Miracle-dz/UniFAR-FTUs huggingface dataset:
Bounding extrema over global attractors using polynomial optimisation

We present a framework for bounding extreme values of quantities on global attractors of differential dynamical systems. A global attractor is the minimal set that attracts all bounded sets; it contains all forward-time limit points. Our approach uses (generalised) Lyapunov functions to find attracting sets, which must contain the global attractor, and the choice of Lyapunov function is optimised based on the quantity whose extreme value one aims to bound. We also present a non-global framework for bounding extrema over the minimal set that is attracting in a specified region of state space. If the dynamics are governed by ordinary differential equations, and the equations and quantities of interest are polynomial, then our methods can be implemented computationally using polynomial optimisation. In particular, we enforce nonnegativity of certain polynomial expressions by requiring them to be representable as sums of squares, leading to a convex optimisation problem that can be recast as a semidefinite program and solved computationally. This computer assistance lets one construct complicated polynomial Lyapunov functions. Computations are illustrated using three examples. The first is the chaotic Lorenz system, where we bound extreme values of various monomials of the coordinates over the global attractor. In the second example we bound extreme values over a chaotic saddle in a nine-mode truncation of fluid dynamics that displays long-lived chaotic transients. The third example has two locally stable limit cycles, each with its own basin of attraction, and we apply our non-global framework to construct bounds for one basin that do not apply to the other. For each example we compute Lyapunov functions of polynomial degrees up to at least eight. In cases where we can judge the sharpness of our bounds, they are sharp to at least three digits when the polynomial degree is at least four or six.
UPI-Net: Semantic Contour Detection in Placental Ultrasound

Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach, namely UPI-Net, aims to capture long-range dependencies in placenta geometry through lightweight global context modelling and effective multi-scale feature aggregation. We perform a subject-level 10-fold nested cross-validation on a placental ultrasound database (4,871 images with labelled UPI from 49 scans). Experimental results demonstrate that, without introducing considerable computational overhead, UPI-Net yields the highest performance in terms of standard contour detection metrics, compared to other competitive benchmarks.
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation

Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations. The widely used method for document-level translation usually compresses the context information into a representation via hierarchical attention networks. However, this method neither considers the relationship between context words nor distinguishes the roles of context words. To address this problem, we propose a query-guided capsule networks to cluster context information into different perspectives from which the target translation may concern. Experiment results show that our method can significantly outperform strong baselines on multiple data sets of different domains.
Pre-training of Deep Contextualized Embeddings of Words and Entities for Named Entity Disambiguation

Deep contextualized embeddings trained using unsupervised language modeling (e.g., ELMo and BERT) are successful in a wide range of NLP tasks. In this paper, we propose a new contextualized embedding model of words and entities for named entity disambiguation (NED). Our model is based on the bidirectional transformer encoder and produces contextualized embeddings for words and entities in the input text. The embeddings are trained using a new masked entity prediction task that aims to train the model by predicting randomly masked entities in entity-annotated texts. We trained the model using entity-annotated texts obtained from Wikipedia. We evaluated our model by addressing NED using a simple NED model based on the trained contextualized embeddings. As a result, we achieved state-of-the-art or competitive results on several standard NED datasets.
THE PROPERTIES AND LONGITUDINAL EXPERIENCE OF CHINESE TYPE A BOTULINUM TOXIN FOR THE TREATMENT OF FOCAL DYSTONIA AND HEMIFACIAL SPASM

