Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch Normalization Is Blind to the First and Second Derivatives of the Loss

We prove that when we do the Taylor series expansion of the loss function, the BN operation will block the influence of the first-order term and most influence of the second-order term of the loss. We also find that such a problem is caused by the standardization phase of the BN operation. We believe that proving the blocking of certain loss terms provides an analytic perspective for potential detects of a deep model with BN operations, although the blocking problem is not fully equivalent to significant damages in all tasks on benchmark datasets. Experiments show that the BN operation significantly affects feature representations in specific tasks.
On the Value of Retransmissions for Age of Information in Random Access Networks without Feedback

We focus on a slotted ALOHA system without feedback, in which nodes transmit time-stamped updates to a common gateway. Departing from the classical generate-at-will model, we assume that each transmitter may not always have fresh information to deliver, and tackle the fundamental question of whether sending stale packets can be beneficial from an age-of-information standpoint. Leaning on a signal-flow-graph analysis of Markov processes the study reveals that, when packets can be lost due to channel impairments, retransmissions can indeed lower AoI for low generation rates of new information, although at a cost in terms of throughput.
A Novel Control-Oriented Cell Transmission Model Including Service Stations on Highways

In this paper, we propose a novel model that describes how the traffic evolution on a highway stretch is affected by the presence of a service station. The presented model enhances the classical Cell Transmission Model (CTM) dynamics by adding the dynamics associated with the service stations, where the vehicles may stop before merging back in the main stream. We name it Cell Transmission Model with service station (CTM-s). We discuss its flexibility in describing different complex scenarios where multiple stations are characterized by different drivers’ average stopping times corresponding to different services. The model has been developed to help designing control strategies aimed to decrease traffic congestion. Thus, we discuss how classical control schemes can interact with the proposed CTM-s. Finally, we demonstrate the proposed model through numerical simulations and assess the effects of service stations on traffic evolution, which appear to be beneficial especially for relatively short congested periods.
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch

Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase. However, temporally correlated frames are also available at inference time for many clinical applications, e.g., surgical navigation. The vast majority of monocular systems do not exploit this valuable signal that could be deployed to enhance the depth estimates. Those that do, achieve only limited gains due to the unique challenges in endoscopic scenes, such as low and homogeneous textures and inter-frame brightness fluctuations. In this work, we present SMUDLP, a novel and unsupervised paradigm for multi-frame monocular endoscopic depth estimation. The SMUDLP integrates a learnable patchmatch module to adaptively increase the discriminative ability in low-texture and homogeneous-texture regions, and enforces cross-teaching and self-teaching consistencies to provide efficacious regularizations towards brightness fluctuations. Our detailed experiments on both SCARED and Hamlyn datasets indicate that the SMUDLP exceeds state-of-the-art competitors by a large margin, including those that use single or multiple frames at inference time. The source code and trained models will be publicly available upon the acceptance.
Set Based Stochastic Subsampling

Deep models are designed to operate on huge volumes of high dimensional data such as images. In order to reduce the volume of data these models must process, we propose a set-based two-stage end-to-end neural subsampling model that is jointly optimized with an arbitrary downstream task network (e.g. classiﬁer). In the ﬁrst stage, we efﬁciently subsample candidate elements using conditionally independent Bernoulli random variables by capturing coarse grained global information using set encoding functions, followed by conditionally dependent autoregressive subsampling of the candidate elements using Categorical random variables by modeling pair-wise interactions using set attention networks in the second stage. We apply our method to feature and instance selection and show that it outperforms the relevant baselines under low subsampling rates on a variety of tasks including image classiﬁcation, image reconstruction, function reconstruction and few-shot classiﬁcation. Additionally, for nonparametric models such as Neural Processes that require to leverage the whole training data at inference time, we show that our method en-hances the scalability of these models.
Hidden symmetries from distortions of the conformal structure

