Miracle-dz/UniFAR-FTUs huggingface dataset:
Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

Interferometric imaging is an emerging technique for particle tracking and mass photometry. Mass or position are estimated from weak signals, coherently scattered from nanoparticles or single molecules, and interfered with a co-propagating reference. In this work, we perform a statistical analysis and derive lower bounds on the measurement precision of the parameters of interest from shot-noise limited images. This is done by computing the classical Cramér–Rao bound (CRB) for localization and mass estimation, using a precise vectorial model of interferometric imaging techniques. We then derive fundamental bounds valid for any imaging system, based on the quantum Cramér–Rao formalism. This approach enables a rigorous and quantitative comparison of common techniques such as interferometric scattering microscopy (iSCAT), coherent brightfield microscopy, and dark-field microscopy. In particular, we demonstrate that the light collection geometry in iSCAT greatly increases the axial position sensitivity, and that the Quantum CRB for mass estimation yields a minimum relative estimation error of σm/m=1/(2N) , where N is the number of collected scattered photons.
Large-Scale Network Embedding in Apache Spark

Network embedding has been widely used in social recommendation and network analysis, such as recommendation systems and anomaly detection with graphs. However, most of previous approaches cannot handle large graphs efficiently, due to that (i) computation on graphs is often costly and (ii) the size of graph or the intermediate results of vectors could be prohibitively large, rendering it difficult to be processed on a single machine. In this paper, we propose an efficient and effective distributed algorithm for network embedding on large graphs using Apache Spark, which recursively partitions a graph into several small-sized subgraphs to capture the internal and external structural information of nodes, and then computes the network embedding for each subgraph in parallel. Finally, by aggregating the outputs on all subgraphs, we obtain the embeddings of nodes in a linear cost. After that, we demonstrate in various experiments that our proposed approach is able to handle graphs with billions of edges within a few hours and is at least 4 times faster than the state-of-the-art approaches. Besides, it achieves up to 4.25% and 4.27% improvements on link prediction and node classification tasks respectively. In the end, we deploy the proposed algorithms in two online games of Tencent with the applications of friend recommendation and item recommendation, which improve the competitors by up to 91.11% in running time and up to 12.80% in the corresponding evaluation metrics.
Derived Langlands VIII: Local GLn

In this, the eighth article in my Derived Langlands series, I describe the construction of a 2-variable L-function for two representations of general linear groups of a $p$-adic local field. Due to extenuating health circumstances, many of the proofs and ideas herein are suggestive rather than the finished definitive versions.
A new microscopic look into pressure fields and local buoyancy

The concept of local pressure is pivotal to describe many important physical phenomena, such as buoyancy or atmospheric phenomena, which always require the consideration of space-varying pressure fields. These fields have precise definitions within the phenomenology of hydro-thermodynamics, but a simple and pedagogical microscopic description based on Statistical Mechanical is still lacking in present literature. In this paper, we propose a new microscopic definition of the local pressure field inside a classical fluid, relying on a local barometer potential that is built into the many-particle Hamiltonian. Such a setup allows the pressure to be locally defined, at an arbitrary point inside the fluid, simply by doing a standard ensemble average of the radial force exerted by the barometer potential on the gas' particles. This setup is further used to give a microscopic derivation of the generalized Archimedes's buoyancy principle, in the presence of an arbitrary external field. As instructive examples, buoyancy force fields are calculated for ideal fluids in the presence of: i) a uniform force field, ii) a spherically symmetric harmonic confinement field, and iii) a centrifugal rotating frame.
Formulation and resolutions of the red sky paradox

