Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters

The freedom of fast iterations of distributed deep learning tasks is crucial for smaller companies to gain competitive advantages and market shares from big tech giants. HorovodRunner brings this process to relatively accessible spark clusters. There have been, however, no benchmark tests on HorovodRunner per se, nor specifically graph convolutional network (GCN, hereafter), and very limited scalability benchmark tests on Horovod, the predecessor requiring custom built GPU clusters. For the first time, we show that Databricks' HorovodRunner achieves significant lift in scaling efficiency for the convolutional neural network (CNN, hereafter) based tasks on both GPU and CPU clusters, but not the original GCN task. We also implemented the Rectified Adam optimizer for the first time in HorovodRunner.
PSDet: Efficient and Universal Parking Slot Detection

While real-time parking slot detection plays a critical role in valet parking systems, existing methods have limited success in real-world application. We argue two reasons accounting for the unsatisfactory performance: i, The available datasets have limited diversity, which causes the low generalization ability. ii, Expert knowledge for parking slot detection is under-estimated. Thus, we annotate a large-scale benchmark for training the network and release it for the benefit of community. Driven by the observation of various parking lots in our benchmark, we propose the circular descriptor to regress the coordinates of parking slot vertexes and accordingly localize slots accurately. To further boost the performance, we develop a two-stage deep architecture to localize vertexes in the coarse-to-fine manner. In our benchmark and other datasets, it achieves the state-of-the-art accuracy while being real-time in practice. Benchmark is available at: https://github.com/wuzzh/Parking-slot-dataset
Human-Robot Collaboration in Microgravity: The Object Handover Problem

Collaborative space robots are an emerging research field with high impact as robots facilitate servicing functions in collaboration with astronauts with higher precision during lengthy tasks, on a tight schedule, with less risk and without requiring life support, making them more efficient and economically more viable. However, human-robot collaboration in space is still a challenge concerning key issues such as manipulation of weightless objects and mobility due to the peculiar motion dynamics of the robot and the manipulator on a microgravity environment. Therefore, this paper formulates and validates an algorithm that enables a free-flyer robot equipped with a manipulator to perform a successful, fluent, and dynamic robot-to-human and human-to-robot object handover. Furthermore, a virtual reality user interaction interface is developed using a Leap Motion device and a systematic user study is conducted. This study aims to analyse the user's preferences between a rigid and compliant impedance robot behavior during the object handover and to understand the impact of those behaviors on the success of the task. The results showed that the rigid behavior was overall more preferable and registered higher transfer success during the tasks.
A high-energy neutrino coincident with a tidal disruption event

Cosmic neutrinos provide a unique window into the otherwise-hidden mechanism of particle acceleration in astrophysical objects. A flux of high-energy neutrinos was discovered in 2013, and the IceCube Collaboration recently associated one high-energy neutrino with a flare from the relativistic jet of an active galaxy pointed towards the Earth. However a combined analysis of many similar active galaxies revealed no excess from the broader population, leaving the vast majority of the cosmic neutrino flux unexplained. Here we present the association of a radio-emitting tidal disruption event (AT2019dsg) with another high-energy neutrino, identified as part of our systematic search for optical counterparts to high-energy neutrinos with the Zwicky Transient Facility (ZTF). The probability of finding any radio-emitting tidal disruption event by chance is 0.5%, while the probability of finding one as bright in bolometric energy flux as AT2019dsg is 0.2%. Our electromagnetic observations can be explained through a multi-zone model, with radio analysis revealing a central engine, embedded in a UV photosphere, that powers an extended synchrotron-emitting outflow. This provides an ideal site for PeV neutrino production. The association suggests that tidal disruption events contribute to the cosmic neutrino flux. Unlike previous work which considered the rare subset of tidal disruption events with relativistic jets, our observations of AT2019dsg suggest an empirical model with a mildly-relativistic outflow.
Nice formulas, $xyx$-formulas, and palindrome patterns

We characterize the formulas that are avoided by every $\alpha$-free word for some $\alpha>1$. We study the avoidability index of formulas whose fragments are of the form $XYX$. The largest avoidability index of an avoidable palindrome pattern is known to be at least $4$ and at most $16$. We make progress toward the conjecture that every avoidable palindrome pattern is $4$-avoidable.
High-Scalability CMOS Quantum Magnetometer With Spin-State Excitation and Detection of Diamond Color Centers

