Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Query Processing on Autonomous Cameras

Surveillance IoT cameras are becoming autonomous: they operate on batteries without connecting to power wires, and run analytics without help from external compute resources. Such autonomous paradigm significantly frees video analytics from privacy concern and deployment constraint. However, they are constrained by limited energy and on-device compute resources. We present a system AutCam to support counting query, an important query class, on autonomous cameras. The system is built atop three key ideas. First, AutCam identifies and utilizes the rich trade-off between sample quality and sample quantity on a given time window. Second, AutCam introduces a split-phase sampling planner to judiciously allocate energy across multiple time windows, and also balance the capture and process energy. Third, AutCam constructs a unified bounded error by integrating the errors from both sampling and inaccurate neural networks. Tested on over 1,000-hour videos, our prototype of AutCam can provide good estimation of ground-truth object counts with bounded, narrow error under typical solar-harvested energy, and achieve up to 10X energy saving over baselines.
Radiation reaction, over-reaction, and under-reaction

The subject of radiation reaction in classical electromagnetism remains controversial over 120 years after the pioneering work of Lorentz. We give a simple but rigorous treatment of the subject at the textbook level that explains the apparent paradoxes that are much discussed in the literature on the subject. We first derive the equation of motion of a charged particle from conservation of energy and momentum, which includes the self-force term. We then show that this theory is unstable if charged particles are pointlike: the energy is unbounded from below, and charged particles self-accelerate (`over-react') due to their negative `bare' mass. This theory clearly does not describe our world, but we show that these instabilities are absent if the particle has a finite size larger than its classical radius. For such finite-size charged particles, the effects of radiation reaction can be computed in a systematic expansion in the size of the particle. The leading term in this expansion is the reduced-order Abraham-Lorentz equation of motion, which has no stability problems. We also discuss the apparent paradox that a particle with constant acceleration radiates, but does not suffer radiation reaction (`under-reaction'). Along the way, we introduce the ideas of renormalization and effective theories, which are important in many areas of modern theoretical physics. We hope that this will be a useful addition to the literature that will remove some of the air of mystery and paradox surrounding the subject.
A Tale of Two Trees: One Writes, and Other Reads

Abstract The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T3, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access machine (ORAM) to protect SPV clients’ requests from potentially malicious nodes. The key novelty of T3 lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output (UTXO) Set. Our experiment shows that T3 is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.
Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning

Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop “Help, Anna! ” (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural language-and-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments.
Decentralized Stochastic Gradient Tracking for Empirical Risk Minimization

Recent works have shown superiorities of decentralized SGD to centralized counterparts in large-scale machine learning, but their theoretical gap is still not fully understood. In this paper, we propose a decentralized stochastic gradient tracking (DSGT) algorithm over peer-to-peer networks for empirical risk minimization problems, and explicitly evaluate its convergence rate in terms of key parameters of the problem, e.g., algebraic connectivity of the communication network, mini-batch size, and gradient variance. Importantly, it is the first theoretical result that can \emph{exactly} recover the rate of the centralized SGD, and has optimal dependence on the algebraic connectivity of the networks when using stochastic gradients. Moreover, we explicitly quantify how the network affects speedup and the rate improvement over existing works. Interestingly, we also point out for the first time that both linear and sublinear speedup can be possible. We empirically validate DSGT on neural networks and logistic regression problems, and show its advantage over the state-of-the-art algorithms.
On Domination Coloring in Graphs

A domination coloring of a graph G is a proper vertex coloring of G, such that each vertex of G dominates at least one color class (possibly its own class), and each color class is dominated by at least one vertex. The minimum number of colors among all domination colorings is called the domination chromatic number, denoted by χdd(G). In this paper, we study the complexity of the k-domination coloring problem by proving its NP-completeness for arbitrary graphs. We give basic results and properties of χdd(G), including the bounds and characterization results, and further research χdd(G) of some special classes of graphs, such as the split graphs, the generalized Petersen graphs, corona products, and edge corona products. Several results on graphs with χdd(G)=χ(G) are presented. Moreover, an application of domination colorings in social networks is proposed.
ShapeBots: Shape-changing Swarm Robots