Significance Red dwarf stars are the most numerous and long-lived stars in the cosmos, and recent exoplanet discoveries indicate an abundance of rocky, temperate planets around them. This presents an apparent paradox as to why we do not see a red dwarf in our sky. This “red sky paradox” could plausibly be random chance at the 1-in-100 level but would then come into tension with the Copernican principle. Three additional resolutions to the paradox are outlined, which broadly inhibit the opportunities for complex life to develop around such stars: attenuated emergence rates, truncated evolutionary windows, and/or a paucity of suitable habitats. All three appear viable given our present limited knowledge but the potential for future observational tests is explored. Most stars in the Universe are red dwarfs. They outnumber stars like our Sun by a factor of 5 and outlive them by another factor of 20 (population-weighted mean). When combined with recent observations uncovering an abundance of temperate, rocky planets around these diminutive stars, we are faced with an apparent logical contradiction—Why do we not see a red dwarf in our sky? To address this “red sky paradox,” we formulate a Bayesian probability function concerning the odds of finding oneself around an F/G/K-spectral type (Sun-like) star. If the development of intelligent life from prebiotic chemistry is a universally rapid and ensured process, the temporal advantage of red dwarfs dissolves, softening the red sky paradox, but exacerbating the classic Fermi paradox. Otherwise, we find that humanity appears to be a 1-in-100 outlier. While this could be random chance (resolution I), we outline three other nonmutually exclusive resolutions (II to IV) that broadly act as filters to attenuate the suitability of red dwarfs for complex life. Future observations may be able to provide support for some of these. Notably, if surveys reveal a paucity of temperate rocky planets around the smallest (and most numerous) red dwarfs, then this would support resolution II. As another example, if future characterization efforts were to find that red dwarf worlds have limited windows for complex life due to stellar evolution, this would support resolution III. Solving this paradox would reveal guidance for the targeting of future remote life sensing experiments and the limits of life in the cosmos.
Efficiencies of Magnetic Field Amplification and Electron Acceleration in Young Supernova Remnants: Global Averages and Kepler’s Supernova Remnant

Particle acceleration to suprathermal energies in strong astrophysical shock waves is a widespread phenomenon, generally explained by diffusive shock acceleration. Such shocks can also amplify the upstream magnetic field considerably beyond simple compression. The complex plasma physics processes involved are often parameterized by assuming that shocks put some fraction ϵ e of their energy into fast particles and another fraction ϵ B into the magnetic field. Modelers of shocks in supernovae, supernova remnants (SNRs), and gamma-ray bursters, among other locations, often assume typical values for these fractions, presumed to remain constant in time. However, it is rare that enough properties of a source are independently constrained that values of the epsilons can be inferred directly. SNRs can provide such circumstances. Here we summarize results from global fits to spatially integrated emission in six young SNRs, finding 10−4 ≲ ϵ e ≲ 0.05 and 0.001 ≲ ϵ B ≲ 0.1. These large variations might be put down to the differing ages and environments of these SNRs, so we conduct a detailed analysis of a single remnant, that of Kepler’s supernova. Both epsilons can be determined at seven different locations around the shock, and we find even larger ranges for both epsilons, as well as for their ratio (thus independent of the shock energy itself). We conclude that unknown factors have a large influence on the efficiency of both processes. Shock obliquity, upstream neutral fraction, or other possibilities need to be explored, while calculations assuming fixed values of the epsilons should be regarded as provisional.
AutoTune: Improving End-to-end Performance and Resource Efficiency for Microservice Applications

Most large web-scale applications are now built by composing collections (from a few up to 100s or 1000s) of microservices. Operators need to decide how many resources are allocated to each microservice, and these allocations can have a large impact on application performance. Manually determining allocations that are both cost-efficient and meet performance requirements is challenging, even for experienced operators. In this paper we present AutoTune, an end-to-end tool that automatically minimizes resource utilization while maintaining good application performance.
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples

Training images with data transformations have been suggested as contrastive examples to complement the testing set for generalization performance evaluation of deep neural networks (DNNs). In this work, we propose a practical framework ContRE (The word"contre"means"against"or"versus"in French.) that uses Contrastive examples for DNN geneRalization performance Estimation. Specifically, ContRE follows the assumption in contrastive learning that robust DNN models with good generalization performance are capable of extracting a consistent set of features and making consistent predictions from the same image under varying data transformations. Incorporating with a set of randomized strategies for well-designed data transformations over the training set, ContRE adopts classification errors and Fisher ratios on the generated contrastive examples to assess and analyze the generalization performance of deep models in complement with a testing set. To show the effectiveness and the efficiency of ContRE, extensive experiments have been done using various DNN models on three open source benchmark datasets with thorough ablation studies and applicability analyses. Our experiment results confirm that (1) behaviors of deep models on contrastive examples are strongly correlated to what on the testing set, and (2) ContRE is a robust measure of generalization performance complementing to the testing set in various settings.
EFPE: Efficient fully precessing eccentric gravitational waveforms for binaries with long inspirals

