Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence Analysis of the Randomized Newton Method with Determinantal Sampling

We analyze the convergence rate of the Randomized Newton Method (RNM) introduced by Qu et. al. (2016) for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix $\mathbf{M}$. The convergence analysis of RNM is challenging because of its complex dependence on the structure of $\mathbf{M}$. However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix $\mathbf{M}$, and has an analytically tractable form. To do so, we derive a fundamental new expectation formula for determinantal point processes. We show that determinantal sampling allows us to reason about the optimal subset size of blocks in terms of the spectrum of $\mathbf{M}$. Additionally, we provide a numerical evaluation of our analysis, demonstrating cases where determinantal sampling is superior or on par with uniform sampling.
Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations

Recently, over-parameterized neural networks have been extensively analyzed in the literature. However, the previous studies cannot satisfactorily explain why fully trained neural networks are successful in practice. In this paper, we present a new theoretical framework for analyzing over-parameterized neural networks which we call neural feature repopulation. Our analysis can satisfactorily explain the empirical success of two level neural networks that are trained by standard learning algorithms. Our key theoretical result is that in the limit of infinite number of hidden neurons, over-parameterized two-level neural networks trained via the standard (noisy) gradient descent learns a well-defined feature distribution (population), and the limiting feature distribution is nearly optimal for the underlying learning task under certain conditions. Empirical studies confirm that predictions of our theory are consistent with the results observed in real practice.
The Use of Microdialysis in the Study of Encephalopathies

Microdialysis is a technique that was devised by Bito et al. (1966) in a study to determine free aminoacids and electrolytes in cerebrospinal fluid and blood plasma in dogs. It consists of recollection of samples in specific places by using semi-permeable membranes, used to evaluate compounds (endogenous neurotransmitters or exogenous such as drugs), in extracellular spaces from different tissues as brain, skin, subcutaneous, tumors, kidney, abdominal and femoral adipose tissue, muscle, lung, middle ear fluid, and other extracellular spaces like blood. Currently, one of the main applications of this methodology in the field of neuroscience is to conduct studies to monitor the release of different substances in brain of conscious and unconscious anesthetized animals. The technique has also been used in surgical procedures as well as in monitoring patients in intensive care unit with traumatic brain injury and subarachnoid hemorrhage. It is employed in neuronal diseases like Epilepsy, Alzheimer, Parkinson, Huntington, Amyotrophic Lateral Sclerosis (ALS) and Encephalopathies.
Channel Adversarial Training for Speaker Verification and Diarization

Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong. We propose a training strategy which aims to produce features that are invariant at the granularity of the recording or channel, a finer grained objective than dataset- or environment-invariance. By training an adversary to predict whether pairs of same-speaker embeddings belong to the same recording in a Siamese fashion, learned features are discouraged from utilizing channel information that may be speaker discriminative during training. Experiments for verification on VoxCeleb and diarization and verification on CALLHOME show promising improvements over a strong baseline in addition to outperforming a dataset-adversarial model. The VoxCeleb model in particular performs well, achieving a 4% relative improvement in EER over a Kaldi baseline, while using a similar architecture and less training data.
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators

Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation tasks, which we call Interactive Navigation, requires the use of mobile manipulators: mobile bases with manipulation capabilities. Interactive Navigation tasks are usually long-horizon and composed of heterogeneous phases of pure navigation, pure manipulation, and their combination. Using the wrong part of the embodiment is inefficient and hinders progress. We propose HRL4IN, a novel Hierarchical RL architecture for Interactive Navigation tasks. HRL4IN exploits the exploration benefits of HRL over flat RL for long-horizon tasks thanks to temporally extended commitments towards subgoals. Different from other HRL solutions, HRL4IN handles the heterogeneous nature of the Interactive Navigation task by creating subgoals in different spaces in different phases of the task. Moreover, HRL4IN selects different parts of the embodiment to use for each phase, improving energy efficiency. We evaluate HRL4IN against flat PPO and HAC, a state-of-the-art HRL algorithm, on Interactive Navigation in two environments - a 2D grid-world environment and a 3D environment with physics simulation. We show that HRL4IN significantly outperforms its baselines in terms of task performance and energy efficiency. More information is available at this https URL.
From diffuse extragalactic and galactic gamma-rays to limits on extra dimensions

