Miracle-dz/UniFAR-FTUs huggingface dataset:
i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery

We propose a novel, structured pruning algorithm for neural networks—the i terative, Spa rse S tructured P runing algorithm, dubbed as i-SpaSP. Inspired by ideas from sparse signal recovery, i-SpaSP operates by iteratively identifying a larger set of important parameter groups (e.g., ﬁlters or neurons) within a network that contribute most to the residual between pruned and dense network output, then thresholding these groups based on a smaller, pre-deﬁned pruning ratio. For both two-layer and multi-layer network architectures with ReLU activations, we show the error induced by pruning with i-SpaSP decays polynomially, where the degree of this polynomial becomes arbitrarily large based on the sparsity of the dense network’s hidden representations. In our experiments, i-SpaSP is evaluated across a variety of datasets (i.e., MNIST, ImageNet, and XNLI) and architectures (i.e., feed forward networks, ResNet34, MobileNetV2, and BERT), where it is shown to discover high-performing sub-networks and improve upon the pruning efﬁciency of provable baseline methodologies by several orders of magnitude. Put simply, i-SpaSP is easy to implement with automatic differentiation, achieves strong empirical results, comes with theoretical convergence guarantees, and is efﬁcient, thus distinguishing itself as one of the few computationally efﬁcient, practical, and provable pruning algorithms.
A New Measure of Model Redundancy for Compressed Convolutional Neural Networks

While recently many designs have been proposed to improve the model efficiency of convolutional neural networks (CNNs) on a fixed resource budget, theoretical understanding of these designs is still conspicuously lacking. This paper aims to provide a new framework for answering the question: Is there still any remaining model redundancy in a compressed CNN? We begin by developing a general statistical formulation of CNNs and compressed CNNs via the tensor decomposition, such that the weights across layers can be summarized into a single tensor. Then, through a rigorous sample complexity analysis, we reveal an important discrepancy between the derived sample complexity and the naive parameter counting, which serves as a direct indicator of the model redundancy. Motivated by this finding, we introduce a new model redundancy measure for compressed CNNs, called the $K/R$ ratio, which further allows for nonlinear activations. The usefulness of this new measure is supported by ablation studies on popular block designs and datasets.
Refined Commonsense Knowledge From Large-Scale Web Contents

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O. This paper presents a method called Ascent++ to automatically build a large-scale knowledge base (KB) of CSK assertions, with refined expressiveness and both better precision and recall than prior works. Ascent++ goes beyond SPO triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter is essential to express the temporal and spatial validity of assertions and further qualifiers. Furthermore, Ascent++ combines open information extraction (OpenIE) with judicious cleaning and ranking by typicality and saliency scores. For high coverage, our method taps into the large-scale crawl C4 with broad web contents. The evaluation with human judgments shows the superior quality of the Ascent++ KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent++. A web interface, data, and code can be accessed at https://ascentpp.mpi-inf.mpg. de/.
Quantifying the breakdown of the rotating-wave approximation in single-photon superradiance

We study quantitatively the breakdown of the rotating-wave approximation (RWA) when calculating collective light emission by quantum emitters, in particular in the weak-excitation limit. Our starting point is a known multiple-scattering formalism where the full light–matter interaction leads to induced inter-emitter interactions described by the classical Green function of inhomogeneous dielectric media. When making the RWA in the light–matter interaction, however, these induced interactions differ from the classical Green function, and for free space we find a reduction of the interatomic interaction strength by up to a factor of two. By contrast, for the corresponding scalar model the relative RWA error for the inter-emitter interaction even diverges in the near field. For two identical emitters, the errors due to the RWA in collective light emission will show up in the emission spectrum, but not in the sub- and superradiant decay rates. In case of two non-identical emitters, also the collective emission rates will differ by making the RWA. For three or more identical emitters, the RWA errors in the interatomic interaction in general affect both the collective emission spectra and the collective decay rates. Ring configurations with discrete rotational symmetry are an interesting exception. Interestingly, the maximal errors in the collective decay rates due to making the RWA occur for finite emitter separations.
Toward a resolution of the NN controversy