It is well established that the mass parameter breaks the conformal symmetries in the case of geodesic motion. The proper conformal Killing vectors cease to generate conserved charges when non-null geodesics are considered. We examine how the introduction of the mass is actually related to the appearance of appropriate distortions in the conformal sector, which lead to new conservation laws. As a prominent example we use a general pp-wave metric, which exploits this property to the maximum. We study the necessary geometric conditions, so that such types of distortions are applicable. We show that the relative vectors are generators of disformal transformations and prove their connection to higher order (hidden) symmetries. Except from the pp-wave geometry, we also provide an additional example in the form of the de Sitter metric. Again, the proper conformal Killing vectors can be appropriately distorted to generate conserved quantities for massive geodesics. Subsequently, we proceed by introducing an additional symmetry breaking eﬀect. The latter is realized by considering a Bogoslovsky type of line-element, which involves a Lorentz violating parameter. We utilize once more the pp-wave case as a guide to study how the broken symmetries - this time also related to Killing vectors - are substituted by distortions of the original generators. We further analyze and discuss the necessary geometric conditions that lead to the emergence of these distortions.
Manuscript on XXX pdfauthor=et al. and Uwe Thiele

Tobias Frohoff-Hülsmann,1, ∗ Max Philipp Holl,1, † Edgar Knobloch,2, ‡ Svetlana V. Gurevich,1, 3, § and Uwe Thiele1, 3, 4, ¶ 1Institut für Theoretische Physik, Westfälische Wilhelms-Universität Münster, Wilhelm-Klemm-Str. 9, 48149 Münster, Germany 2Department of Physics, University of California at Berkeley, Berkeley CA 94720, USA 3Center for Nonlinear Science (CeNoS), Westfälische Wilhelms-Universität Münster, Corrensstr. 2, 48149 Münster, Germany 4Center for Multiscale Theory and Computation (CMTC), Westfälische Wilhelms-Universität, Corrensstr. 40, 48149 Münster, Germany Abstract We demonstrate that selected, commonly used continuum models of active matter exhibit nongeneric behavior: each model supports asymmetric but stationary localized states even when the gradient structure of the model is broken by activity. As a result such states only begin to drift following a drift-transcritical bifurcation as the activity increases. We explain the origin of this nongeneric behavior, elucidate the model structure responsible for it, and identify the types of additional terms that render the model generic, i.e. with asymmetric states that appear via drift-pitchfork bifurcations and are at rest at most at isolated values of the activity parameter. We give detailed illustrations of our results using numerical continuation of spatially localized structures in both generic and nongeneric cases.
Universality of group convolutional neural networks based on ridgelet analysis on groups

We show the universality of depth-2 group convolutional neural networks (GCNNs) in a unified and constructive manner based on the ridgelet theory. Despite widespread use in applications, the approximation property of (G)CNNs has not been well investigated. The universality of (G)CNNs has been shown since the late 2010s. Yet, our understanding on how (G)CNNs represent functions is incomplete because the past universality theorems have been shown in a case-by-case manner by manually/carefully assigning the network parameters depending on the variety of convolution layers, and in an indirect manner by converting/modifying the (G)CNNs into other universal approximators such as invariant polynomials and fully-connected networks. In this study, we formulate a versatile depth-2 continuous GCNN $S[\gamma]$ as a nonlinear mapping between group representations, and directly obtain an analysis operator, called the ridgelet trasform, that maps a given function $f$ to the network parameter $\gamma$ so that $S[\gamma]=f$. The proposed GCNN covers typical GCNNs such as the cyclic convolution on multi-channel images, networks on permutation-invariant inputs (Deep Sets), and $\mathrm{E}(n)$-equivariant networks. The closed-form expression of the ridgelet transform can describe how the network parameters are organized to represent a function. While it has been known only for fully-connected networks, this study is the first to obtain the ridgelet transform for GCNNs. By discretizing the closed-form expression, we can systematically generate a constructive proof of the $cc$-universality of finite GCNNs. In other words, our universality proofs are more unified and constructive than previous proofs.
To Catch A Chorus, Verse, Intro, or Anything Else: Analyzing a Song with Structural Functions

Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., ‘A’, ‘B’, and ‘C’). However, explicitly identifying the function of each segment (e.g., ‘verse’ or ‘chorus’) is rarely attempted, but has many applications. We introduce a multi-task deep learning framework to model these structural semantic labels directly from audio by estimating "verseness," "chorusness," and so forth, as a function of time. We propose a 7-class taxonomy (i.e., intro, verse, chorus, bridge, outro, instrumental, and silence) and provide rules to consolidate annotations from four disparate datasets. We also propose to use a spectral-temporal Transformer-based model, called SpecTNT, which can be trained with an additional connectionist temporal localization (CTL) loss. In cross-dataset evaluations using four public datasets, we demonstrate the effectiveness of the SpecTNT model and CTL loss, and obtain strong results overall: the proposed system outperforms state-of-the-art chorus-detection and boundary-detection methods at detecting choruses and boundaries, respectively.
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification

This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the C processes are dependent by construction without needing a mixing matrix, 3) training and making predictions is very efficient since the number of Gaussian Processes (GP) operations (e.g. inverting the inducing point's covariance matrix) do not depend on the number of processes. This makes the ETGP particularly suited for multi-class problems with a very large number of classes, which are the problems studied in this work. ETGPs exploit the recently proposed Transformed Gaussian Process (TGP), a stochastic process specified by transforming a Gaussian Process using an invertible transformation. However, unlike TGPs, ETGPs are constructed by transforming a single sample from a GP using C invertible transformations. We derive an efficient sparse variational inference algorithm for the proposed model and demonstrate its utility in 5 classification tasks which include low/medium/large datasets and a different number of classes, ranging from just a few to hundreds. Our results show that ETGPs, in general, outperform state-of-the-art methods for multi-class classification based on GPs, and have a lower computational cost (around one order of magnitude smaller).
Correlations among neutron-proton and neutron-deuteron elastic scattering observables

We employ two models of the nucleon-nucleon force: the OPE-Gaussian as well as the chiral N 4 LO and N 4 LO + interactions with semilocal regularization in momentum space to study correlations among two-nucleon and three-nucleon elastic scattering observables. These models contain a number of free parameters whose values and covariance matrices are evaluated from a ﬁt to the two-nucleon data. Such detailed knowledge of parameters allows us to create, using various sets of statistically generated parameters, numerous versions of these potentials and next apply them to two- and three-nucleon scattering to make predictions of various observables at the reaction energies up to 200 MeV. This permits a systematic analysis of correlations among two-nucleon and three-nucleon observables, basing on a relatively big sample of predictions. We found that most observables in neutron-proton and neutron-deuteron systems are uncorrelated, but there are exceptions revealing strong correlations, which depend on the reaction energy and scattering angle. This information may be useful for precise ﬁxing free parameters of two-nucleon and three-nucleon forces and for understanding dependencies and correlations between potential parameters and observables.
Ca II Triplet Spectroscopy of Small Magellanic Cloud Red Giants. VI. Analysis of chemical properties of the Main Body

We derived radial velocities and CaT metallicity of more than 150 red giants stars in six SMC star clusters and their surrounding fields, with the instrument GMOS on GEMINI-S. The mean cluster radial velocity and metallicity were obtained with mean errors of 2.2 km\,s$^{-1}$ and 0.03 dex, while the mean field metallicities have a mean error of 0.13 dex. We add this information to that available for another 51 clusters and 30 fields with CaT metallicities on the same scale. Using this expanded sample we analize the chemical properties of the SMC Main Body, defined as the inner 3.4 degrees in semimajor axis. We found a high probability that the metallicity distribution of the Main Body clusters is bimodal with a metal-rich and a metal-poor cluster group, having mean metallicities with a dispersion of $\mu = -0.80$, $\sigma = 0.06$ and $\mu = -1.15$, $\sigma = 0.10$ dex, respectively. On the other hand, Main Body field stars show a unimodal metallicity distribution peaking at $[Fe/H] \sim -1$ and dispersion of $0.3$. Neither metal-rich nor metal-poor clusters present a metallicity gradient. However the full Main Body cluster sample and field stars have a negative metallicity gradient consistent with each other, but the one corresponding to clusters has a large error due to the large metallicity dispersion present in the clusters studied in that region. Metal-rich clusters present a clear age-metallicity relation, while metal-poor clusters present no chemical enrichment throughout the life of the galaxy. We present observational evidence that the chemical enrichment is complex in the SMC Main Body. Two cluster groups with potential different origins could be coexisting in the Main Body. More data with precise and homogeneous metallicities and distances are needed and dynamical simulations are required to understand possible different origins for the two possible cluster groups.
Overcoming Congestion in Distributed Coloring