We introduce shape-changing swarm robots. A swarm of self-transformable robots can both individually and collectively change their configuration to display information, actuate objects, act as tangible controllers, visualize data, and provide physical affordances. ShapeBots is a concept prototype of shape-changing swarm robots. Each robot can change its shape by leveraging small linear actuators that are thin (2.5 cm) and highly extendable (up to 20cm) in both horizontal and vertical directions. The modular design of each actuator enables various shapes and geometries of self-transformation. We illustrate potential application scenarios and discuss how this type of interface opens up possibilities for the future of ubiquitous and distributed shape-changing interfaces.
Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow

Reynolds-averaged Navier-Stokes (RANS) equations are presently one of the most popular models for simulating turbulence. Performing RANS simulation requires additional modelling for the anisotropic Reynolds stress tensor, but traditional Reynolds stress closure models lead to only partially reliable predictions. Recently, data-driven turbulence models for the Reynolds anisotropy tensor involving novel machine learning techniques have garnered considerable attention and have been rapidly developed. Focusing on modelling the Reynolds stress closure for the specific case of turbulent channel flow, this paper proposes three modifications to a standard neural network to account for the no-slip boundary condition of the anisotropy tensor, the Reynolds number dependence, and spatial non-locality. The modified models are shown to provide increased predicative accuracy compared to the standard neural network when they are trained and tested on channel flow at different Reynolds numbers. The best performance is yielded by the model combining the boundary condition enforcement and Reynolds number injection. This model also outperforms the Tensor Basis Neural Network in Ling et al. [Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J Fluid Mech. 2016;807:155–166] on the turbulent channel flow dataset.
Neural Architecture Search in Embedding Space

The neural architecture search (NAS) algorithm with reinforcement learning can be a powerful and novel framework for the automatic discovering process of neural architectures. However, its application is restricted by noncontinuous and high-dimensional search spaces, which result in difficulty in optimization. To resolve these problems, we proposed NAS in embedding space (NASES), which is a novel framework. Unlike other NAS with reinforcement learning approaches that search over a discrete and high-dimensional architecture space, this approach enables reinforcement learning to search in an embedding space by using architecture encoders and decoders. The current experiment demonstrated that the performance of the final architecture network using the NASES procedure is comparable with that of other popular NAS approaches for the image classification task on CIFAR-10. The beneficial-performance and effectiveness of NASES was impressive even when only the architecture-embedding searching and pre-training controller were applied without other NAS tricks such as parameter sharing. Specifically, considerable reduction in searches was achieved by reducing the average number of searching to < 100 architectures to achieve a final architecture for the NASES procedure.
Automated Let's Play Commentary

Let's Plays of video games represent a relatively unexplored area for experimental AI in games. In this short paper, we discuss an approach to generate automated commentary for Let's Play videos, drawing on convolutional deep neural networks. We focus on Let's Plays of the popular game Minecraft. We compare our approach and a prior approach and demonstrate the generation of automated, artificial commentary.
Semilocalization transition driven by a single asymmetrical tunneling

A local impurity usually only strongly affects few single-particle energy levels, thus cannot induce a quantum phase transition (QPT), or any macroscopic quantum phenomena in a many-body system within the Hermitian regime. However, it may happen for a non-Hermitian impurity. We investigate the many-body ground state property of a one-dimensional tight-binding ring with an embedded single asymmetrical dimer based on exact solutions. We introduce the concept of semi-localization state to describe a new quantum phase, which is a crossover from extended to localized state. The peculiar feature is that the decay length is of the order of the system size, rather than fixed as a usual localized state. In addition, the spectral statistics is non-analytic as asymmetrical hopping strengths vary, resulting a sudden charge of the ground state. The distinguishing feature of such a QPT is that the density of ground state energy varies smoothly due to unbroken symmetry. However, there are other observables, such as the groundstate center of mass and average current, exhibit the behavior of second-order QPT. This behavior stems from time-reversal symmetry breaking of macroscopic number of single-particle eigen states.
A Rewriting Logic Approach to Stochastic and Spatial Constraint System Specification and Verification

