Miracle-dz/UniFAR-FTUs huggingface dataset:
Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines Industry Track Paper

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe. A critical challenge to our—and any—autonomous machine is accurate and efficient localization under resource constraints, which has fueled specialized localization accelerators recently. Prior acceleration efforts are point solutions in that they each specialize for a specific localization algorithm. In real-world commercial deployments, however, autonomous machines routinely operate under different environments and no single localization algorithm fits all the environments. Simply stacking together point solutions not only leads to cost and power budget overrun, but also results in an overly complicated software stack.This paper demonstrates our new software-hardware co-designed framework for autonomous machine localization, which adapts to different operating scenarios by fusing fundamental algorithmic primitives. Through characterizing the software framework, we identify ideal acceleration candidates that contribute significantly to the end-to-end latency and/or latency variation. We show how to co-design a hardware accelerator to systematically exploit the parallelisms, locality, and common building blocks inherent in the localization framework. We build, deploy, and evaluate an FPGA prototype on our next-generation self-driving cars. To demonstrate the flexibility of our framework, we also instantiate another FPGA prototype targeting drones, which represent mobile autonomous machines. We achieve about $2 \times$ speedup and $4 \times$ energy reduction compared to widely-deployed, optimized implementations on general-purpose platforms.
Simple Mitigation Strategy for a Systematic Gate Error in IBMQ

We report the observation and characterisation of a systematic error in the implementation of $U_3$ gates in the IBM quantum computers. The error appears as a consistent shift in one of the angles of the gate, whose magnitude does not correlate with IBM's cited errors calculated using Clifford randomized benchmarking. We propose a simple mitigation procedure, leading to an improvement in the observed value for the CHSH inequality, highlighting the utility of simple mitigation strategies for short-depth quantum circuits.
DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference

Most of the existing work on FPGA acceleration of Convolutional Neural Network (CNN) focuses on employing a single strategy (algorithm, dataflow, etc.) across all the layers. Such an approach does not achieve optimal latency on complex and deep CNNs. Emerging CNNs have diverse per-layer computation characteristics including parallelism, arithmetic intensity, locality, and memory footprint. Per-layer strategy selection and fine-grained tuning are required to achieve low end-to-end latency. However, specialized hardware modules dedicated to each layer limit the per-layer utilization and adversely affect end-to-end latency. In this paper, we address these problems by an algorithm-architecture co-optimization framework, DYNAMAP, consisting of (1) a unified hardware overlay that can be reused across layers, supporting dynamic mapping of all three families of popular convolution algorithms, and further allowing flexible dataflow switching to maximize hardware utilization for each layer; (2) a novel software Design Space Exploration (DSE) flow that customizes the hardware overlay and chooses optimal strategy mapping. We show that the algorithm mapping space increases exponentially with network depth, and while the optimal algorithm selection problem is NP-hard in general, by exploiting the series-parallel structure of CNN models, we demonstrate a polynomial-time solution for optimal algorithm mapping. DYNAMAP is optimized for any CNN, including those having diverse computation and memory requirements across the layers. We demonstrate DYNAMAP using two state-of-the-art CNNs - GoogleNet and Inception-V4. The generated accelerators achieve up to 2.8x and 1.4x speedups, respectively, wrt inference latency compared with the state-of-the-art FPGA implementations.
Quantitative bounds on vortex fluctuations in 2d$2d$ Coulomb gas and maximum of the integer‐valued Gaussian free field

In this paper, we study the influence of the vortices on the fluctuations of 2d$2d$ systems such as the Coulomb gas, the Villain model, or the integer‐valued Gaussian free field (GFF). In the case of the 2d$2d$ Villain model, we prove that the fluctuations induced by the vortices are at least of the same order of magnitude as the ones produced by the spin wave. We obtain the following quantitative upper bound on the two‐point correlation in Z2$\mathbb {Z}^2$ when β>1$\beta >1$ ⟨σxσy⟩βVillain⩽C1∥x−y∥212πβ1+βe−(2π)22β. $$\begin{align*} \hspace*{20pt}\langle \sigma _x \sigma _y\rangle _{\beta }^{\rm Villain}\leqslant C \, {\left(\frac{1}{\Vert x-y\Vert _2}\right)}^{\frac{1}{2\pi \beta }{\left(1+\beta e^{-\frac{(2\pi )^2}{2} \beta }\right)}}. \end{align*}$$The proof is entirely nonperturbative. Furthermore, it provides a new and algorithmically efficient way of sampling the 2d$2d$ Coulomb gas. For the 2d$2d$ Coulomb gas, we obtain the following lower bound on its fluctuations at high inverse temperature: EβCoul⟨Δ−1q,g⟩⩾exp(−π2β+o(β))⟨g,(−Δ)−1g⟩. $$\begin{align*} \hspace*{10pt}{ \mathbb {E}_{\beta }^{\,Coul} \bigl [ \langle \Delta ^{-1}q, g\rangle \bigr ]} \geqslant \exp (-\pi ^2 \beta + o(\beta )) \langle g,(-\Delta )^{-1}g\rangle . \end{align*}$$This estimate coincides with the predictions based on Renormalization group (RG) analysis by José et al. [Phys. Rev. B 16 (1977), no. 3, 1217] and suggests that the Coulomb potential Δ−1q$\Delta ^{-1}q$ at inverse temperature β$\beta$ should scale like a GFF of inverse temperature of order exp(π2β)$\exp (\pi ^2 \beta )$ .
Ship Detection: Parameter Server Variant

Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correct classification of ships, typically limiting class accuracy scores to 88\%. This work explores the tensions between customization strategies, class accuracy rates, training times, and costs in cloud based solutions. We demonstrate how a custom U-Net can achieve 92\% class accuracy over a validation dataset and 68\% over a target dataset with 90\% confidence. We also compare a single node architecture with a parameter server variant whose workers act as a boosting mechanism. The parameter server variant outperforms class accuracy on the target dataset reaching 73\% class accuracy compared to the best single node approach. A comparative investigation on the systematic performance of the single node and parameter server variant architectures is discussed with support from empirical findings.
Policy Supervectors: General Characterization of Agents by their Behaviour

By studying the underlying policies of decision-making agents, we can learn about their shortcomings and potentially improve them. Traditionally, this has been done either by examining the agent's implementation, its behaviour while it is being executed, its performance with a reward/fitness function or by visualizing the density of states the agent visits. However, these methods fail to describe the policy's behaviour in complex, high-dimensional environments or do not scale to thousands of policies, which is required when studying training algorithms. We propose policy supervectors for characterizing agents by the distribution of states they visit, adopting successful techniques from the area of speech technology. Policy supervectors can characterize policies regardless of their design philosophy (e.g. rule-based vs. neural networks) and scale to thousands of policies on a single workstation machine. We demonstrate method's applicability by studying the evolution of policies during reinforcement learning, evolutionary training and imitation learning, providing insight on e.g. how the search space of evolutionary algorithms is also reflected in agent's behaviour, not just in the parameters.
Global and Individualized Community Detection in Inhomogeneous Multilayer Networks

In network applications, it has become increasingly common to obtain datasets in the form of multiple networks observed on the same set of subjects, where each network is obtained in a related but different experiment condition or application scenario. Such datasets can be modeled by multilayer networks where each layer is a separate network itself while different layers are associated and share some common information. The present paper studies community detection in a stylized yet informative inhomogeneous multilayer network model. In our model, layers are generated by different stochastic block models, the community structures of which are (random) perturbations of a common global structure while the connecting probabilities in different layers are not related. Focusing on the symmetric two block case, we establish minimax rates for both \emph{global estimation} of the common structure and \emph{individualized estimation} of layer-wise community structures. Both minimax rates have sharp exponents. In addition, we provide an efficient algorithm that is simultaneously asymptotic minimax optimal for both estimation tasks under mild conditions. The optimal rates depend on the \emph{parity} of the number of most informative layers, a phenomenon that is caused by inhomogeneity across layers.
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks

In this paper, we consider jointly optimizing cell load balance and network throughput via a reinforcement learning (RL) approach, where inter-cell handover (i.e., user association assignment) and massive MIMO antenna tilting are configured as the RL policy to learn. Our rationale behind using RL is to circumvent the challenges of analytically modeling user mobility and network dynamics. To accomplish this joint optimization, we integrate vector rewards into the RL value network and conduct RL action via a separate policy network. We name this method as Pareto deterministic policy gradients (PDPG). It is an actor-critic, model-free and deterministic policy algorithm which can handle the coupling objectives with the following two merits: 1) It solves the optimization via leveraging the degree of freedom of vector reward as opposed to choosing handcrafted scalar-reward; 2) Cross-validation over multiple policies can be significantly reduced. Accordingly, the RL enabled network behaves in a self-organized way: It learns out the underlying user mobility through measurement history to proactively operate handover and antenna tilt without environment assumptions. Our numerical evaluation demonstrates that the introduced RL method outperforms scalar-reward based approaches. Meanwhile, to be self-contained, an ideal static optimization based brute-force search solver is included as a benchmark. The comparison shows that the RL approach performs as well as this ideal strategy, though the former one is constrained with limited environment observations and lower action frequency, whereas the latter ones have full access to the user mobility. The convergence of our introduced approach is also tested under different user mobility environment based on our measurement data from a real scenario.
ARMADA. I. Triple Companions Detected in B-type Binaries α Del and ν Gem

