily accessible to developers using GitHub. GitQ is evaluated with 15 developers based on the UTAUT model to understand developer perception towards its usefulness. We observed that 11 out of 15 developers perceived GitQ to be useful in identifying the right set of reposi-tories using visual cues such as generated by GitQ. The source code and tool are available for download on GitHub at https://github.com/gitq-for-github/plugin, and the demo can be found at https://youtu.be/c0yohmIat3A.
TGHop: an explainable, efficient, and lightweight method for texture generation

An explainable, efficient, and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, and computationally expensive in training. In contrast, TGHop is small in its model size, mathematically transparent, efficient in training and inference, and able to generate high-quality texture. Given an exemplary texture, TGHop first crops many sample patches out of it to form a collection of sample patches called the source. Then, it analyzes pixel statistics of samples from the source and obtains a sequence of fine-to-coarse subspaces for these patches by using the PixelHop++ framework. To generate texture patches with TGHop, we begin with the coarsest subspace, which is called the core, and attempt to generate samples in each subspace by following the distribution of real samples. Finally, texture patches are stitched to form texture images of a large size. It is demonstrated by experimental results that TGHop can generate texture images of superior quality with a small model size and at a fast speed.
Laser structured micro-targets generate MeV electron temperature at $4 \times 10^{16}$ W/cm$^2$

Angana Mondal1,2,†, Ratul Sabui, Sheroy Tata, R.M. G.M Trines, S.V. Rahul, Feiyu Li, Soubhik Sarkar, William Trickey, Rakesh Y. Kumar, Debobrata Rajak, John Pasley, Zhengming Sheng, J. Jha, M. Anand, Ram Gopal, A.P.L. Robinson and M.Krishnamurthy1,2∗ Tata Institute of Fundamental Research,Homi Bhabha Road, Navy Nagar, Colaba, Mumbai 400005, INDIA Tata Institute of Fundamental Research, Hyderabad, Gopanpally, Serlingampalli, Telangana, INDIA, 500107 †Currently at ETH Zurich, 8093, Switzerland. Central Laser Facility, Rutherford Appleton Laboratory, Chilton, Didcot OX10 0QX, UK Department of Physics, University of Strathclyde, Glasgow G4 0NG, UK York Plasma Institute, Department of Physics, University of York, York, YO10 5DD, United Kingdom Laboratory for Laser Plasmas and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China and Laboratory for Laser Plasmas and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China (Dated: July 9, 2021)
J ul 2 02 1 A NEW CLASS OF THE ENTIRE FUNCTIONS : A STUDY OF TWO CASES FROM EULER

In this article an alternative infinite product for a special class of the entire functions are studied by using some results of the Laguerre-Pólya entire functions. The zeros for a class of the special even entire functions are discussed in detail. It is proved that the infinite product and series representations for the hyperbolic and trigonometric cosine functions, which are coming from Euler, are our special cases.
BumbleBee: A Transformer for Music

We will introduce BumbleBee, a transformer model that will generate MIDI music data . We will tackle the issue of transformers applied to long sequences by implementing a longformer generative model that uses dilating sliding windows to compute the attention layers. We will compare our results to that of the music transformer and Long-Short term memory (LSTM) to benchmark our results. This analysis will be performed using piano MIDI files, in particular , the JSB Chorales dataset that has already been used for other research works (Huang et al., 2018)
An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild

In this work we tackle the task of video-based audio-visual emotion recognition, within the premises of the 2nd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW2). Poor illumination conditions, head/body orientation and low image resolution constitute factors that can potentially hinder performance in case of methodologies that solely rely on the extraction and analysis of facial features. In order to alleviate this problem, we leverage both bodily and contextual features, as part of a broader emotion recognition framework. We choose to use a standard CNN-RNN cascade as the backbone of our proposed model for sequence-to-sequence (seq2seq) learning. Apart from learning through the RGB input modality, we construct an aural stream which operates on sequences of extracted mel-spectrograms. Our extensive experiments on the challenging and newly assembled Aff-Wild2 dataset verify the validity of our intuitive multi-stream and multi-modal approach towards emotion recognition "in-the-wild". Emphasis is being laid on the the beneficial influence of the human body and scene context, as aspects of the emotion recognition process that have been left relatively un-explored up to this point. All the code was implemented using PyTorch 1 and is publicly available 2.
Toward a systematic cartography of the chemical stratification inside white dwarfs from deep asteroseismic probing of ZZ Ceti stars

DA-type white dwarfs account for 80% of all white dwarfs and represent, for most of them, the ultimate outcome of the typical evolution of low-to-intermediate mass stars. Their internal chemical stratification is strongly marked by passed, often uncertain, stellar evolution processes that occurred during the helium (core and shell) burning phases, i.e., from the horizontal branch through AGB and post-AGB stages. Pulsating white dwarfs, in particular the"cool"DA-type ZZ Ceti variables, offer an outstanding opportunity to dig into these stars by fully exploiting their asteroseismic potential. With our most recent tools dedicated to that purpose, we show that a complete cartography of the stratification of the main constituents of a white dwarf can be inferred, leading in particular to strong constraints on the C/O core structure produced by the processes mentioned above. This opens up the way toward a systematic exploration of white-dwarf internal properties.
The GOGREEN Survey: Evidence of an Excess of Quiescent Disks in Clusters at 1.0 < z < 1.4

We present the results of the measured shapes of 832 galaxies in 11 galaxy clusters at 1.0 < z < 1.4 from the GOGREEN survey. We measure the axis ratio (q), the ratio of the minor to the major axis, of the cluster galaxies from near-infrared Hubble Space Telescope imaging using Sérsic profile fitting and compare them with a field sample. We find that the median q of both star-forming and quiescent galaxies in clusters increases with stellar mass, similar to the field. Comparing the axis ratio distributions between clusters and the field in four mass bins, the distributions for star-forming galaxies in clusters are consistent with those in the field. Conversely, the distributions for quiescent galaxies in the two environments are distinct, most remarkably in 10.1≤log(M/M⊙)<10.5 where clusters show a flatter distribution, with an excess at low q. Modelling the distribution with oblate and triaxial components, we find that the cluster and field sample difference is consistent with an excess of flattened oblate quiescent galaxies in clusters. The oblate population contribution drops at high masses, resulting in a narrower q distribution in the massive population than at lower masses. Using a simple accretion model, we show that the observed q distributions and quenched fractions are consistent with a scenario where no morphological transformation occurs for the environmentally quenched population in the two intermediate-mass bins. Our results suggest that environmental quenching mechanism(s) likely produce a population that has a different morphological mix than those resulting from the dominant quenching mechanism in the field.
Single Pole-To-Earth Fault Detection and Location on the Tehran Railway System Using ICA and PSO Trained Neural Network

In a railroad feeding system, detecting a location of pole to earth faults is important for safe operation of the system. The goal of this paper is to use a combination of the evolutionary algorithm and neural networks to increase the accuracy of single pole-to-earth fault detection and location on Tehran railroad power supply system. Accordingly, Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network for enhancing learning process accuracy and the convergence. Owing to the nonlinearity of system, the fault detection is an ideal application for the proposed method where 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer and the silicon rectifier has been developed by typical Tehran metro parameters. Required data for the network learning the process have been gathered from simulation results. 600Hz components value will change with the change of the location of single pole to earth fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the fault location can be accurately predicted in proposed methods. Keyword: Single pole-to-pole fault, Tehran Railway, ICA, PSO, Artificial Neural Network.