This paper addresses the issue of specifying, simulating, and verifying reactive systems in rewriting logic. It presents an executable semantics for probabilistic, timed, and spatial concurrent constraint programming ---here called stochastic and spatial concurrent constraint systems (SSCC)--- in the rewriting logic semantic framework. The approach is based on an enhanced and generalized model of concurrent constraint programming (CCP) where computational hierarchical spaces can be assigned to belong to agents. The executable semantics faithfully represents and operationally captures the highly concurrent nature, uncertain behavior, and spatial and epistemic characteristics of reactive systems with flow of information. In SSCC, timing attributes ---represented by stochastic duration--- can be associated to processes, and exclusive and independent probabilistic choice is also supported. SMT solving technology, available from the Maude system, is used to realize the underlying constraint system of SSCC with quantifier-free formulas over integers and reals. This results in a fully executable real-time symbolic specification that can be used for quantitative analysis in the form of statistical model checking. The main features and capabilities of SSCC are illustrated with examples throughout the paper. This contribution is part of a larger research effort aimed at making available formal analysis techniques and tools, mathematically founded on the CCP approach, to the research community.
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone’s preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then finetune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models finetuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.
A phenomenological study of chronic nonmalignant pain

Chronic nonmalignant pain is a poorly understood subjective phenomenon that affects millions of people. Numerous physiological and psychological hypotheses have been proposed to explain the mechanisms by which it occurs and persists, yet treatment is not always successful in alleviating pain. Phenomenology is a research approach that attempts to provide an understanding of the meaning of lived experience. Such an understanding enhances personal and meaningful care, which can improve the patient’s response to treatment and his or her quality of life. Despite these potential benefits, little phenomenological work on chronic nonmalignant pain has been found in the literature. This report describes the results of a phenomenological study that addresses the question: What is the essential structure of the lived experience of chronic nonmalignant pain? Three major concepts: loss of control, social support, and acceptance, were identified based on 13 themes that emerged from interviews with 11 participants who had chronic nonmalignant pain for over six months.
The heat kernel on $SL(2,\mathbb{R})$

Let $G$ be a noncompact semisimple Lie group equipped with a certain invariant Riemannian metric. Then, we can consider a heat kernel function on $G$ associated to the Riemannian metric. We give an explicit formula for the heat kernel when $G = SL(2,\mathbb{R})$. The main tools are spherical transform and Helgason-Fourier transform for homogeneous vector bundles studied by R. Camporesi.
Ultra-low dissipation patterned silicon nanowire arrays for scanning probe microscopy.

In recent years, self-assembled semiconductor nanowires have been successfully used as ultra-sensitive cantilevers in a number of unique scanning probe microscopy (SPM) settings. We describe the fabrication of ultra-low dissipation patterned silicon nanowire (SiNW) arrays optimized for scanning probe applications. Our fabrication process produces, with high yield, ultra-high aspect ratio vertical SiNWs that exhibit exceptional force sensitivity. The highest sensitivity SiNWs have thermomechanical-noise limited force sensitivity of 9.7 ± 0.4 aN/√Hz at room temperature and 500 ± 20 zN/√Hz at 4 K. To facilitate their use in SPM, the SiNWs are patterned within 7 μm from the edge of the substrate, allowing convenient optical access for displacement detection.
Global Attenuation in Spiral Galaxies in Optical and Infrared Bands

