Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from Matured Dumb Teacher for Fine Generalization

The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods. A surprising result from recent knowledge distillation (KD) literature is that random, untrained, and equally structured teacher networks can also vastly improve generalization performance. It raises the possibility of existence of undiscovered assumptions useful for generalization on an uncertain region. In this paper, we shed light on the assumptions by analyzing decision boundaries and confidence distributions of both simple and KD-based generalization methods. Assuming that a decision boundary exists to represent the most general tendency of distinction on an input sample space (i.e., the simplest hypothesis), we show the various limitations of methods when using the hypothesis. To resolve these limitations, we propose matured dumb teacher based KD, conservatively transferring the hypothesis for generalization of the student without massive destruction of trained information. In practical experiments on feed-forward and convolution neural networks for image classification tasks on MNIST, CIFAR-10, and CIFAR-100 datasets, the proposed method shows stable improvement to the best test performance in the grid search of hyperparameters. The analysis and results imply that the proposed method can provide finer generalization than existing methods.
Put the Bear on the Chair! Intelligent Robot Interaction with Previously Unseen Chairs via Robot Imagination

In this paper, we study the problem of autonomously seating a teddy bear on a previously unseen chair. To achieve this goal, we present a novel method for robots to imagine the sitting pose of the bear by physically simulating a virtual humanoid agent sitting on the chair. We also develop a robotic system which leverages motion planning to plan SE(2) motions for a humanoid robot to walk to the chair and whole-body motions to put the bear on it. Furthermore, to cope with cases where the chair is not in an accessible pose for placing the bear, a human assistance module is introduced for a human to follow language instructions given by the robot to rotate the chair and help make the chair accessible. We implement our method with a robot arm and a humanoid robot. We calibrate the proposed system with 3 chairs and test on 12 previously unseen chairs in both accessible and inaccessible poses extensively. Results show that our method enables the robot to autonomously seat the teddy bear on the 12 previously unseen chairs with a very high success rate. The human assistance module is also shown to be very effective in changing the accessibility of the chair. Video demos and more details are available at https://chirikjianlab.github. io/putbearonchair/.
The academic motherload: Models of parenting engagement and the effect on academic productivity and performance

Gender differences in research productivity are well documented, and have been mostly explained by access parental leave and child-related responsibilities. Those explanations are based on the assumption that women take on the majority of childcare responsibilities, and take the same level of leave at the birth of a child. Changing social dynamics around parenting has seen fathers increasingly take an active role in parenting. This demands a more nuanced approach to understanding how parenting affects both men and women. Using a global survey of 11,226 academic parents, this study investigates the effect of parental engagement (Lead, Dual (shared), and Satellite parenting), and partner type, on measures of research productivity and impact for men and for women. It also analyzes the effect of different levels of parental leave on academic productivity. Results show that the parenting penalty for men and women is a function of the level of engagement in parenting activities. Men who serve in lead roles suffer similar penalties, but women are more likely to serve in lead parenting roles and to be more engaged across time and tasks. Taking a period of parental leave is associated with higher levels of productivity, however the productivity advantage is lost for the US-sample at 6 months, and at 12-months for the non-US sample. These results suggest that parental engagement is a more powerful variable to explain gender differences in academic productivity than the mere existence of children, and that policies should that factor into account.
Deep Amended Gradient Descent for Efficient Spectral Reconstruction From Single RGB Images

This paper investigates the problem of recovering hyperspectral (HS) images from single RGB images. To tackle such a severely ill-posed problem, we propose a physically-interpretable, compact, efficient, and end-to-end learning-based framework, namely AGD-Net. Precisely, by taking advantage of the imaging process, we first formulate the problem explicitly based on the classic gradient descent algorithm. Then, we design a lightweight neural network with a multi-stage architecture to mimic the formed amended gradient descent process, in which efficient convolution and novel spectral zero-mean normalization are proposed to effectively extract spatial-spectral features for regressing an initialization, a basic gradient, and an incremental gradient. Besides, based on the approximate low-rank property of HS images, we propose a novel rank loss to promote the similarity between the global structures of reconstructed and ground-truth HS images, which is optimized with our singular value weighting strategy during training. Moreover, AGD-Net, a single network after one-time training, is flexible to handle the reconstruction with various spectral response functions. Extensive experiments over three commonly-used benchmark datasets demonstrate that AGD-Net can improve the reconstruction quality by more than 1.0 dB on average while saving 67× parameters and 32× FLOPs, compared with state-of-the-art methods. The code will be publicly available at https://github.com/zbzhzhy/GD-Net.
Converging Radar and Communications in the Superposition Transmission