Objective. To introduce the properties of Chinese type A botulinum toxin (CBTXA, made by Lanzhou Institute of Biological Products), and its long-term effect for focal dystonia and hemifacial spasm. Method. The purity and recovery of crude and crystalline toxin were tested. Long-term data from 305 patients with hemifacial spasm (HFS), blepharospasm (BS) and cervical dystonia (CD) were evaluated and subgroups of patients received CBTXA injections between 1994 and 2000 in at least six separate treatment sessions, with follow up for 2-8 years. The therapeutic results of the last session CBTXA injections were analyzed in comparison with the first session. Result. CBTXA purity was high[(2.55～2.60)×107D50/mgPr, A260/A280 ≤0.55, high molecular substance accounted for 99.2% of total proteins]. Long term treatment with CBTXA in patients with focal dystonia and HFS was not associated with any decline in benefit, and efficacy may improve slightly with repeat treatments. CBTXA is an excellent long-term treatment of HFS, BS and CD. Conchusion. We conclude that Chinese type A botulinum toxin is of botulinum toxin therapy quality standard according to results obtained from the basic study and long-term clinical applications. The reinjection of CBTXA significantly improves the quality of life of most patients and is a safe, effective and comparatively economical treatment for patients with focal dystonia and HFS.
Push for Quantization: Deep Fisher Hashing

Current massive datasets demand light-weight access for analysis. Discrete hashing methods are thus beneficial because they map high-dimensional data to compact binary codes that are efficient to store and process, while preserving semantic similarity. To optimize powerful deep learning methods for image hashing, gradient-based methods are required. Binary codes, however, are discrete and thus have no continuous derivatives. Relaxing the problem by solving it in a continuous space and then quantizing the solution is not guaranteed to yield separable binary codes. The quantization needs to be included in the optimization. In this paper we push for quantization: We optimize maximum class separability in the binary space. We introduce a margin on distances between dissimilar image pairs as measured in the binary space. In addition to pair-wise distances, we draw inspiration from Fisher's Linear Discriminant Analysis (Fisher LDA) to maximize the binary distances between classes and at the same time minimize the binary distance of images within the same class. Experiments on CIFAR-10, NUS-WIDE and ImageNet100 demonstrate compact codes comparing favorably to the current state of the art.
An analogue of Pillai's theorem for continued fraction normality and an application to subsequences

In the study of normal numbers using the base‐ b representation, b⩾2 , we know that a number is normal if and only if it is simply normal in all the bases bn , n⩾1 . We prove the analogue of this result for continued fraction normality. In particular, we show that two notions of continued fraction normality, one where overlapping occurrences of finite patterns are counted as distinct occurrences, and another where only disjoint occurrences are counted as distinct, are identical. This equivalence involves an analogue of a theorem due to Pillai (Proc. Indian Acad. Sci. Sect. A 11 (1940) 73–80) for base‐ b expansions. The proof requires techniques which are fundamentally different, since the continued fraction expansion utilizes a countably infinite alphabet, leading to a non‐compact space.
Secularly powered outflows from AGNs: the dominance of non-merger driven supermassive black hole growth

Recent observations and simulations have revealed the dominance of secular processes over mergers in driving the growth of both supermassive black holes (SMBHs) and galaxy evolution. Here, we obtain narrow-band imaging of AGN powered outflows in a sample of 12 galaxies with disc-dominated morphologies, whose history is assumed to be merger-free. We detect outflows in 10/12 sources in narrow-band imaging of the $\mathrm{[O\, \small {III}] }$ $5007~\mathring{\rm A}$ emission using filters on the Shane-3m telescope. We calculate a mean outflow rate for these AGNs of $0.95\pm 0.14~\rm {M}_{\odot }~\rm {yr}^{-1}$. This exceeds the mean accretion rate of their SMBHs ($0.054\pm 0.039~\rm {M}_{\odot }~\rm {yr}^{-1}$) by a factor of 18. Assuming that the galaxy must provide at least enough material to power both the AGN and outflow, this gives a lower limit on the average inflow rate of $1.01\pm 0.14~\rm {M}_{\odot }~\rm {yr}^{-1}$, a rate which simulations show can be achieved by bars, spiral arms, and cold accretion. We compare our disc-dominated sample to a sample of nearby AGNs with merger dominated histories and show that the black hole accretion rates in our sample are five times higher (4.2σ) and the outflow rates are five times lower (2.6σ). We suggest that this could be a result of the geometry of the smooth, planar inflow in a secular dominated system, which is both spinning up the black hole to increase accretion efficiency and less affected by feedback from the outflow, than in a merger-driven system with chaotic quasi-spherical inflows. This work provides further evidence that secular processes are sufficient to fuel SMBH growth.
Active Collaborative Sensing for Energy Breakdown

Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.
Multi-level Attention Network using Text, Audio and Video for Depression Prediction

Depression has been the leading cause of mental-health illness worldwide. Major depressive disorder (MDD), is a common mental health disorder that affects both psychologically as well as physically which could lead to loss of lives. Due to the lack of diagnostic tests and subjectivity involved in detecting depression, there is a growing interest in using behavioural cues to automate depression diagnosis and stage prediction. The absence of labelled behavioural datasets for such problems and the huge amount of variations possible in behaviour makes the problem more challenging. This paper presents a novel multi-level attention based network for multi-modal depression prediction that fuses features from audio, video and text modalities while learning the intra and intermodality relevance. The multi-level attention reinforces overall learning by selecting the most influential features within each modality for the decision making. We perform exhaustive experimentation to create different regression models for audio, video and text modalities. Several fusions models with different configurations are constructed to understand the impact of each feature and modality. We outperform the current baseline by 17.52% in terms of root mean squared error.
Performance of Q-learning with Linear Function Approximation: Stability and Finite-Time Analysis

In this paper, we consider the model-free reinforcement learning problem and study the popular Q-learning algorithm with linear function approximation for estimating the optimal policy. Despite its popularity, it is known that Q-learning with linear function approximation may diverge in general due to off-policy sampling. Our main contribution is to provide a finite-time bound and the convergence rate on the performance of Q-learning with linear function approximation under an assumption on the behavior policy. Unlike some prior work in the literature, we do not need to make the unnatural assumption that the samples are i.i. d. (since they are Markovian), and do not require an additional projection step in the algorithm. To show this result, we first consider a more general nonlinear stochastic approximation algorithm with Markovian noise, and derive a finite-time bound on the mean-square error, which we believe is of independent interest. Our proof is based on Lyapunov drift arguments and exploits the geometric mixing of the underlying Markov chain. We also provide numerical simulations to illustrate the effectiveness of our assumption on the behavior policy, and demonstrate the rate of convergence of Q-learning with linear function approximation.
Discovery of a Red Supergiant Donor Star in SN2010da/NGC 300 ULX-1

SN2010da/NGC 300 ULX-1 was first detected as a supernova impostor in 2010 May and was recently discovered to be a pulsating ultraluminous X-ray source. In this Letter, we present Very Large Telescope/X-shooter spectra of this source obtained in 2018 October, covering the wavelength range 350–2300 nm. The J- and H-bands clearly show the presence of a red supergiant (RSG) donor star that is best matched by a MARCS stellar atmosphere with Teff = 3650–3900 K and log(Lbol/L⊙) = 4.25 ± 0.10, which yields a stellar radius R = 310 ± 70R⊙. To fit the full spectrum, two additional components are required: a blue excess that can be fitted either by a hot blackbody (T ≳ 20,000 K) or a power law (spectral index α ≈ 4) and is likely due to X-ray emission reprocessed in the outer accretion disk or the donor star; and a red excess that is well fitted by a blackbody with a temperature of ∼1100 K, and is likely due to warm dust in the vicinity of SN2010da. The presence of an RSG in this system implies an orbital period of at least 0.8–2.1 yr, assuming Roche-lobe overflow. Given the large donor-to-compact object mass ratio, orbital modulations of the radial velocity of the RSG are likely undetectable. However, the radial velocity amplitude of the neutron star is large enough (up to 40–60 km s−1) to potentially be measured in the future, unless the system is viewed at a very unfavorable inclination.
Cross-Cutting Political Awareness through Diverse News Recommendations