We derive the maximum fraction of energy emitted in the form of massive (Kaluza–Klein) gravitons by core collapse supernovae, and the corresponding minimal extra-dimensional Planck mass M* in the ADD gravity framework at TeV scales. Our constraints arise (a) from the extragalactic gamma-ray background observed by Fermi-LAT after astrophysical sources have been removed and (b) via the residual galactic emission left after astrophysical and potentially dark matter emission have been removed. We focus on a number of extra dimensions 3 and 4, since M* is then in the TeV range, where astrophysical and collider constraints compete. Lower limits on M* are derived in the case (a) of 8.0 and 1.1 TeV for n = 3 and n = 4, respectively, and in the case (b) of 16 and 1.9 TeV. These limits are especially robust and insensitive to the various uncertainties involved.
Linear preservers of semipositive matrices over proper cones in finite dimensions

Given two proper cones $K_1$ and $K_2$ in $\mathbb{R}^n$ and $\mathbb{R}^m$, respectively, we say that an $m \times n$ matrix $A$ is semipositive if there exists a $x \in K_1^\circ$ such that $Ax \in K_2^\circ$. The set of semipositive matrices relative to $K_1$ and $K_2$ will be denoted by $S(K_1,K_2)$. The aim of this manuscript is two fold: (1) resolve a conjecture on the structure of into linear preservers of semipositive matrices when $K_1$ and $K_2$ are nonnegative orthants in $\mathbb{R}^n$ and $\mathbb{R}^m$, respectively, when $m \geq n$ (2) determine the structure of linear preservers (into as well as onto/strong) of $S(K_1,K_2)$. Linear preservers of the subclass of those semipositive matrices (relative to $K_1$ and $K_2$) with a $(K_2,K_1)$-nonnegative left inverse and connections of strong linear preservers of $S(K_1,K_2)$ with other preserver problems are also discussed.
Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters

DARTS is a popular algorithm for neural architecture search (NAS). Despite its great advantage in search efficiency, DARTS often suffers weak stability, which reflects in the large variation among individual trials as well as the sensitivity to the hyper-parameters of the search process. This paper owes such instability to an optimization gap between the super-network and its sub-networks, namely, improving the validation accuracy of the super-network does not necessarily lead to a higher expectation on the performance of the sampled sub-networks. Then, we point out that the gap is due to the inaccurate estimation of the architectural gradients, based on which we propose an amended estimation method. Mathematically, our method guarantees a bounded error from the true gradients while the original estimation does not. Our approach bridges the gap from two aspects, namely, amending the estimation on the architectural gradients, and unifying the hyper-parameter settings in the search and re-training stages. Experiments on CIFAR10 and ImageNet demonstrate that our approach largely improves search stability and, more importantly, enables DARTS-based approaches to explore much larger search spaces that have not been investigated before.
A Neutral Beam Model for High-energy Neutrino Emission from the Blazar TXS 0506+056

The IceCube collaboration reported an ∼3. 5σ excess of 13 ± 5 neutrino events in the direction of the blazar TXS 0506+056 during an ∼6 month period in 2014–2015, as well as the (∼3σ) detection of a high-energy muon neutrino during an electromagnetic flare in 2017. We explore the possibility that the 2014–2015 neutrino excess and the 2017 multimessenger flare are both explained in a common physical framework that relies on the emergence of a relativistic neutral beam in the blazar jet due to interactions of accelerated cosmic rays (CRs) with photons. We demonstrate that the neutral beam model provides an explanation for the 2014–2015 neutrino excess without violating X-ray and γ-ray constraints and yields results consistent with the detection of one high-energy neutrino during the 2017 flare. If both neutrino associations with TXS 05065+056 are real, our model requires that (i) the composition of accelerated CRs is light, with a ratio of helium nuclei to protons ≳5; (ii) a luminous external photon field (∼1046 erg s−1) variable (on yearlong timescales) is present; and (iii) the CR injection luminosity, as well as the properties of the dissipation region (i.e., Lorentz factor, magnetic field, and size), vary on yearlong timescales.
Finite Mixtures of ERGMs for Ensembles of Networks

Ensembles of networks arise in many scientific fields, but currently there are few statistical models aimed at understanding their generative processes. To fill in this gap, we propose characterizing network ensembles via finite mixtures of exponential family random graph models, employing a Metropolis-within-Gibbs algorithm to conduct Bayesian inference. Simulation studies show that the proposed procedure can recover the true cluster assignments and cluster-specific parameters. We demonstrate the utility of the proposed approach using an ensemble of political co-voting networks among U.S. Senators.
Expanding ring-shaped Bose-Einstein condensates as analogs of cosmological models: Analytical characterization of the inflationary dynamics