This paper proposes a superposition transmission scheme for the future Radio Frequency (RF) convergence applications. The scheme is discussed under the assumption of a monostatic broadcasting channel topology. Under communications quality-of-service (QoS) constraints, the joint performance region of communications sum rate and radar estimation error variance is studied. Two radar signal waveforms, namely linear FM and parabolic FM, are used to investigate how signal shapes may influence the estimation accuracy. Both waveforms are generated with rectangular envelope. In the end, a numerical analysis is applied, which concludes that a moderate communications QoS promises a good communications fairness while with the limited radar performance degradation.
Agnostic Online Learning and Excellent Sets

We use algorithmic methods from online learning to revisit a key idea from the interaction of model theory and combinatorics, the existence of large “indivisible” sets, called “ǫ-excellent,” in k-edge stable graphs (equivalently, Littlestone classes). These sets arise in the Stable Regularity Lemma, a theorem characterizing the appearance of irregular pairs in Szemerédi’s celebrated Regularity Lemma. Translating to the language of probability, we find a quite different existence proof for ǫ-excellent sets in Littlestone classes, using regret bounds in online learning. This proof applies to any ǫ < 1/2, compared to < 1/2 k or so in the original proof. We include a second proof using closure properties and the VC theorem, with other advantages but weaker bounds. As a simple corollary, the Littlestone dimension remains finite under some natural modifications to the definition. A theme in these proofs is the interaction of two abstract notions of majority, arising from measure, and from rank or dimension; we prove that these densely often coincide and that this is characteristic of Littlestone (stable) classes. The last section lists several open problems.
NoFake at CheckThat!2021: Fake News Detection Using BERT

Much research has been done for debunking and analysing fake news. Many researchers study fake news detection in the last year, but many are limited to social media data. Currently, multiples fact-checkers are publishing their results in various formats. Also, multiple fact-checkers use different labels for the fake news, making it difficult to make a generalisable classifier. With the merge classes, the performance of the machine model can be enhanced. This domain categorisation will help group the article, which will help save the manual effort in assigning the claim verification. In this paper, we have presented BERT based classification model to predict the domain and classification. We have also used additional data from fact-checked articles. We have achieved a macro F1 score of 83.76 % for Task 3Aand 85.55 % for Task 3B using the additional training data.
Learning Visual Affordance Grounding from Demonstration Videos

Visual affordance grounding aims to segment all possible interaction regions between people and objects from an image/video, which benefits many applications, such as robot grasping and action recognition. Prevailing methods predominantly depend on the appearance feature of the objects to segment each region of the image, which encounters the following two problems: 1) there are multiple possible regions in an object that people interact with and 2) there are multiple possible human interactions in the same object region. To address these problems, we propose a hand-aided affordance grounding network (HAG-Net) that leverages the aided clues provided by the position and action of the hand in demonstration videos to eliminate the multiple possibilities and better locate the interaction regions in the object. Specifically, HAG-Net adopts a dual-branch structure to process the demonstration video and object image data. For the video branch, we introduce hand-aided attention to enhance the region around the hand in each video frame and then use the long short-term memory (LSTM) network to aggregate the action features. For the object branch, we introduce a semantic enhancement module (SEM) to make the network focus on different parts of the object according to the action classes and utilize a distillation loss to align the output features of the object branch with that of the video branch and transfer the knowledge in the video branch to the object branch. Quantitative and qualitative evaluations on two challenging datasets show that our method has achieved state-of-the-art results for affordance grounding. The source code is available at: https://github.com/lhc1224/HAG-Net.
Distributional Depth-Based Estimation of Object Articulation Models

We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a priori. By contrast, existing methods that learn articulation models from raw observations typically only predict point estimates of the model parameters, which are insufficient to guarantee the safe manipulation of articulated objects. Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds. Combining these concepts allows for an efficient, mathematically sound representation that implicitly satisfies the constraints that rigid body transformations and articulations must adhere to. Leveraging this representation, we introduce a novel deep learning based approach, DUST-net, that performs category-independent articulation model estimation while also providing model uncertainties. We evaluate our approach on several benchmarking datasets and real-world objects and compare its performance with two current state-of-the-art methods. Our results demonstrate that DUST-net can successfully learn distributions over articulation models for novel objects across articulation model categories, which generate point estimates with better accuracy than state-of-the-art methods and effectively capture the uncertainty over predicted model parameters due to noisy inputs. Project webpage: https://pearl-utexas.github. io/DUST-net/
LkHα 225 (V1318 Cyg) South in Outburst