Amy Nicholson,a,i,∗ Evan Berkowitz, John Bulava, j Chia Cheng Chang, M.A. Clark, Andrew D. Hanlon, f Ben Hörz, Dean Howarth, Christopher Körber, Wayne Tai Lee, Aaron S. Meyer, Henry Monge-Camacho, Colin Morningstar, Enrico Rinaldi, Pavlos Vranas and André Walker-Loud Department of Physics and Astronomy, University of North Carolina, Chapel Hill, NC 27516-3255, USA Intel Corporation, Santa Clara, CA 95054, USA Physics Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA Physics Department, University of Michigan, Ann Arbor, MI 48109, USA Theoretical Quantum Physics Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan f Physics Department, Brookhaven National Laboratory, Upton, New York 11973, USA Institut für Theoretische Physik II, Ruhr-Universität Bochum, D-44780 Bochum, Germany Department of Physics, University of California, Berkeley, CA 94720, USA Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA CP3 -Origins & Dept. of Mathematics and Computer Science, University of Southern Denmark Campusvej 55, 5230 Odense M, Denmark NVIDIA Corporation, Santa Clara, CA 95050, USA Department of Statistics, Columbia University, New York, NY 10027, USA Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA Institut für Kernphysik and Institute for Advanced Simulation, Forschungszentrum Jülich, 54245 Jülich, Germany Escuela de Física, Universidad de Costa Rica, San José, San Pedro, 11501, Costa Rica iTHEMS, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Center for Quantum Computing, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Thermodynamic Constraints on Quantum Information Gain and Error Correction: A Triple Trade-Off

Quantum error correction (QEC) is a procedure by which the quantum state of a system is protected against a known type of noise, by preemptively adding redundancy to that state. Such a procedure is commonly used in quantum computing when thermal noise is present. Interestingly, thermal noise has also been known to play a central role in quantum thermodynamics (QTD). This fact hints at the applicability of certain QTD statements in the QEC of thermal noise, which has been discussed previously in the context of Maxwell's demon. In this article, we view QEC as a quantum heat engine with a feedback controller (i.e., a demon). We derive an upper bound on the measurement heat dissipated during the error-identification stage in terms of the Groenewold information gain, thereby providing the latter with a physical meaning also when it is negative. Further, we derive the second law of thermodynamics in the context of this QEC engine, operating with general quantum measurements. Finally, we show that, under a set of physically motivated assumptions, this leads to a fundamental triple trade-off relation, which implies a trade-off between the maximum achievable fidelity of QEC and the super-Carnot efficiency that heat engines with feedback controllers have been known to possess. A similar trade-off relation occurs for the thermodynamic efficiency of the QEC engine and the efficacy of the quantum measurement used for error identification.
An Introduction to Motility-Induced Phase Separation

Motility-induced phase separation, MIPS, is arguably the most well-studied collective phenomenon occurring in active matter without alignment interactions (scalar active matter). Its basic origin is simple: since self-propelled particles accumulate where they move slowly, having a propulsion speed that decreases steeply enough with density, due to collisions or chemical interactions, leads to a feedback loop that induces the formation of a dense phase. In these notes, I will discuss some of the main theoretical and computational efforts that have been made over the last decade in understanding the basic structural and dynamical properties of MIPS phase coexistence in microscopic active particle models.
Opinion Extraction as A Structured Sentiment Analysis using Transformers

Relationship extraction and named entity recognition have always been considered as two distinct tasks that require different input data, labels, and models. However, both are essential for structured sentiment analysis. We believe that both tasks can be combined into a single stacked model with the same input data. We performed different experiments to find the best model to extract multiple opinion tuples from a single sentence. The opinion tuples will consist of holders, targets, and expressions. With the opinion tuples, we will be able to extract the relationship we need.
A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation

Echo chambers on social media are a significant problem that can elicit a number of negative consequences, most recently affecting the response to COVID-19. Echo chambers promote conspiracy theories about the virus and are found to be linked to vaccine hesitancy, less compliance with mask mandates, and the practice of social distancing. Moreover, the problem of echo chambers is connected to other pertinent issues like political polarization and the spread of misinformation. An echo chamber is defined as a network of users in which users only interact with opinions that support their pre-existing beliefs and opinions, and they exclude and discredit other viewpoints. This survey aims to examine the echo chamber phenomenon on social media from a social computing perspective and provide a blueprint for possible solutions. We survey the related literature to understand the attributes of echo chambers and how they affect the individual and society at large. Additionally, we show the mechanisms, both algorithmic and psychological, that lead to the formation of echo chambers. These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily. While it is immensely challenging to mitigate internal biases, there has been great efforts seeking to mitigate the bias of recommender systems. These recommender systems take advantage of our own biases to personalize content recommendations to keep us engaged in order to watch more ads. Therefore, we further investigate different computational approaches for echo chamber detection and prevention, mainly based around recommender systems.
Resolving Hall and dissipative viscosity ambiguities via boundary effects

