Miracle-dz/UniFAR-FTUs huggingface dataset:
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research

Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves. The ultimate challenge of reinforcement learning research is to train real agents to operate in the real environment, but until now there has not been a common real-world RL benchmark. In this work, we present a prototype real-world environment from OffWorld Gym -- a collection of real-world environments for reinforcement learning in robotics with free public remote access. Close integration into existing ecosystem allows the community to start using OffWorld Gym without any prior experience in robotics and takes away the burden of managing a physical robotics system, abstracting it under a familiar API. We introduce a navigation task, where a robot has to reach a visual beacon on an uneven terrain using only the camera input and provide baseline results in both the real environment and the simulated replica. To start training, visit this https URL
Entanglement between light and microwave via electro-optic effect

We theoretically proposed one of the approaches achieving the quantum entanglement between light and microwave by means of electro-optic effect. Based on the established full quantum model of electro-optic interaction, the entanglement characteristics between light and microwave are studied by using the logarithmic negativity as a measure of the steady-state equilibrium operating point of the system. The investigation shows that the entanglement between light and microwave is a complicated function of multiple physical parameters, the parameters such as ambient temperature, optical detuning, microwave detuning and coupling coefficient have an important influence on the entanglement. When the system operates at narrow pulse widths and/or low repetition frequencies, it has obvious entanglement about 20 K, which is robust to the thermal environment.
Link-homotopy classes of 4-component links and claspers

Two links are link-homotopic if they are transformed into each other by a sequence of self-crossing changes and ambient isotopies. The link-homotopy classes of 4-component links were classified by Levine with enormous algebraic computations. We modify the results by using Habiro's clasper theory. The new classification gives more symmetrical and schematic points of view to the link-homotopy classes of 4-component links. We also gives some new subsets of the link-homotopy classes of 4-component links which are classified by invariants.
The Tessellation-Level-Tree: characterizing the nested hierarchy of density peaks and their spatial distribution in cosmological N-body simulations

We use the Millennium and Millennium-II simulations to illustrate the Tessellation-Level-Tree (tlt), a hierarchical tree structure linking density peaks in a field constructed by voronoi tessellation of the particles in a cosmological N-body simulation. The tlt uniquely partitions the simulation particles into disjoint subsets, each associated with a local density peak. Each peak is a subpeak of a unique higher peak. The tlt can be persistence filtered to suppress peaks produced by discreteness noise. Thresholding a peak’s particle list at $\sim 80\left \langle \rho \right \rangle \,$ results in a structure similar to a standard friend-of-friends halo and its subhaloes. For thresholds below $\sim 7\left \langle \rho \right \rangle \,$, the largest structure percolates and is much more massive than other objects. It may be considered as defining the cosmic web. For a threshold of $5\left \langle \rho \right \rangle \,$, it contains about half of all cosmic mass and occupies $\sim 1{{\ \rm per\ cent}}$ of all cosmic volume; a typical external point is then ∼7h−1 Mpc from the web. We investigate the internal structure and clustering of tlt peaks. Defining the saddle point density ρlim as the density at which a peak joins its parent peak, we show the median value of ρlim for FoF-like peaks to be similar to the density threshold at percolation. Assembly bias as a function of ρlim is stronger than for any known internal halo property. For peaks of group mass and below, the lowest quintile in ρlim has b ≈ 0, and is thus uncorrelated with the mass distribution.
Plutos Far Side.

The New Horizons spacecraft provided near global observations of Pluto that far exceed the resolution of Earth-based data sets. Most Pluto New Horizons analysis hitherto has focused on the encounter hemisphere of Pluto (i.e., the antiCharon hemisphere containing Sputnik Planitia). In this work, we summarize and interpret data on the far side (i.e., the non-encounter hemisphere), providing the first integrated New Horizons overview of the far side terrains. We find strong evidence for widespread bladed deposits, evidence for an impact crater about as large as any on the near side hemisphere, evidence for complex lineations approximately antipodal to Sputnik Planitia that may be causally related, and evidence that the far side maculae are smaller and more structured than the encounter hemisphere maculae.
The acute effects of moderate intensity exercise on vascular stiffness in children with repaired coarctation of the aorta