We analytically study the expansion of a Bose-Einstein condensate in a ring-shaped trap with an increasing central radius. The evolution of the ground state is described using a scaling transform. Additionally, the dynamics of excited azimuthal modes over the varying ground state is analyzed through a generalization of the Bogoliubov--de Gennes approach. Our results explain some of the features observed in recent experiments focused on testing the applicability of the system as a parallel of cosmological inflationary models. The radial dynamics, which corresponds to the inflaton field of the cosmological counterpart, is analytically characterized: The expansion is found to induce the oscillatory displacement of the condensate as well as the coupled variation of the radial and vertical widths. Our findings account also for the observed redshift and emergence of the Hubble friction in the evolution of initially prepared azimuthal modes. Our description, which traces the role of the different components of the setup in the expansion, enhances the controllability and therefore the potential of the system as a ground for emulating the inflationary dynamics of cosmological models.
Perpendicular Ion Heating by Cyclotron Resonant Dissipation of Turbulently Generated Kinetic Alfvén Waves in the Solar Wind

Solar wind ions are observed to be heated in the directions perpendicular to the large-scale magnetic field, with preferential heating given to heavy ions. In the solar corona, this heating may be responsible for the generation of the wind itself. It is likely that this heating results from the dissipation of plasma turbulence, but the specific kinetic mechanism that produces these preferential effects is not known. Solar wind turbulence at proton scales is often characterized in terms of highly oblique kinetic Alfvén waves (KAWs), which have been thought to dissipate through the Landau resonance and yield parallel heating. We show that the quasilinear resonant cyclotron interaction between KAWs and solar wind ions can actually produce perpendicular ion heating. We present an illustrative calculation of a steady, critically balanced spectrum of KAWs acting on homogeneous ion distributions with a plasma β = 0.1, representative of turbulent conditions in the fast solar wind near 20 Rs. The KAWs are described here by a two-fluid dispersion relation. We find that thermal protons are strongly heated in the perpendicular direction within a typical quasilinear time of several thousand gyroperiods, which corresponds to only a few minutes at 20 Rs. Alpha particles in the same fluctuation field are heated to similar perpendicular thermal speeds, equivalent to the greater than mass proportional perpendicular temperatures that are commonly observed. We discuss improvements to this simple model that may determine whether this mechanism can be responsible for the observed coronal and solar wind ion heating.
Does car sharing reduce greenhouse gas emissions? Life cycle assessment of the modal shift and lifetime shift rebound effects

Car-sharing platforms provide access to a shared rather than a private fleet of automobiles distributed in the region. Participation in such services induces changes in mobility behaviour as well as vehicle ownership patterns that could have positive environmental impacts. This study contributes to the understanding of the total mobility-related greenhouse gas emissions reduction related to business-to-consumer car-sharing participation. A comprehensive model which takes into account distances travelled annually by the major urban transport modes as well as their life-cycle emissions factors is proposed, and the before-and-after analysis is conducted for an average car-sharing member in three geographical cases (Netherlands, San Francisco, Calgary). In addition to non-operational emissions for all the transport modes involved, this approach considers the rebound effects associated with the modal shift effect (substituting driving distances with alternative modes) and the lifetime shift effect for the shared automobiles, phenomena which have been barely analysed in the previous studies. As a result, in contrast to the previous impact assessments in the field, a significantly more modest reduction of the annual total mobility-related life-cycle greenhouse gas emissions caused by car-sharing participation has been estimated, 3-18% for three geographical case studies investigated (versus up to 67% estimated previously). This suggests the significance of the newly considered effects and provides with the practical implications for improved assessments in the future.
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection

Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and require large-scale labeled event datasets which are unfortunately not readily available in many real-life applications. In this paper, we consider and reformulate the ED task with limited labeled data as a Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs. The experiments show that DMB-PN not only deals with sample scarcity better than a series of baseline models but also performs more robustly when the variety of event types is relatively large and the instance quantity is extremely small.
SpeechBERT: Cross-Modal Pre-trained Language Model for End-to-end Spoken Question Answering