Magnetometers based on quantum mechanical processes enable high sensitivity and long-term stability without the need for re-calibration, but their integration into fieldable devices remains challenging. This article presents a CMOS quantum vector-field magnetometer that miniaturizes the conventional quantum sensing platforms using nitrogen-vacancy (NV) centers in diamond. By integrating key components for spin control and readout, the chip performs magnetometry through optically detected magnetic resonance (ODMR) through a diamond slab attached to a custom CMOS chip. The ODMR control is highly uniform across the NV centers in the diamond, which is enabled by a CMOS-generated ~2.87 GHz magnetic field with < 5% inhomogeneity across a large-area current-driven wire array. The magnetometer chip is 1.5 mm<sup>2</sup> in size, prototyped in 65-nm bulk CMOS technology, and attached to a 300 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 80 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}^{2}$ </tex-math></inline-formula> diamond slab. NV fluorescence is measured by CMOS-integrated photodetectors. This ON-chip measurement is enabled by efficient rejection of the green pump light from the red fluorescence through a CMOS-integrated spectral filter based on a combination of spectrally dependent plasmonic losses and diffractive filtering in the CMOS back-end-of-line (BEOL). This filter achieves a measured ~25 dB of green light rejection. We measure a sensitivity of 245 nT/Hz<sup>1/2</sup>, marking a 130<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvement over a previous CMOS-NV sensor prototype, largely thanks to the better spectral filtering and homogeneous microwave generation over larger area.
Quantum Annealing-Based Software Components: An Experimental Case Study with SAT Solving

Quantum computers have the potential of solving problems more efficiently than classical computers. While first commercial prototypes have become available, the performance of such machines in practical application is still subject to exploration. Quantum computers will not entirely replace classical machines, but serve as accelerators for specific problems. This necessitates integrating quantum computational primitives into existing applications. In this paper, we perform a case study on how to augment existing software with quantum computational primitives for the Boolean satisfiability problem (SAT) implemented using a quantum annealer (QA). We discuss relevant quality measures for quantum components, and show that mathematically equivalent, but structurally different ways of transforming SAT to a QA can lead to substantial differences regarding these qualities. We argue that engineers need to be aware that (and which) details, although they may be less relevant in traditional software engineering, require considerable attention in quantum computing.
FISHERIES MANAGED TO REBUILD ECOSYSTEMS? RECONSTRUCTING THE PAST TO SALVAGE THE FUTURE

This paper presents the case for adopting ecosystem rebuilding as the goal of fisheries management. Movement toward this goal may represent the only hope for fisheries, as we know them, to exist 50 years in the future alongside essential services provided by marine ecosystems. First, I review archaeological, historical, and recent evidence that bears witness to a long, dismal record of overexploitation. Second, I examine the ecological effects of overfishing on aquatic ecosystems. Fish with life histories and spatial behavior inimical to harvesting are selectively removed, both within and among species. The loss of keystone species and the replacement of high-value, demersal resources with pelagic, rapid-turnover, low-value species shifts the nature of ecosystems, evidenced by accelerating local extinctions and a worldwide decline in trophic level. Disconcertingly, harvest limits that appear safe by single species evaluation can engender ecosystem changes that are hard to reverse. Driven by a progression .
Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)

In this study, we reported our exploration of Text-To-Speech without Text (TTS without T) in the Zero Resource Speech Challenge 2020, in which participants proposed an end-to-end, unsupervised system that learned speech recognition and TTS together. We addressed the challenge using biologically/psychologically motivated modules of Artificial Neural Networks (ANN), with a particular interest in unsupervised learning of human language as a biological/psychological problem. The system first processes Mel Frequency Cepstral Coefficient (MFCC) frames with an Echo-State Network (ESN), and simulates computations in cortical microcircuits. The outcome is discretized by our original Variational Autoencoder (VAE) that implements the Dirichlet-based Bayesian clustering widely accepted in computational linguistics and cognitive science. The discretized signal is then reverted into sound waveform via a neural-network implementation of the source-filter model for speech production.
The n-point streaming model: how velocities shape correlation functions in redshift space