Some individuals with coarctation of the aorta (CoA) have impaired cardiac and vascular structure and function, despite surgical intervention. Exercise training has been shown to improve cardiovascular structure and function in individuals with acquired heart disease; however, little information exists to inform exercise prescription in individuals with congenital heart abnormalities. Nine children with CoA (7 males, age: 12 ± 3 y) and 9 age and sex matched controls (7 males, age: 11 ± 3 y) completed a 20 minute bout of leg and arm cycling on two separate days at workloads which elicited exercising heart rates which were 50% above resting heart rate. Central arterial stiffness was measured using both carotid distensibility and pulse wave velocity (PWV) before exercise and at 5 and 15 minutes post exercise. At the same time points, upper limb and lower limb arterial stiffness were assessed using PWV. At rest prior to exercise, the CoA group had larger carotid lumen diameters in comparison to controls (p<0.05). Carotid distensibility was not different between the groups at any time point (p>0.05) and did not change in response to either arm or leg exercise (p>0.05). Central PWV was elevated at 5 minutes post arm cycling exercise (main effect for time, p<0.05) but was not different between groups (p>0.05). Central PWV was reduced at 5 and 15 minutes post leg cycling exercise in CoA and did not change in the controls. Non-exercised limb stiffness did not change with arm or leg exercise in either group (p>0.05). Stiffness decreased in the exercised limb following arm exercise (p<0.05) but not leg exercise (p>0.05). There was a significant group difference for slower PWV in the upper limb with arm and leg exercise in repaired CoA group in comparison to controls (p<0.05). Our findings indicate
CSID: Center, Scale, Identity and Density-aware Pedestrian Detection in a Crowd

Pedestrian detection in a crowd is very challenging due to vastly different scales and poor conditions. Pedestrian detectors are generally designed by extending generic object detectors, where Non-maximum suppression (NMS) is a standard but critical post-processing step for reﬁning detection results. In this paper, we propose CSID: a Center, Scale, Identity-and-Density-aware pedestrian detector with a novel Identity-and-Density-aware NMS (ID-NMS) algo-rithm to reﬁne the results of anchor-free pedestrian detection. Our main contributions in this work include (i) a novel Identity and Density Map (ID-Map) which converts each positive instance into a feature vector to encode both identity and density information simultaneously, (ii) a modiﬁed optimization target in deﬁning ID-loss and addressing the extremely class imbalance issue during training, and (iii) a novel ID-NMS algorithm by considering both identity and density information of each predicted box provided by ID-Map to effectively reﬁne the detection results. We evaluate the proposed CSID pedestrian detector using the novel ID-NMS technique, and achieve new state-of-the-art results on two benchmark data sets (CityPersons and CrowdHuman) for pedestrian detection.
Fingerprints of the Protosolar Cloud Collapse in the Solar System. II. Nucleosynthetic Anomalies in Meteorites

The isotopic heterogeneity of the solar system shown by meteorite analyses is more pronounced for its earliest objects, the calcium–aluminum-rich inclusions (CAIs). This suggests that it was inherited from spatial variations in stardust populations in the protosolar cloud. We model the formation of the solar protoplanetary disk following its collapse and find that the solid-weighted standard deviation of different nucleosynthetic contributions in the disk is reduced by one order of magnitude compared to the protosolar cloud, whose successive isotopic signatures are fossilized by CAIs. The enrichment of carbonaceous chondrites in r-process components, whose proportions are inferred to have diminished near the end of infall, is consistent with their formation at large heliocentric distances, where the early signatures would have been preferentially preserved after outward advection. We also argue that thermal processing had little effect on the (mass-independent) isotopic composition of bulk meteorites for refractory elements.
Alias Sampling Effect on the Calculation of MHD Mode Number in Fusion Plasma

Detailed alias frequency formula and the effect of alias sampling on the calculation of MHD mode number are derived. It is discovered that the absolute MHD mode number/structure does not change under alias sampling. This discovery can help us to determine the structure of the high frequency MHD mode with low frequency sampled diagnostics even when the Nyquist-Shannon sampling theorem is no longer valid.
LinesToFacePhoto: Face Photo Generation From Lines With Conditional Self-Attention Generative Adversarial Networks

In this paper, we explore the task of generating photo-realistic face images from lines. Previous methods based on conditional generative adversarial networks (cGANs) have shown their power to generate visually plausible images when a conditional image and an output image share well-aligned structures. However, these models fail to synthesize face images with a whole set of well-defined structures, e.g. eyes, noses, mouths, etc., especially when the conditional line map lacks one or several parts. To address this problem, we propose a conditional self-attention generative adversarial network (CSAGAN). We introduce a conditional self-attention mechanism to cGANs to capture long-range dependencies between different regions in faces. We also build a multi-scale discriminator. The large-scale discriminator enforces the completeness of global structures and the small-scale discriminator encourages fine details, thereby enhancing the realism of generated face images. We evaluate the proposed model on the CelebA-HD dataset by two perceptual user studies and three quantitative metrics. The experiment results demonstrate that our method generates high-quality facial images while preserving facial structures. Our results outperform state-of-the-art methods both quantitatively and qualitatively.
Empirical Process of Multivariate Gaussian under General Dependence