The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias. As a result, they have been observed to promote well-known "blockbuster" items, and to present users with "more of the same" choices that entrench their existing beliefs and biases. This limits users' exposure to diverse viewpoints and potentially increases political polarization. To promote the diversity of views, we developed a novel computational framework that can identify the political leanings of users and the news items they share on online social networks. Based on such information, our system can recommend news items that purposefully expose users to different viewpoints and increase the diversity of their information "diet. " Our research on recommendation diversity and political polarization helps us to develop algorithms that measure each user's reaction %to diverse viewpoints and adjust the recommendation accordingly. The result is an approach that exposes users to a variety of political views and will, hopefully, broaden their acceptance (not necessarily the agreement) of various opinions.
Dark Energy Survey year 1 results: the relationship between mass and light around cosmic voids

What are the mass and galaxy profiles of cosmic voids? In this paper, we use two methods to extract voids in the Dark Energy Survey (DES) Year 1 redMaGiC galaxy sample to address this question. We use either 2D slices in projection, or the 3D distribution of galaxies based on photometric redshifts to identify voids. For the mass profile, we measure the tangential shear profiles of background galaxies to infer the excess surface mass density. The signal-to-noise ratio for our lensing measurement ranges between 10.7 and 14.0 for the two void samples. We infer their 3D density profiles by fitting models based on N-body simulations and find good agreement for void radii in the range 15–85 Mpc. Comparison with their galaxy profiles then allows us to test the relation between mass and light at the 10 per cent level, the most stringent test to date. We find very similar shapes for the two profiles, consistent with a linear relationship between mass and light both within and outside the void radius. We validate our analysis with the help of simulated mock catalogues and estimate the impact of photometric redshift uncertainties on the measurement. Our methodology can be used for cosmological applications, including tests of gravity with voids. This is especially promising when the lensing profiles are combined with spectroscopic measurements of void dynamics via redshift-space distortions.
Amplitude and Phase Noise of Magnons.

The low-frequency amplitude and phase noise spectra of magnetization waves, i.e. magnons, was measured in the yttrium iron garnet (YIG) waveguides. This type of noise, which originates from the fluctuations of the physical properties of the YIG crystals, has to be taken into account in the design of YIG-based RF generators and magnonic devices for data processing, sensing and imaging applications. It was found that the amplitude noise level of magnons depends strongly on the power level, increasing sharply at the on-set of nonlinear dissipation. The noise spectra of both the amplitude and phase noise have the Lorentzian shape with the characteristic frequencies below 100 Hz.
A New Calibration of Star Formation Rate in Galaxies Based on Polycyclic Aromatic Hydrocarbon Emission

Polycyclic aromatic hydrocarbon (PAH) emission has long been proposed to be a potential star formation rate indicator, as it arises from the photodissociation region bordering the Strömgren sphere of young, massive stars. We apply a recently developed technique of mid-infrared spectral decomposition to obtain a uniform set of PAH measurements from Spitzer low-resolution spectra of a large sample of star-forming galaxies spanning a wide range in stellar mass (M⋆ ≈ 106–1011.4 M⊙) and star formation rate (∼0.1–2000 M⊙ yr−1). High-resolution spectra are also analyzed to measure [Ne ii] 12.8 μm and [Ne iii] 15.6 μm, which effectively trace the Lyman continuum. We present a new relation between PAH luminosity and star formation rate based on the [Ne ii] and [Ne iii] lines. Calibrations are given for the integrated 5–15 μm PAH emission, the individual features at 6.2, 7.7, 8.6, and 11.3 μm, as well as several mid-infrared bandpasses sensitive to PAH. We confirm that PAH emission is suppressed in low-mass dwarf galaxies, and we discuss the possible physical origin of this effect.
Learning Adaptive Display Exposure for Real-Time Advertising

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning

For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of training data is not good enough. In the real world, the quality of datasets without manually check cannot be guaranteed, even Google Play may contain malicious applications, which will cause the trained model failure. To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets. The proposed model utilizes genetic algorithm to help find the best combination of the component learners and improve robustness of the model. Our results show that the proposed approach achieves a more robust performance than other approaches in the same area.