In this paper, we derive a set of equations of motions for binaries on eccentric orbits undergoing spin-induced precession that can efficiently be integrated on the radiation-reaction timescale. We find a family of solutions with a computation cost improved by a factor 10 50 down to ∼ 10 ms per waveform evaluation compared to waveforms obtained by directly integrating the precession equations, that maintain a mismatch of the order 10−4 10−6 for waveforms lasting a million orbital cycles and a thousand spin-induced precession cycles. We express it in terms of parameters that make the solution regular in the equal-mass limit, thus bypassing a problem of previous similar solutions. We point to ways in which the solution presented in this paper can be perturbed to take into account effects such as general quadrupole momenta and post-Newtonian corrections to the precession equations. This new waveform, with its improved efficiency and its accuracy, makes possible Bayesian parameter estimation using the full spin and eccentricity parameter volume for long lasting inspiralling signals such as stellar-origin black hole binaries observed by LISA.
Practical Transferability Estimation for Image Classification Tasks

Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transferring a source model (or source task) to a target task. Recent analytical transferability metrics have been widely used for source model selection and multi-task learning. A major challenge is how to make transfereability estimation robust under the cross-domain cross-task settings. The recently proposed OTCE score solves this problem by considering both domain and task differences, with the help of transfer experiences on auxiliary tasks, which causes an efficiency overhead. In this work, we propose a practical transferability metric called JC-NCE score that dramatically improves the robustness of the task difference estimation in OTCE, thus removing the need for auxiliary tasks. Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with a ground cost considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy of the matched labels. Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the auxiliary-task free version of OTCE for 7% and 12%, respectively, and is also more robust than other existing transferability metrics on average.
Modern compressive tomography for quantum information science

This review serves as a concise introductory survey of modern compressive tomography developed since 2019. These are schemes meant for characterizing arbitrary low-rank quantum objects, be it an unknown state, a process or detector, using minimal measuring resources (hence compressive) without any a priori assumptions (rank, sparsity, eigenbasis, etc.) about the quantum object. This paper contains a reasonable amount of technical details for the quantum-information community to start applying the methods discussed here. To facilitate the understanding of formulation logic and physics of compressive tomography, the theoretical concepts and important numerical results (both new and cross-referenced) shall be presented in a pedagogical manner.
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel i terative network p runing with uncertainty r egularization method for l ifelong s entiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with uncertainty regularization in an iterative manner, IPRLS can adapt a single BERT model to work with continuously arriving data from multiple domains while avoiding catastrophic forgetting and interference. Specifically, we leverage an iterative pruning method to remove redundant parameters in large deep networks so that the freed-up space can then be employed to learn new tasks, tackling the catastrophic forgetting problem. Instead of keeping the old-tasks fixed when learning new tasks, we also use an uncertainty regularization based on the Bayesian online learning framework to constrain the update of old tasks weights in BERT, which enables positive backward transfer, i.e. learning new tasks improves performance on past tasks while protecting old knowledge from being lost. In addition, we propose a task-specific low-dimensional residual function in parallel to each layer of BERT, which makes IPRLS less prone to losing the knowledge saved in the base BERT network when learning a new task. Extensive experiments on 16 popular review corpora demonstrate that the proposed IPRLS method significantly outperforms the strong baselines for lifelong sentiment classification. For reproducibility, we submit the code and data at: \urlhttps://github.com/siat-nlp/IPRLS .
Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this article, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that leverages the computing power of the cloud to compute optimal control based on a high-fidelity nonlinear model (thus, more accurate) but is subject to communication delays with a local MPC that relies on simplified linear dynamics due to limited local computation capability (thus, less accurate) while has timely feedback. Unlike traditional cloud-based control that treats the cloud as a powerful, remote, and sole controller in a networked control system setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks with explicit consideration for model mismatches and errors due to request-response communication delays. We analyze stability-type properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-type conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials.
Dipole blockade without dipole-dipole interaction