This paper explores certain kinds of empirical process with respect to the components of multivariate Gaussian. We put forward some finite sample bounds which hold for multivariate Gaussian under general dependence. We give necessary and sufficient condition for the convergence in probability of the random variable sequence $\displaystyle\left\{\sup_t\vert\widehat{F}_n(t)-\mathbf{E}\widehat{F}_n(t)\vert\right\}_{n\in \mathbb{N}}$, where $\widehat{F}_n(t)$ is the empirical distribution. Also, we find a similar sufficient condition for almost surely convergence.
A New Connection Between Node and Edge Depth Robust Graphs

We create a graph reduction that transforms an $(e, d)$-edge-depth-robust graph with $m$ edges into a $(e/4,d)$-depth-robust graph with $O(m)$ nodes and constant indegree. An $(e,d)$-depth robust graph is a directed, acyclic graph with the property that that after removing any $e$ nodes of the graph there remains a path with length at least $d$. Similarly, an $(e, d)$-edge-depth robust graph is a directed, acyclic graph with the property that after removing any $e$ edges of the graph there remains a path with length at least $d$. Our reduction relies on constructing graphs with a property we define and analyze called ST-Robustness. We say that a directed, acyclic graph with $n$ inputs and $n$ outputs is $(k_1, k_2)$-ST-Robust if we can remove any $k_1$ nodes and there exists a subgraph containing at least $k_2$ inputs and $k_2$ outputs such that each of the $k_2$ inputs is connected to all of the $k_2$ outputs. We use our reduction on a well known edge-depth-robust graph to construct an $(\frac{n \log \log n}{\log n}, \frac{n}{\log n (\log n)^{\log \log n}})$-depth-robust graph.
Theoretical study of deeply virtual Compton scattering off $^4$He

An interesting breakthrough in understanding the elusive inner content of nuclear systems in terms of partonic degrees of freedom is represented by deeply virtual Compton scattering processes. In such a way, tomographic view of nuclei and bound nucleons in coordinate space could be achieved for the first time. Moreover, nowadays experimental results for such a process considering ^44He targets recently released at Jefferson Lab are available. In this talk, the recent results of our rigorous Impulse Approximation for DVCS off ^44He, in terms of state-of-the-art models of the nuclear spectral function and of the parton structure of the bound proton, able to explain present data, has been shown.
Image Restoration Using Deep Regulated Convolutional Networks

While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest. The width of networks, defined as the size of the receptive fields and the density of the channels, has demonstrated crucial importance in low-level vision tasks such as image denoising and restoration. However, the limited generalization ability, due to the increased width of networks, creates a bottleneck in designing wider networks. In this paper, we propose the Deep Regulated Convolutional Network (RC-Net), a deep network composed of regulated sub-network blocks cascaded by skip-connections, to overcome this bottleneck. Specifically, the Regulated Convolution block (RC-block), featured by a combination of large and small convolution filters, balances the effectiveness of prominent feature extraction and the generalization ability of the network. RC-Nets have several compelling advantages: they embrace diversified features through large-small filter combinations, alleviate the hazy boundary and blurred details in image denoising and super-resolution problems, and stabilize the learning process. Our proposed RC-Nets outperform state-of-the-art approaches with significant performance gains in various image restoration tasks while demonstrating promising generalization ability. The code is available at this https URL.
Redistribution Mechanism Design on Networks

Redistribution mechanisms have been proposed for more efficient resource allocation but not for profit. We consider redistribution mechanism design for the first time in a setting where participants are connected and the resource owner is only aware of her neighbours. In this setting, to make the resource allocation more efficient, the resource owner has to inform the others who are not her neighbours, but her neighbours do not want more participants to compete with them. Hence, the goal is to design a redistribution mechanism such that participants are incentivized to invite more participants and the resource owner does not earn or lose much money from the allocation. We first show that existing redistribution mechanisms cannot be directly applied in the network setting to achieve the goal. Then we propose a novel network-based redistribution mechanism such that all participants in the network are invited, the allocation is more efficient and the resource owner has no deficit.
Analytical models of Energy and Throughput for Caches in MPSoCs