Magakian et al. called attention to the current bright state of LkHα 225 South, a well-known highly embedded, intermediate-mass young stellar object that over the past two decades has brightened visually from >20 m to <13 m . We present recent optical photometric monitoring showing colorless, nonsinusoidal, periodic brightness oscillations occurring every 43 days with amplitude ∼0.7 mag. We also present new flux-calibrated optical and near-infrared spectroscopy, which we model in terms of a Keplerian accretion disk, and high-dispersion spectra that demonstrate similarity to some categories of “mixed-temperature” accretion-outburst objects. At blue wavelengths, LkHα 225 South has a pure absorption spectrum and is a good spectral match to the FU Ori stars V1515 Cyg and V1057 Cyg. At red optical and infrared wavelengths, however, the spectrum is more similar to Gaia 19ajj, showing emission in TiO, CO, and metals. Sr ii absorption indicates a low-surface-gravity atmosphere. There are also signatures of a strong wind/outflow. LkHα 225 South was moderately bright in the early 1950s as well as in the late 1980s, with evidence for deep fades during intervening epochs. The body of evidence suggests that LkHα 225 South is another case of a source with episodically enhanced accretion that causes brightening by orders of magnitude, and development of a hot absorption spectrum and warm wind. It is similar to Gaia 19ajj, but also reminiscent in its long brightening time and brightness oscillation near peak to the embedded sources L1634 IRS7 and ESO Ha 99.
C ii and H i 21-cm line intensity mapping from the EoR: impact of the light-cone effect on auto and cross-power spectra

C ii line intensity mapping (LIM) is a potential technique to probe the early galaxies from the Epoch of Reionization (EoR). Several experiments e.g. CONCERTO, TIME, CCAT-p are underway to map the C ii LIM signal fluctuations from the EoR, enabling us to estimate the C ii power-spectrum and C ii × 21-cm cross-power spectrum. The observed LIM signal will have its time evolution embedded in it along the line of sight due to the finite traveltime of the signal from its origin to the observer. We have investigated this so-called light-cone effect on the observed statistics of our seminumerically simulated C ii signal from the EoR. Using a suit of simulated C ii and neutral hydrogen 21-cm maps and corresponding light-cone boxes, we have shown that the light-cone effect can impact the C ii power spectrum by more than 15 per cent at large scales ($k\sim 0.1\, \text{Mpc}^{-1}$, at $z$ = 6.8). We have also observed that the impact of light-cone effect on the C ii power spectrum drops with decreasing redshift within the redshift range considered here (7.2 ≲ $z$ ≲ 6). The C ii × 21-cm cross-power spectrum is also affected by light cone, and in our models where reionization ends before $z$ = 6, we find that the maximum impact on cross-power can reach up to 20 per cent. At $z$ = 6.4, we find comparatively pronounced variation in the light-cone effect with reionization history on the cross-power. Faster reionization histories have a more drastic light-cone effect on cross-power. We conclude that we need to incorporate the light cone in order to properly model the signal, constrain the EoR-related astrophysical parameters and reionization history using the C ii × 21-cm cross-power spectrum.
Going Deeper in Frequency Convolutional Neural Network: A Theoretical Perspective

Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning. However, the high-computational cost of CNN still hampers more universal uses to light devices. Fortunately, the Fourier transform on convolution gives an elegant and promising solution to dramatically reduce the computation cost. Recently, some studies devote to such a challenging problem and pursue the complete frequency computation without any switching between spatial domain and frequent domain. In this work, we revisit the Fourier transform theory to derive feed-forward and back-propagation frequency operations of typical network modules such as convolution, activation and pooling. Due to the calculation limitation of complex numbers on most computation tools, we especially extend the Fourier transform to the Laplace transform for CNN, which can run in the real domain with more relaxed constraints. This work more focus on a theoretical extension and discussion about frequency CNN, and lay some theoretical ground for real application.
On the Compensation Between Magnitude and Phase in Speech Separation

Deep neural network (DNN) based end-to-end optimization in the complex time-frequency (T-F) domain or time domain has shown considerable potential in monaural speech separation. Many recent studies optimize loss functions defined solely in the time or complex domain, without including a loss on magnitude. Although such loss functions typically produce better scores if the evaluation metrics are objective time-domain metrics, they however produce worse scores on speech quality and intelligibility metrics and usually lead to worse speech recognition performance, compared with including a loss on magnitude. While this phenomenon has been experimentally observed by many studies, it is often not accurately explained and there lacks a thorough understanding on its fundamental cause. This letter provides a novel view from the perspective of the implicit compensation between estimated magnitude and phase. Analytical results based on monaural speech separation and robust automatic speech recognition (ASR) tasks in noisy-reverberant conditions support the validity of our view.
Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction

Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems. It is a challenging problem as agents adjust their behavior depending on their intentions, the others’ actions, and the road layout. In this paper, we propose Decoder Fusion RNN (DF-RNN), a recurrent, attention-based approach for motion forecasting. Our network is composed of a recurrent behavior encoder, an inter-agent multi-headed attention module, and a context-aware decoder. We design a map encoder that embeds polyline segments, combines them to create a graph structure, and merges their relevant parts with the agents’ embeddings. We fuse the encoded map information with further inter-agent interactions only inside the decoder and propose to use explicit training as a method to effectively utilize the information available. We demonstrate the efficacy of our method by testing it on the Argoverse motion forecasting dataset and show its state-of-the-art performance on the public benchmark.
Syntax Matters! Syntax-Controlled in Text Style Transfer

Existing text style transfer (TST) methods rely on style classifiers to disentangle the text’s content and style attributes for text style transfer. While the style classifier plays a critical role in existing TST methods, there is no known investigation on its effect on the TST methods. In this paper, we conduct an empirical study on the limitations of the style classifiers used in existing TST methods. We demonstrated that the existing style classifiers cannot learn sentence syntax effectively and ultimately worsen existing TST models’ performance. To address this issue, we propose a novel Syntax-Aware Controllable Generation (SACG) model, which includes a syntax-aware style classifier that ensures learned style latent representations effectively capture the sentence structure for TST. Through extensive experiments on two popular text style transfer tasks, we show that our proposed method significantly outperforms twelve state-of-the-art methods. Our case studies have also demonstrated SACG’s ability to generate fluent target-style sentences that preserved the original content.
Accumulation of individual fitness or wealth as a population game

The accumulation of individual fitness or wealth is modelled as a population game in which pairs of individuals are recurrently and randomly matched to play a game over a resource. In addition, all individuals have random access to a constant background resource, and ∗This manuscript builds upon an incomplete sketch by one of the authors, see Weibull (1999). We thank Ingela Alger, Jérôme Renault, Laurent Miclo, Daniel Waldenström, and two anonymous referees for comments and suggestions to an earlier version of the manuscript with the title "A model of wealth accumulation". Sylvain Gibaud (gibaudsylvain@gmail.com) thanks Unité Mixte de Recherche 5219 for its financial support. Jörgen Weibull (jorgen.weibull@hhs.se) thanks the Knut and Alice Wallenberg Research Foundation, the Agence Nationale de la Recherche, Chaire IDEX ANR-11-IDEX-0002-02, and the Tore Browald and Tom Hedelius Foundation for financial support. 1 ar X iv :1 70 7. 00 99 6v 2 [ ph ys ic s. so cph ] 1 2 A ug 2 02 1 their fitness or wealth depreciates over time. For brevity we focus on the well-known Hawk-Dove game. In the base-line model, the probability of winning a fight (that is, when both play Hawk) is the same for both parties. In an extended version, the individual with higher current fitness or wealth has a higher probability of winning. Analytical results are given for the fitness/wealth distribution at any given time, for the evolution of average fitness/wealth over time, and for the asymptotics with respect to time and population size. Long-run average fitness/wealth is non-monotonic in the value of the resource, thus providing a potential explanation of the curse of the riches.
On a new geometric homology theory and an application in categorical Gromov-Witten theory

The purpose of this paper is twofold: 1. we prove the triangulability of smooth orbifolds with corners, generalizing the same statement for orbifolds. 2. based on 1, we propose a new homology theory. We call it geometric homology theory (GHT for abbreviaty). GHT is a natural and flexible generalization of singular homology. It has some advantages overcoming the unpleasant combinatoric rigidity of singular homology, e.g. ill-defined pullbacks of singular chains along fiber bundles. The method we use are mainly based on the celebrated stratification and triangulation theories of Lie groupoids and their orbit spaces, as well as the extension to Lie groupoids with corners by us. We illustrate a simple application of GHT in categorical Gromov-Witten theory, initiatied by Costello. We will develop further of this theory in our sequel paper.
Adaptive Discretization for Adversarial Lipschitz Bandits

Lipschitz bandits is a prominent version of multi-armed bandits that studies large, structured action spaces such as the [0,1] interval, where similar actions are guaranteed to have similar rewards. A central theme here is the adaptive discretization of the action space, which gradually ``zooms in'' on the more promising regions thereof. The goal is to take advantage of ``nicer'' problem instances, while retaining near-optimal worst-case performance. While the stochastic version of the problem is well-understood, the general version with adversarial rewards is not. We provide the first algorithm for adaptive discretization in the adversarial version, and derive instance-dependent regret bounds. In particular, we recover the worst-case optimal regret bound for the adversarial version, and the instance-dependent regret bound for the stochastic version. Further, an application of our algorithm to dynamic pricing (where a seller repeatedly adjusts prices for a product) enjoys these regret bounds without any smoothness assumptions.