While end-to-end models for spoken language understanding tasks have been explored recently, there is still no end-to-end model for spoken question answering (SQA) tasks, which would be catastrophically influenced by speech recognition errors. Meanwhile, pre-trained language models, such as BERT, have performed successfully in text question answering. To bring this advantage of pre-trained language models into spoken question answering, we propose SpeechBERT, a cross-modal transformer-based pre-trained language model. Our model can outperform conventional approaches on the dataset which contains both correctly recognized answers and incorrectly recognized answers. Our experimental results show the potential of end-to-end SQA models.
Local Morrey estimate in Musielak-Orlicz-Sobolev space

Under appropriate assumptions on the $N(\Omega)$-fucntion, locally uniform Morrey estimate is presented in the Musielak-Orlicz-Sobolev space. The assumptions include a new increasing condition on the $x$-derivative of the Young complementary function of the $N(\Omega)$-fucntion. The conclusion applies to several important nonlinear examples frequently appeared in mathematical literature.
Learning to Track Any Object

Object tracking can be formulated as "finding the right object in a video". We observe that recent approaches for class-agnostic tracking tend to focus on the "finding" part, but largely overlook the "object" part of the task, essentially doing a template matching over a frame in a sliding-window. In contrast, class-specific trackers heavily rely on object priors in the form of category-specific object detectors. In this work, we re-purpose category-specific appearance models into a generic objectness prior. Our approach converts a category-specific object detector into a category-agnostic, object-specific detector (i.e. a tracker) efficiently, on the fly. Moreover, at test time the same network can be applied to detection and tracking, resulting in a unified approach for the two tasks. We achieve state-of-the-art results on two recent large-scale tracking benchmarks (OxUvA and GOT, using external data). By simply adding a mask prediction branch, our approach is able to produce instance segmentation masks for the tracked object. Despite only using box-level information on the first frame, our method outputs high-quality masks, as evaluated on the DAVIS '17 video object segmentation benchmark.
Complexities of differentiable dynamical systems

We define the notion of localizable property for a dynamical system. Then we survey three properties of complexity and relate how they are known to be typical among differentiable dynamical systems. These notions are the fast growth of the number of periodic points, the positive entropy and the high emergence. We finally propose a dictionary between the previously explained theory on entropy and the ongoing one on emergence.
Study of the Galactic radio sources in the SCORPIO survey resolved by ATCA at 2.1 GHz

We present a catalogue of a large sample of extended radio sources in the SCORPIO field, observed and resolved by the Australia Telescope Compact Array. SCORPIO, a pathfinder project for addressing the early operations of the Australia SKA Pathfinder, is a survey of ∼5 square degrees between 1.4 and 3.1 GHz, centered at l = 343.5○, b = 0.75○ and with an angular resolution of about 10 arcsec. It is aimed at understanding the scientific and technical challenges to be faced by future Galactic surveys. With a mean sensitivity around $100\ \mu \mathrm{Jy\ beam}^{-1}$ and the possibility to recover angular scales at least up to 4 arcmin, we extracted 99 extended sources, 35 of them detected for the first time. Among the 64 known sources 55 had at least a tentative classification in literature. Studying the radio morphology and comparing the radio emission with infrared we propose as candidates 6 new H ii regions, 2 new planetary nebulae, 2 new luminous blue variable or Wolf–Rayet stars and 3 new supernova remnants. This study provides an overview of the potentiality of future radio surveys in terms of Galactic source extraction and characterization and a discussion on the difficulty to reduce and analyze interferometric data on the Galactic plane.
ON EXTREMAL QUASI-MODULAR FORMS AFTER KANEKO AND KOIKE

Kaneko and Koike introduced the notion of extremal quasi-modular form and proposed conjectures on their arithmetic properties. The aim of this note is to prove a rather sharp multiplicity estimate for these quasi-modular forms. The note ends with discussions and partial answers around these conjectures and an appendix by G. Nebe containing the proof of the integrality of the Fourier coefficients of the normalised extremal quasimodular form of weight 14 and depth 1.
On the Tunability of Optimizers in Deep Learning

There is no consensus yet on the question whether adaptive gradient methods like Adam are easier to use than non-adaptive optimization methods like SGD. In this work, we fill in the important, yet ambiguous concept of `ease-of-use' by defining an optimizer's \emph{tunability}: How easy is it to find good hyperparameter configurations using automatic random hyperparameter search? We propose a practical and universal quantitative measure for optimizer tunability that can form the basis for a fair optimizer benchmark. Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, we find that Adam is the most tunable for the majority of problems, especially with a low budget for hyperparameter tuning.
Self-Supervised Learning of Detailed 3D Face Reconstruction

In this article, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-to-image translation network to predict a displacement map in UV-space. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of our method over previous work.