C. J. Zhu, 2, 3 W. Li, Y. P. Yang, ∗ and G. S. Agarwal † School of Physical Science and Technology, Soochow University, Suzhou 215006, China MOE Key Laboratory of Advanced Micro-Structured Materials, School of Physics Science and Engineering, Tongji University, Shanghai, China 200092 Collaborative Innovation Center of Light Manipulations and Applications, Shandong Normal University, Jinan 250358, China Institute for Quantum Science and Engineering, Department of Biological and Agricultural Engineering, Department of Physics and Astronomy, Texas A&M University, College Station, Texas 77843, USA (Dated: June 22, 2021)
Disordered Phase in Ising and Metastability in Cellular Potts Models Hint at Glassy Dynamics

In this paper, quantum algorithms are to be used to simulate glassy systems in toy models. To look for glassy behavior, the energy landscape and spin configurations of the transverse field Ising model in a longitudinal field are studied. The Variational Quantum Eigensolver (VQE) is used to obtain the ground-state energies and corresponding eigenstates for a $6 \times 6$ Ising lattice using 36 qubits and a 1-dimensional Ising chain of length 25. For the $8 \times 8$ Cellular Potts model, the original Hamiltonian is converted to an Ising formulation for the VQE to reduce to its ground state. The energy change during minimization is carefully analyzed to find whether the effects of interfacial tension among cells could probably induce glassiness in the cell system.
Better Training using Weight-Constrained Stochastic Dynamics

We employ constraints to control the parameter space of deep neural networks throughout training. The use of customized, appropriately designed constraints can reduce the vanishing/exploding gradients problem, improve smoothness of classification boundaries, control weight magnitudes and stabilize deep neural networks, and thus enhance the robustness of training algorithms and the generalization capabilities of neural networks. We provide a general approach to efficiently incorporate constraints into a stochastic gradient Langevin framework, allowing enhanced exploration of the loss landscape. We also present specific examples of constrained training methods motivated by orthogonality preservation for weight matrices and explicit weight normalizations. Discretization schemes are provided both for the overdamped formulation of Langevin dynamics and the underdamped form, in which momenta further improve sampling efficiency. These optimization schemes can be used directly, without needing to adapt neural network architecture design choices or to modify the objective with regularization terms, and see performance improvements in classification tasks.
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning

Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated learning remains limited due to high cost incurred by conventional privacy-preserving methods. To address the problem, we propose two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy and lower overhead than encryption-based techniques. Developed independently, the second protocol FedXGBoost-LDP is heuristically designed with noise perturbation for local differential privacy, and empirically evaluated on real-world and synthetic datasets.
Multi-Pair Text Style Transfer on Unbalanced Data

Text-style transfer aims to convert text given in one domain into another by paraphrasing the sentence or substituting the keywords without altering the content. By necessity, state-of-the-art methods have evolved to accommodate nonparallel training data, as it is frequently the case there are multiple data sources of unequal size, with a mixture of labeled and unlabeled sentences. Moreover, the inherent style defined within each source might be distinct. A generic bidirectional (e.g., formal $\Leftrightarrow$ informal) style transfer regardless of different groups may not generalize well to different applications. In this work, we developed a task adaptive meta-learning framework that can simultaneously perform a multi-pair text-style transfer using a single model. The proposed method can adaptively balance the difference of meta-knowledge across multiple tasks. Results show that our method leads to better quantitative performance as well as coherent style variations. Common challenges of unbalanced data and mismatched domains are handled well by this method.
The Design and Integrated Performance of SPT-3G

SPT-3G is the third survey receiver operating on the South Pole Telescope dedicated to high-resolution observations of the cosmic microwave background (CMB). Sensitive measurements of the temperature and polarization anisotropies of the CMB provide a powerful data set for constraining cosmology. Additionally, CMB surveys with arcminute-scale resolution are capable of detecting galaxy clusters, millimeter-wave bright galaxies, and a variety of transient phenomena. The SPT-3G instrument provides a significant improvement in mapping speed over its predecessors, SPT-SZ and SPTpol. The broadband optics design of the instrument achieves a 430 mm diameter image plane across observing bands of 95, 150, and 220 GHz, with 1.2′ FWHM beam response at 150 GHz. In the receiver, this image plane is populated with 2690 dual-polarization, trichroic pixels (∼16,000 detectors) read out using a 68× digital frequency-domain multiplexing readout system. In 2018, SPT-3G began a multiyear survey of 1500 deg2 of the southern sky. We summarize the unique optical, cryogenic, detector, and readout technologies employed in SPT-3G, and we report on the integrated performance of the instrument.