Starting from first principles, we derive the fundamental equations that relate the n-point correlation functions in real and redshift space. Our result generalises the so-called `streaming model' to higher-order statistics: the full n-point correlation in redshift-space is obtained as an integral of its real-space counterpart times the joint probability density of n−1 relative line-of-sight peculiar velocities. Equations for the connected n-point correlation functions are obtained by recursively applying the generalised streaming model for decreasing n. Our results are exact within the distant-observer approximation and completely independent of the nature of the tracers for which the correlations are evaluated. Focusing on 3-point statistics, we use an N-body simulation to study the joint probability density function of the relative line-of-sight velocities of pairs of particles in a triplet. On large scales, we find that this distribution is approximately Gaussian and that its moments can be accurately computed with standard perturbation theory. We use this information to formulate a phenomenological 3-point Gaussian streaming model. A practical implementation is obtained by using perturbation theory at leading order to approximate several statistics in real space. In spite of this simplification, the resulting predictions for the matter 3-point correlation function in redshift space are in rather good agreement with measurements performed in the simulation. We discuss the limitations of the simplified model and suggest a number of possible improvements. Our results find direct applications in the analysis of galaxy clustering but also set the basis for studying 3-point statistics with future peculiar-velocity surveys and experiments based on the kinetic Sunyaev-Zel'dovich effect.
Identifying Mechanical Models through Differentiable Simulations

This paper proposes a new method for manipulating unknown objects through a sequence of non-prehensile actions that displace an object from its initial configuration to a given goal configuration on a flat surface. The proposed method leverages recent progress in differentiable physics models to identify unknown mechanical properties of manipulated objects, such as inertia matrix, friction coefficients and external forces acting on the object. To this end, a recently proposed differentiable physics engine for two-dimensional objects is adopted in this work and extended to deal forces in the three-dimensional space. The proposed model identification technique analytically computes the gradient of the distance between forecasted poses of objects and their actual observed poses and utilizes that gradient to search for values of the mechanical properties that reduce the reality gap. Experiments with real objects using a real robot to gather data show that the proposed approach can identify the mechanical properties of heterogeneous objects on the fly.
Solving the Faddeev-Merkuriev equations in total orbital momentum representation via spline collocation and tensor product preconditioning

The computational approach for solving the Faddeev-Merkuriev equations in total orbital momentum representation is presented. These equations describe a system of three quantum charged particles and are widely used in bound state and scattering calculations. The approach is based on the spline collocation method and exploits intensively the tensor product form of discretized operators and preconditioner, which leads to a drastic economy in both computer resources and time.
Constraining the primordial magnetic field with dwarf galaxy simulations

Using a set of cosmological hydro-dynamical simulations, we constrained the properties of primordial magnetic fields by studying their impact on the formation and evolution of dwarf galaxies. We performed a large set of simulations (8 dark matter only and 72 chemo-hydrodynamical) including primordial magnetic fields through the extra density fluctuations they induce at small length scales (k ≥ 10 h Mpc−1) in the matter power spectrum. Our sample of dwarfs includes nine systems selected out of the initial (3.4 Mpc h−1)3 parent box, resimulated from z = 200 to z = 0 using a zoom-in technique and including the physics of baryons. We explored a wide variety of primordial magnetic fields with strength Bλ ranging from 0.05 to 0.50 nG and magnetic energy spectrum slopes nB from −2.9 to −2.1. Strong magnetic fields characterized by a high amplitude (Bλ = 0.50, 0.20 nG with nB = −2.9) or by a steep initial power spectrum slope (nB = −2.1, −2.4, with Bλ = 0.05 nG) induce perturbations on mass scales from 107 to 109 M⊙. In this context emerging galaxies see their star formation rates strongly boosted. They become more luminous and metal rich than their counterparts without primordial magnetic fields. Such strong fields are ruled out by their inability to reproduce the observed scaling relations of dwarf galaxies. They predict that dwarf galaxies are at the origin of an unrealistically early reionization of the Universe and that they also overproduce luminous satellites in the Local Group. Weaker magnetic fields impacting the primordial density field at corresponding masses ≲106 M⊙, produce a large number of mini dark matter halos orbiting the dwarfs, however out of reach for current lensing observations. This study allows us, for the first time, to constrain the properties of primordial magnetic fields based on realistic cosmological simulations of dwarf galaxies.
Optimizing Vessel Trajectory Compression

In previous work we introduced a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. This methodology can provide reliable trajectory synopses with little deviations from the original course by discarding at least 70% of the raw data as redundant. However, such trajectory compression is very sensitive to parametrization. In this paper, our goal is to fine-tune the selection of these parameter values. We take into account the type of each vessel in order to provide a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. Furthermore, we employ a genetic algorithm converging to a suitable configuration per vessel type. Our tests against a publicly available AIS dataset have shown that compression efficiency is comparable or even better than the one with default parametrization without resorting to a laborious data inspection.
Towards Privacy-assured and Lightweight On-chain Auditing of Decentralized Storage