We present a new technique to efficiently sample and communicate a large number of elements from a distributed sampling space. When used in the context of a recent Local algorithm for (degree +1)-list-coloring (D1LC), this allows us to solve D1LC in O(log5 logn) Congest rounds, and in only O(log* n) rounds when the graph has minimum degree Ω(log7 n), w.h. p. The technique also has immediate applications in testing some graph properties locally, and for estimating the sparsity/density of local subgraphs in O(1) Congest rounds, w.h. p.
SEREN: Knowing When to Explore and When to Exploit

Efﬁcient reinforcement learning (RL) involves a trade-off between “exploitative" actions that maximise expected reward and “explorative’" ones that sample un-visited states. To encourage exploration, recent approaches proposed adding stochasticity to actions, separating exploration and exploitation phases, or equating reduction in uncertainty with reward. However, these techniques do not neces-sarily offer entirely systematic approaches making this trade-off. Here we in-troduce SE lective R einforcement E xploration N etwork (SEREN) that poses the exploration-exploitation trade-off as a game between an RL agent— Exploiter , which purely exploits known rewards, and another RL agent— Switcher , which chooses at which states to activate a pure exploration policy that is trained to minimise system uncertainty and override Exploiter . Using a form of policies known as impulse control , Switcher is able to determine the best set of states to switch to the exploration policy while Exploiter is free to execute its actions everywhere else. We prove that SEREN converges quickly and induces a natural schedule towards pure exploitation. Through extensive empirical studies in both discrete (MiniGrid) and continuous (MuJoCo) control benchmarks, we show that SEREN can be readily combined with existing RL algorithms to yield signiﬁcant improvement in performance relative to state-of-the-art algorithms.
Zero field optically pumped magnetometer with independent dual-mode operation

We propose a dual-mode optically pumped magnetometer (OPM) that can flexibly switch between single-beam modulation mode and double-beam DC mode. Based on a 4 mm × 4 mm × 4 mm miniaturized vapor cell, the double-beam DC mode achieves a sensitivity of 7 fT = Hz 1 = 2 with probe noise below 4 fT = Hz 1 = 2 and working bandwidth over 65 Hz. This mode is designed to precisely measure the noise floor of a mu-metal magnetic shield. The single-beam modulation mode (sensitivity 20 fT = Hz 1 = 2 ) exhibits bandwidth characteristics suitable for biomagnetic measurements. Thus, our design is suitable for a miniaturized OPM with multiple functions, including magnetic-shield background noise measurement and medical imaging. DOI:
Application of discrete-event simulation to investigate effects of client order behaviour on off-site manufacturing performance in Australia

ABSTRACT Off-site manufacturing (OSM) has implied capabilities that could enhance housing supply in Australia. The OSM system requires the efficient management of two concurrent working locations: the off-site fabrication facility and the construction site. Client order behaviour impacts on the OSM system. Accordingly, client order information flow is an essential component for efficiently managing this system. This paper investigates the effects of client order behaviour on the performance of OSM within the Australian housebuilding context using discrete-event simulation (DES). The data used to develop the conceptual model were based on a review of 21 related technical documents and interviews with 13 OSM experts in Australia. The operational data used for executing the simulation model were collected through a questionnaire survey of 30 builders as well as three one-day site visits to the fabrication facilities and construction sites of three major building organisations in Australia. The simulation model assisted in experimentation with four scenarios that represent client order interaction in the OSM system in Australia. The model simulation results showed noteworthy improvements in house completion time and the number of completed client orders per year.
Recovering Factor Volatility∗