The emerging light from a galaxy is under the influence of its own interstellar medium, as well as its spatial orientation. Considering a sample of 2239 local spiral galaxies in optical (Sloan Digital Sky Survey u, g, r, i, and z) and infrared bands (WISE W1, W1), we study the dependency of the global intrinsic attenuation in spiral galaxies on their morphologies, sizes, and spatial inclinations. Reddening is minimal at the extremes of low mass and gas depletion and maximal in galaxies that are relatively massive and metal-rich and still retain substantial gas reserves. A principal component constructed from observables that monitor galaxy mass, relative H i content to old stars, and infrared surface brightness is strongly correlated with the amplitude of obscuration. We determine both a parametric model for dust obscuration and a nonparametric model based on the Gaussian process formalism. An average dust attenuation curve is derived for wavelengths between 0.36 and 4.5 μm.
Progenitors and explosion properties of supernova remnants hosting central compact objects: I. RCW 103 associated with the peculiar source 1E 161348−5055

We present a Chandra and XMM–Newton imaging and spectroscopic study of the supernova remnant (SNR) RCW 103 (G332.4−00.4) containing the central compact object 1E 161348−5055. The high-resolution Chandra X-ray images reveal enhanced emission in the south-eastern and north-western regions. Equivalent width line images of Fe L, Mg, Si, and S using XMM–Newton data were used to map the distribution of ejecta. The SNR was sectioned into 56 regions best characterized by two-component thermal models. The harder component (kT ∼ 0.6 keV) is adequately fitted by the VPSHOCK non-equilibrium ionization model with an ionization time-scale net ∼ 1011–1012 cm−3 s, and slightly enhanced abundances over solar values. The soft component (kT ∼ 0.2 keV), fitted by the APEC model, is well described by plasma in collisional ionization equilibrium with abundances consistent with solar values. Assuming a distance of 3.1 kpc and a Sedov phase of expansion into a uniform medium, we estimate an SNR age of 4.4 kyr, a swept-up mass Msw = 16$f_\mathrm{ s}^{-1/2}$ D$_{3.1}^{5/2}$ M⊙, and a low explosion energy E* = 3.7 × 1049 $f_\mathrm{ s}^{-1/2}$ D$_{3.1}^{5/2}$ erg. This energy could be an order of magnitude higher if we relax the Sedov assumption, the plasma has a low filling factor, the plasma temperature is underestimated, or if the SNR is expanding into the progenitor’s wind-blown bubble. Standard explosion models did not match the ejecta yields. By comparing the fitted abundances to the most recent core-collapse nucleosynthesis models, our best estimate yields a low-mass progenitor of around 12–13 M⊙, lower than previously reported. We discuss degeneracies in the model fitting, particularly the effect of altering the explosion energy on the progenitor mass estimate.
Interfacial Thermal Conductance across Room-Temperature Bonded GaN-Diamond Interfaces for GaN-on-Diamond Devices.

The wide bandgap, high-breakdown electric field, and high carrier mobility makes GaN an ideal material for high-power and high-frequency electronics applications such as wireless communication and radar systems. However, the performance and reliability of GaN-based high electron mobility transistors (HEMTs) are limited by the high channel temperature induced by Joule-heating in the device channel. Integration of GaN with high thermal conductivity substrates can improve the heat extraction from GaN-based HEMTs and lower the device operating temperature. However, heterogeneous integration of GaN with diamond substrates presents technical challenges to maximize the heat dissipation potential brought by the ultrahigh thermal conductivity of diamond substrates. In this work, two modified room-temperature surface-activated bonding (SAB) techniques are used to bond GaN and single crystal diamond. Time-domain thermoreflectance (TDTR) is used to measure the thermal properties from room temperature to 480 K. A relatively large thermal boundary conductance (TBC) of the GaN-diamond interfaces with a ~4-nm interlayer (~90 MW/m2-K) was observed and material characterization was performed to link the interfacial structure with the TBC. Device modeling shows that the measured TBC of the bonded GaN-diamond interfaces can enable high-power GaN devices by taking full advantage of the ultrahigh thermal conductivity of single crystal diamond. For the modeled devices, the power density of GaN-on-diamond can reach about 2.5 times as that of GaN-on-SiC and about 5.4 times as that of GaN-on-Si with a maximum device temperature of 250 oC. Our work sheds light on the potential for room-temperature heterogeneous integration of semiconductors with diamond for applications of electronics cooling, especially for GaN-on-diamond devices.