How to audit outsourced data in centralized storage like cloud is well-studied, but it is largely under-explored for the rising decentralized storage network (DSN) that bodes well for a billion-dollar market. To realize DSN as a usable service in a fully decentralized manner, the blockchain comes in handy — to record and verify audit trails in forms of proof of storage, and based on that, to enforce fair payments with necessary dispute resolution.Leaving the audit trails on the blockchain offers transparency and fairness, yet it 1) sacrifices privacy, as they may leak information about the data under audit, and 2) overwhelms on-chain resources, as they may be practically large in size and expensive to verify. Prior auditing designs in centralized settings are not directly applicable here. A handful of proposals targeting DSN cannot satisfactorily address these issues either.We present an auditing solution that addresses on-chain privacy and efficiency, from a synergy of homomorphic linear authenticators with polynomial commitments for succinct proofs, and the sigma protocol for provable privacy. The solution results in, per audit, 288-byte proof written to the blockchain, and constant verification cost. It can sustain long-term operation and easily scale to thousands of users on Ethereum.
Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation

State space truncation methods are widely used to approximate solutions of the chemical master equation. While most methods of this kind focus on truncating the state space directly, in this work, we propose modifying the underlying chemical reaction network by introducing slack reactants that indirectly truncate the state space. More specifically, slack reactants introduce an expanded chemical reaction network and impose a truncation scheme based on desired mass conservation laws. This network structure also allows us to prove inheritance of special properties of the original model, such as irreducibility and complex balancing. We use the network structure imposed by slack reactants to prove the convergence of the stationary distribution and first arrival times. We then provide examples comparing our method with the stationary finite state projection and finite buffer methods. Our slack reactant system appears to be more robust than some competing methods with respect to calculating first arrival times.
Active Inference for Integrated State-Estimation, Control, and Learning

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. First, we show there is a direct relationship between active inference controllers, and classic methods such as PID control. We demonstrate its application for adaptive and robust behaviour of a robotic manipulator that rivals state-of-the-art. Additionally, we show that by learning specific hyperparameters, our approach can deal with unmodeled dynamics, damps oscillations, and is robust against poor initial parameters. The approach is validated on the ‘Franka Emika Panda’ 7 DoF manipulator. Finally, we highlight limitations of active inference controllers for robotic systems.
RetinotopicNet: An Iterative Attention Mechanism Using Local Descriptors with Global Context

Convolutional Neural Networks (CNNs) were the driving force behind many advancements in Computer Vision research in recent years. This progress has spawned many practical applications and we see an increased need to efficiently move CNNs to embedded systems today. However traditional CNNs lack the property of scale and rotation invariance: two of the most frequently encountered transformations in natural images. As a consequence CNNs have to learn different features for same objects at different scales. This redundancy is the main reason why CNNs need to be very deep in order to achieve the desired accuracy. In this paper we develop an efficient solution by reproducing how nature has solved the problem in the human brain. To this end we let our CNN operate on small patches extracted using the log-polar transform, which is known to be scale and rotation equivariant. Patches extracted in this way have the nice property of magnifying the central field and compressing the periphery. Hence we obtain local descriptors with global context information. However the processing of a single patch is usually not sufficient to achieve high accuracies in e.g. classification tasks. We therefore successively jump to several different locations, called saccades, thus building an understanding of the whole image. Since log-polar patches contain global context information, we can efficiently calculate following saccades using only the small patches. Saccades efficiently compensate for the lack of translation equivariance of the log-polar transform.
Recruitment limitation: A theoretical perspective

Abstract A theoretical analysis of the concept of recruitment limitation leads to the conclusion that most populations should he regarded as jointly limited by recruitment and interactions between individuals after recruitment. The open nature of local marine systems does not permit avoidance of density-dependent interactions; it simply may make them more difficult to detect. Local populations consisting of settled organisms may not experience density-dependent interactions under some circumstances, but the entire species population consisting of the collection of local populations and their planktonic larvae must have density-dependent dynamics. Any local population of settled individuals can escape density dependence if sufficient density dependence occurs among planktonic larvae or within other local populations. Common conceptions of density dependence are too narrow, leading too often to the conclusion that it is absent from a system. It is equally wrong to expect that density-dependent interactions after settlement determine local population densities independently of recruitment. Special circumstances allowing density dependence to act strongly and quickly are needed before density dependence can neutralize the effects of recruitment. Recruitment limitation and density-dependent interactions therefore should not be regarded as alternatives but as jointly acting to determine the densities of marine benthic populations. Moreover, the interaction between fluctuating recruitment and density dependence is potentially the most interesting feature of recruitment limitation. For example, this interaction may be an important diversity-maintaining mechanism for marine systems.