We show that any factor structure for stock returns naturally induces a factor structure for return volatility. We use this structure to propose a methodology for estimating forward-looking variances and covariances of both factors and individual assets from option prices at a high frequency. We implement the model empirically and show that our forward-looking volatility estimates provide useful implications for optimal portfolio choice and the prediction of jumps for both factors and individual stocks. ∗We thank Phil Dybvig, Andrew Karolyi, Cisil Sarisoy, Georgios Skoulakis, seminar participants at Cornell University, Southwest Institute of Finance and Economics, Washington University in St. Louis, as well as participants at the European Meeting of the Econometric Society, Frontiers of Factor Investing Conference at Lancaster, and the 2018 NFA Conference for helpful comments and suggestions. †Olin Business School, Washington University in St. Louis, kadan@wustl.edu. ‡Cornell SC Johnson College of Business, fang.liu@cornell.edu. §Naveen Jindal School of Management, University of Texas at Dallas, xiaoxiao.tang@utdallas.edu.
Supplementary material

1 Quality Observables: detailed definitions 2 1.1 Log-likelihood (LL) . 2 1.2 Energy . 2 1.3 Error of the second moment . 2 1.4 Error of the third moment . 3 1.5 Power spectrum density . 3 1.6 Adversarial Accuracy Indicator . 3 1.7 Entropy . 4 1.8 Frechet Inception Distance score . 4
Initialization of Magneto-rotational Simulation of Core-Collapse Supernovae

Initialization of Magneto-rotational Simulation of Core-Collapse Supernovae. The full mechanism behind core-collapse supernovae is currently not fully understood, as computer simulations have been unable to produce explosions for massive progenitor stars. Strong magnetic ﬁelds may play a signiﬁcant role during the process by driving the stalled shock, transporting angular momentum, and possibly accounting for observed features of supernovae such as bipolar jets. However, the simulation of magnetorotational core collapse is an inherently three-dimensional problem. In this paper, we present in detail our initialization of a fully three-dimensional core-collapse simulation, which we run using the GRMHD code COSMOS++. In our initial model, we incorporate a polytropic density distribution, a rotation proﬁle with variable degree of diﬀerential rotation, and a poloidal magnetic ﬁeld. We describe key features that we have implemented in our simulation code, including a logarithmically spaced mesh and a magnetic ﬁeld inclined to the axis of rotation. In our continuing work we aim to analyze the results of a completed simulation and perform a comprehensive parameter study of magneto-rotational core collapse.
Effect of reproductive disorders on production performance of Hf x Gir Halfbred

The data on reproduction and production performance HF x Gir halfbred cows maintained at R.C. D.P. on Cattle, Mahatma Phule Krishi Vidyapeeth, Rahuri (Maharashtra) was collected and analyzed by least-squares technique to estimate the effect of lactation order and reproductive disorders. The lactation order had significant effect on open period and non-significant effect on service period, calving interval, number of service per conception, monthly peak yield, average daily milk yield, monthly peak yield of previous lactation, lactational milk yield, and lactational milk yield of previous lactation. The effect of reproductive disorders was the significant on open period, service period, calving interval, number of service per conception, monthly peak yield, average daily milk yield, monthly peak yield of previous lactation, lactational milk yield, lactational milk yield of previous lactation. Present study shows the per cent increase in open period, service period, calving interval due to reproductive disorders were 15.92 to 91.68, 56.60 to 94.62 and 10.36 to 39.33 per cent, respectively. However there was decline in lactational milk yield in the range of 10.83 to 36.69 per cent due to reproductive disorders.