Ground-based optical long-baseline interferometry has the power to measure the orbits of close binary systems at ∼10 μas precision. This precision makes it possible to detect “wobbles” in the binary motion due to the gravitational pull from additional short-period companions. We started the ARrangement for Micro-Arcsecond Differential Astrometry (ARMADA) survey with the Michigan Infra-Red Combiner (MIRC)/MIRC-X instrument at the Center for High Angular Resoloution Astronomy (CHARA) array for the purpose of detecting giant planets and stellar companions orbiting individual stars in binary systems. We describe our observations for the survey, and introduce the wavelength calibration scheme that delivers precision at the tens of microarcseconds level for <02 binaries. We test our instrument performance on a known triple system, κ Peg, and show that our survey is delivering a factor of 10 better precision than previous similar surveys. We present astrometric detections of tertiary components to two B-type binaries: a 30 day companion to α Del, and a 50 day companion to ν Gem. We also collected radial velocity data for α Del with the Tennessee State University Automated Spectroscopic Telescope at Fairborn Observatory. We are able to measure the orbits and masses of all three components in these systems. We find that the previously published radial velocity orbit for the inner pair of ν Gem is not consistent with our visual orbit. The precision achieved for these orbits suggests that our ARMADA survey will be successful at discovering new compact triple systems to A/B-type binary systems, leading to better statistics of hierarchical system architectures and formation history.
On connection among quantum-inspired algorithms of the Ising model

Various combinatorial optimization NP-hard problems can be reduced to finding the minimizer of an Ising model, which is a discrete mathematical model. It is an intellectual challenge to develop some mathematical tools or algorithms for solving the Ising model. Over the past decades, some continuous approaches or algorithms have been proposed from physical, mathematical or computational views for optimizing the Ising model such as quantum annealing, the coherent Ising machine, simulated annealing, adiabatic Hamiltonian systems, etc. However, the mathematical principle of these algorithms is far from being understood. In this paper, we reveal the mathematical mechanism of dynamical system algorithms for the Ising model by Morse theory and variational methods. We prove that the dynamical system algorithms can be designed to minimize a continuous function whose local minimum points give all the candidates of the Ising model and the global minimum gives the minimizer of Ising problem. Using this mathematical mechanism, we can easily understand several dynamical system algorithms of the Ising model such as the coherent Ising machine, the Kerr-nonlinear parametric oscillators and the simulated bifurcation algorithm. Furthermore, motivated by the works of C. Conley, we study transit and capture properties of the simulated bifurcation algorithm to explain its convergence by the low energy transit and capture in celestial mechanics. A detailed discussion on $2$-spin and $3$-spin Ising models is presented as application.
Green Internet of Things for a Better World

The Internet of Things (IoT) is seen as a novel technical paradigm aimed at enabling connectivity between billions of interconnected devices. This IoT is being used in various domains, such as smart healthcare, traffic surveillance, smart homes, smart cities, and various industries. The main functionality of IoT includes sensing out the surrounding environment and collect the data from the surrounding and transmit those data to the remote data centers or the cloud. This sharing of vast volumes of data between billions of IoT devices generates a large demand for energy and increases energy wastage in the form of heat. The idea of reducing the energy consumption of IoT devices and keeping the environment safe and clean is envisaged by the Green IoT. Inspired by achieving a sustainable IoT ecosystem and guiding us towards making a healthy green planet, we first offer an overview of Green IoT (GIoT), and then the challenges and the future directions with regards to the GIoT are presented in our study.
Ethnicity and gender influence the decision making in a multinational state: The case of Russia

Individuals' behavior in economic decisions depends on such factors as ethnicity, gender, social environment, personal traits. However, the distinctive features of decision making have not been studied properly so far between indigenous populations from different ethnicities in a modern and multinational state like the Russian Federation. Addressing this issue, we conducted a series of experiments between the Russians in Moscow (the capital of Russia) and the Yakuts in Yakutsk (the capital of Russian region with the mostly non-Russian residents). We investigated the effect of socialization on participants' strategies in the Prisoner's Dilemma game, Ultimatum game, and Trust game. At the baseline stage, before socialization, the rates of cooperation, egalitarianism, and trust for the Yakuts are higher than for the Russians in groups composed of unfamiliar people. After socialization, for the Russians all these indicators increase considerably; whereas, for the Yakuts only the rate of cooperation demonstrates a rising trend. The Yakuts are characterized by relatively unchanged indicators regardless of the socialization stage. Furthermore, the Yakutsk females have higher rates of cooperation and trust than the Yakuts males before socialization. After socialization, we observed the alignment in indicators for males and females both for the Russians and for the Yakuts. Hence, we concluded that cultural differences can exist inside one country despite the equal economic, politic, and social conditions.
Temperonic Crystal: A Superlattice for Temperature Waves in Graphene