General trends in computer architecture are shifting more towards parallelism. Multicore architectures have proven to be a major step in processor evolution. With the advancement in multicore architecture, researchers are focusing on finding different solutions to fully utilize the power of multiple cores. With an ever-increasing number of cores on a chip, the role of cache memory has become pivotal. An ideal memory configuration should be both large and fast, however, in fact, system architects have to strike a balance between the size and access time of the memory hierarchy. It is important to know the impact of a particular cache configuration on the throughput and energy consumption of the system at design time. This paper presents an enhanced version of previously proposed cache energy and throughput models for multicore systems. These models use significantly a smaller number of input parameters as compared to other models. This paper also validates the proposed models through cycle accurate simulator and a renowned processor power estimator. The results show that the proposed energy models provide accuracy within a maximum error range of 10% for single-core processors and around 5% for MPSoCs, and the throughput models result in a maximum error of up to 11.5% for both single and multicore architectures.
Distortion of magnetic fields in Barnard 68

The magnetic field structure, kinematical stability, and evolutionary status of the starless dense core Barnard 68 (B68) are revealed based on the near-infrared polarimetric observations of background stars, measuring the dichroically polarized light produced by aligned dust grains in the core. After subtracting unrelated ambient polarization components, the magnetic fields pervading B68 are mapped using 38 stars and axisymmetrically distorted hourglass-like magnetic fields are obtained, although the evidence for the hourglass field is not very strong. On the basis of simple 2D and 3D magnetic field modeling, the magnetic inclination angles on the plane-of-sky and in the line-of-sight direction are determined to be $47^{\circ} \pm 5^{\circ}$ and $20^{\circ} \pm 10^{\circ}$, respectively. The total magnetic field strength of B68 is obtained to be $26.1 \pm 8.7$ $\mu {\rm G}$. The critical mass of B68, evaluated using both magnetic and thermal/turbulent support, is $M_{\rm cr} = 2.30 \pm 0.20$ ${\rm M}_{\odot}$, which is consistent with the observed core mass of $M_{\rm core}=2.1$ M$_{\odot}$, suggesting nearly critical state. We found a relatively linear relationship between polarization and extinction up to $A_V \sim 30$ mag toward the stars with deepest obscuration. Further theoretical and observational studies are required to explain the dust alignment in cold and dense regions in the core.
Benchmark Dataset for Timetable optimization of Bus Routes in the City of New Delhi

Public transport is one of the major forms of transportation in the world. This makes it vital to ensure that public transport is efficient. This research presents a novel realtime GPS bus transit data for over 500 routes of buses operating in New Delhi. The data can be used for modeling various timetable optimization tasks as well as in other domains such as traffic management and travel time estimation. The paper also presents an approach to reduce the waiting time of Delhi buses by analyzing the traffic behavior and proposing a timetable. This algorithm serves as a benchmark for the dataset. The algorithm uses a constrained clustering algorithm for classification of trips. It further analyses the data statistically to provide a timetable that is efficient in learning the inter- and intra-month variations.
Opinion Shaping in Social Networks Using Reinforcement Learning

In this article, we consider a variant of the classical DeGroot model of opinion propagation with random interactions, in which a prescribed subset of agents is amenable to a control parameter. There are also some stubborn agents and some agents that are neither stubborn nor amenable to control. We map the problem to a shortest path problem, where the control parameter is coupled across controlled nodes because of a common resource constraint. Hence, the problem is not amenable to a pure dynamic programming approach, and the classical reinforcement learning schemes for the latter cannot be applied here for maximizing average influence in the long run. We view it instead as a parametric optimization problem and not a control problem and use a nonclassical policy gradient scheme. We analyze its performance theoretically and through numerical experiments. We also consider a situation when only certain interactions between agents are observed.
Propagation of coherent spin waves in individual nano-sized yttrium iron garnet magnonic conduits

Modern-days CMOS-based computation technology is reaching fundamental limitations which restrain further progress towards faster and more energy efficient devices. A promising path to overcome these limitations is the emerging field of magnonics which utilizes spin waves for data transport and computation operations. Many different devices have already been demonstrated on the macro- and microscale. However, the feasibility of this technology essentially relies on the scalability to the nanoscale and a proof that coherent spin waves can propagate in these structures. Here, we present a study of the spin-wave dynamics in individual yttrium iron garnet (YIG) magnonic conduits with lateral dimensions down to 50 nm. Space and time resolved micro-focused Brillouin-Light-Scattering (BLS) spectroscopy is used to extract the exchange constant and directly measure the spin-wave decay length and group velocity. Thereby, the first experimental proof of propagating spin waves in individual nano-sized YIG conduits and the fundamental feasibility of a nano-scaled magnonics are demonstrated.