We examine the physical implications of the viscous redundancy of two-dimensional anisotropic fluids, where different components of the viscosity tensor lead to identical effects in the bulk of a system [Rao and Bradlyn, Phys. Rev. X $\textbf{10}$, 021005 (2020)]. We first re-introduce the redundancy, show how it reflects a lack of knowledge of microscopic information of a system, and give microscopic examples. Next, we show that fluid flow in systems with a boundary can distinguish between otherwise redundant viscosity coefficients. In particular, we show how the dispersion and damping of gravity-dominated surface waves can be used to resolve the redundancies between both dissipative and Hall viscosities, and discuss how these results apply to recent experiments in chiral active fluids with nonvanishing Hall viscosity. Our results highlight the importance of divergenceless, magnetization-like contributions to the stress (which we dub ``contact terms''). Finally, we apply our results to the hydrodynamics of quantum Hall fluids, and show that the extra contribution to the action that renders the bulk Wen-Zee action gauge invariant in systems with a boundary can be reinterpreted in terms of the bulk viscous redundancy.
H-band Light Curves of Milky Way Cepheids via Difference Imaging

We present H-band light curves of Milky Way classical Cepheids observed as part of the Dark Energy, H 0, and peculiar Velocities using Infrared Light from Supernovae survey with the Wide-Field Infrared Camera on the United Kingdom Infrared Telescope. Due to the crowded nature of these fields caused by defocusing the Camera, we performed difference-imaging photometry by modifying a pipeline originally developed to analyze images from the Transiting Exoplanet Survey Satellite. We achieved a photometric precision in line with expectations from photon statistics, reaching 0.01 mag for 8 ≲ H ≲ 11 mag. We used the resulting Cepheid light curves to derive corrections to “mean light” for random-phase Hubble Space Telescope observations in F160W. We find good agreement with previous phase corrections based on VI light curves from the literature, with a mean difference of −1 ± 6 mmag.
Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition

A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously. However, selecting the optimal set of techniques to use in a specific deployment environment a-priori is a difficult and unresolved challenge. Further, to the best of our knowledge, no method exists which can select a set of techniques on a frame-by-frame basis in response to image-to-image variations. In this work, we propose an unsupervised algorithm that finds the most robust set of VPR techniques to use in the current deployment environment, on a frame-by-frame basis. The selection of techniques is determined by an analysis of the similarity scores between the current query image and the collection of database images and does not require ground-truth information. We demonstrate our approach on a wide variety of datasets and VPR techniques and show that the proposed dynamic multi-process fusion (Dyn-MPF) has superior VPR performance compared to a variety of challenging competitive methods, some of which are given an unfair advantage through access to the ground-truth information.
Development of a Very Small Aero-Engine

The presented work is concerned with the development of a very small aero-engine with 100 N thrust. First a commercially available 30 N-model jet engine was studied on a test bench. Based on the measurements, a calculation procedure was built to represent the measurements. Then the calculation was scaled to 100 N thrust. The scaled engine was designed in a 3D-CAD system, in which the stress analysis of selected parts, mainly the turbine wheel were performed. After the engine parts were manufactured and assembled, the new aero-engine was tested on the test bench as comparison with the predicted values.Copyright © 2000 by ASME
End-To-End Alexa Device Arbitration

We introduce a variant of the speaker localization problem, which we call device arbitration. In the device arbitration problem, a user utters a keyword that is detected by multiple distributed microphone arrays (smart home devices), and we want to determine which device was closest to the user. Rather than solving the full localization problem, we propose an end-to-end machine learning system. This system learns a feature embedding that is computed independently on each device. The embeddings from each device are then aggregated together to produce the final arbitration decision. We use a large-scale room simulation to generate training and evaluation data, and compare our system against a signal-processing baseline.
Mean Hitting Time on Recursive Growth Tree Network

Abstract: In this paper, we are concerned with mean hitting time 〈H〉 for random walks on recursive growth tree networks that are built based on an arbitrary tree as the seed via implementing various primitive graphic operations, and propose a series of combinatorial techniques that are called Mapping Transformation to exactly determine the associated 〈H〉−polynomial. Our formulas can be able to completely cover the previously published results in some wellstudied and specific cases where a single edge or a star is often chose to serve as seed for creating recursive growth models. The techniques proposed are more convenient than the commonly-used spectral methods mainly because of getting around the operations of matrix inversion and multiplication. Accordingly, our results can be extended for both many other stochastic models including BA-scale-free tree and random uniform tree as well as graphs of great interest consisting of line graph of tree and Vicsek fractal network to derive numerical solutions of related structural parameters. And then, the closed-form solutions of two extensions of Wiener index with respect to multiplicative and additive degrees on an arbitrary tree are conveniently obtained as well. In addition, we discuss some extremal problems of random walks on tree networks and outline the related research directions in the next step.
Chern characters for supersymmetric field theories