Coherent control of wavelike phenomena via metamaterials is driving, ever since four decades, a technological revolution in fields ranging from electronics, photonics, to phononics. Although temperature has been historically considered as the paradigmatic example of an incoherent field, undergoing diffusive as opposed to wavelike propagation, on short space and timescales Fourier law fails and the possibility for temperature wavelike propagation sets in [1] . The ultimate goal is to devise metamaterials, addressed as temperonic metamaterials, enabling coherent control of temperature oscillations arising in the hydrodynamic heat transport regime and operating at above liquid nitrogen temperature.
Scalable Data Discovery Using Profiles

We study the problem of discovering joinable datasets at scale. This is, how to automatically discover pairs of attributes in a massive collection of independent, heterogeneous datasets that can be joined. Exact (e.g., based on distinct values) and hash-based (e.g., based on locality-sensitive hashing) techniques require indexing the entire dataset, which is unattainable at scale. To overcome this issue, we approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportions between candidate attributes. We implement our approach in a system called NextiaJD, and present extensive experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains similar predictive performance to that of hash-based methods, yet we are able to scale-up to larger volumes of data. Also, NextiaJD generates a considerably less amount of false positives, which is a desirable feature at scale.
Atomic forces by quantum Monte Carlo: Application to phonon dispersion calculations

We report the first successful application of the {\it ab initio} quantum Monte Carlo (QMC) framework to a phonon dispersion calculation. A full phonon dispersion of diamond is successfully calculated at the variational Monte Carlo (VMC) level, based on the frozen-phonon technique. The VMC-phonon dispersion is in good agreement with the experimental results, giving renormalized harmonic optical frequencies very close to the experimental values, by significantly improving upon density functional theory (DFT) in the generalized gradient approximation. Key to success for the QMC approach is the statistical error reduction in atomic force evaluation. We show that this can be achieved by using well conditioned atomic basis sets, by explicitly removing the basis-set redundancy, which reduces the statistical error of forces by up to two orders of magnitude. This leads to affordable and accurate QMC-phonons calculations, up to $10^{4}$ times more efficient than previous attempts, and paves the way to new applications, particularly in correlated materials, where phonons have been poorly reproduced so far.
CORRIDRONE: Corridors for Drones, An Adaptive On-Demand Multi-Lane Design and Testbed

In this article, a novel drone skyway framework called CORRIDRONE is proposed. As the name suggests, this represents virtual air corridors for point-to-point safe passage of multiple drones. The corridors are not permanent but can be set up on demand. A few such scenarios could be those in warehouse/factory floors, package delivery, shore-to-ship delivery, border patrol, etc. Several factors play major roles in the planning and design of such aerial passages. The proposed framework includes many novel features which aid safe and efficient integration of UAVs into the airspace with already available technologies. A several kilometres long test bed is proposed to be set-up at the 1500 acres Challekere campus of Indian Institute of Science, in the state of Karnataka, to design and test the infrastructure required for CORRIDRONE.
A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking

We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking. Applying SfM allows us to determine camera motions without manually defining feature tracks or calibrating the cameras used to capture the image data. With MVS we are able to automatically compute dense scene models, which is not feasible with the built-in tools of Blender. Currently, our framework supports several state-of-the-art SfM and MVS pipelines. The modular system design enables us to integrate further approaches without additional effort. The framework is publicly available as an open source software package.
Analyzing Training Using Phase Transitions in Entropy - Part I: General Theory

We analyze phase transitions in the conditional entropy of a sequence caused by a change in the conditional variables or input distribution. Such transitions happen, for example, when training to learn the parameters of a system, since the switch from training input to data input causes a discontinuous jump in the conditional entropy of the measured system response. We show that the size of the discontinuity is of particular interest in the computation of mutual information during data transmission, and that this mutual information can be calculated as the difference between two derivatives of a single function. For large-scale systems we present a method of computing the mutual information for a system model with one-shot learning that does not require Gaussianity or linearity in the model, and does not require worst-case noise approximations or explicit estimation of any unknown parameters. The model applies to a broad range of algorithms and methods in communication, signal processing, and machine learning that employ training as part of their operation.