We construct a map from $d|1$-dimensional Euclidean field theories to complexified K-theory when $d=1$ and complex analytic elliptic cohomology when $d=2$. This provides further evidence for the Stolz--Teichner program, while also identifying candidate geometric models for Chern characters within their framework. The construction arises as a higher-dimensional and parameterized generalization of Fei Han's realization of the Chern character in K-theory as dimensional reduction for $1|1$-dimensional Euclidean field theories. In the elliptic case, the main new feature is a subtle interplay between the geometry of the super moduli space of $2|1$-dimensional tori and the derived geometry of complex analytic elliptic cohomology. As a corollary, we obtain an entirely geometric proof that partition functions of $\mathcal{N}=(0,1)$ supersymmetric quantum field theories are weak modular forms, following a suggestion of Stolz and Teichner.
Real-time measurement of the full spatiotemporal field of a single terahertz pulse by pulsed digital holography

Up to now, a variety of methods have been developed for the single-shot THz detection, which include spectral encoding technique [2-3], optical streak camera [4], non-collinear geometry spatial encoding [5], non-collinear cross correlation technique [6], retrieval algorithm based on in-line spectral interferometry [7], two-dimensional electro-optic imaging with dual echelons [8], tilted front collinear geometry [9], etc. For a single-shot characterization of THz pulse, all of the schemes mentioned above can be, however, only employed to measure the electric field of a single-shot THz either in its spatial or temporal domain, respectively, in real time. In this paper, we describe a method for a single-shot recording of the full spatiotemporal electric field, E(x, y, t), of freely propagating terahertz pulse based on the electro-optic (E-O) sampling technique and the pulsed digital holographic approach. From a series of sub-holograms recorded digitally, the complete THz electric field E(x, y, t) can be recovered by the following digital reconstruction algorithm. The spatial and temporal resolutions are limited by the wavelength of terahertz pulse and the probe pulse duration, respectively. Our new method will open a possibility of a full characterization of the three-dimensional THz field E(x, y, t) in a single-shot mode.
1 ns time to digital converters for the KM3NeT data readout system

The KM3NeT collaboration aims at the construction of a multi-km3 high-energy neutrino telescope in the Mediterranean Sea consisting of thousands of glass spheres (nodes), each of them containing 31 photomultiplier (PMT) of small photocathode area. The readout and data acquisition system of KM3NeT has to collect, treat and send to shore, in an economic way, the enormous amount of data produced by the photomultipliers. For this purpose, 31 high-resolution time-interval measuring channels are implemented on the Field-Programmable Gate Arrays (FPGA) based on Time to Digital Converter (TDC). TDC are very common devices in particles physics experiments. Architectures with low resources occupancy are desirable allowing the implementation of other instrumentation, communication and synchronization systems on the same device. The required resolution to measure both, time of flight and timestamp must be 1 ns. A 4-Oversampling technique with two high frequency clocks is used to achieve this resolution. The proposed TDC firmware is developed using very few resources in Xilinx Kintex-7.
Neural Radiance Fields for Outdoor Scene Relighting

Photorealistic editing of outdoor scenes from photographs requires a profound understanding of the image formation process and an accurate estimation of the scene geometry, reﬂectance and illumination. A delicate manipulation of the lighting can then be performed while keeping the scene albedo and geometry unaltered. We present NeRF-OSR, i.e., the ﬁrst approach for outdoor scene re-lighting based on neural radiance ﬁelds. In contrast to the prior art, our technique allows simultaneous editing of both scene illumination and camera viewpoint using only a collection of outdoor photos shot in uncontrolled settings. Moreover, it enables direct control over the scene illumination, as deﬁned through a spherical harmonics model. It also includes a dedicated network for shadow reproduction, which is crucial for high-quality outdoor scene relighting. To evaluate the proposed method, we collect a new benchmark dataset of several outdoor sites, where each site is photographed from multiple viewpoints and at different timings. For each timing, a 360 ◦ environment map is captured together with a colour-calibration chequerboard to allow accurate numerical evaluations on real data against ground truth. Comparisons against state of the art show that NeRF-OSR enables controllable lighting and viewpoint editing at higher quality and with realistic self-shadowing reproduction. Our method and the dataset will be made publicly available at https://4dqv.mpi-inf.mpg