Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Frequency aware Perceptual Image Enhancement

In this paper, we introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods that help the network extract information from high-frequency domains to reconstruct clearer images. Our model can be applied to multi-scale image enhancement problems including denoising, deblurring and single image super-resolution. Experiments on SIDD, Flickr2K, DIV2K, and REDS datasets show that our method achieves state-of-the-art performance on each task. Furthermore, we show that our model can overcome the over-smoothing problem commonly observed in existing PSNR-oriented methods and generate more natural high-resolution images by applying adversarial training.
A vortex sheet based analytical model of the curled wake behind yawed wind turbines

Abstract Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.
CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning

Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizing patients by severity of the disease. In this article we adopted an approach based on using an ensemble of deep convolutional neural networks for segmentation of slices of lung CT scans. Using our models, we are able to segment the lesions, evaluate patients’ dynamics, estimate relative volume of lungs affected by lesions, and evaluate the lung damage stage. Our models were trained on data from different medical centers. We compared predictions of our models with those of six experienced radiologists, and our segmentation model outperformed most of them. On the task of classification of disease severity, our model outperformed all the radiologists.
IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization

We propose a novel paradigm of integration of Grover's algorithm in a machine learning framework: the inductive Grover oracular quantum neural network (IGO-QNN). The model defines a variational quantum circuit with hidden layers of parameterized quantum neurons densely connected via entangle synapses to encode a dynamic Grover's search oracle that can be trained from a set of database-hit training examples. This widens the range of problem applications of Grover's unstructured search algorithm to include the vast majority of problems lacking analytic descriptions of solution verifiers, allowing for quadratic speed-up in unstructured search for the set of search problems with relationships between input and output spaces that are tractably underivable deductively. This generalization of Grover's oracularization may prove particularly effective in deep reinforcement learning, computer vision, and, more generally, as a feature vector classifier at the top of an existing model.
Tracking Captured Variables in Types

Type systems usually characterize the shape of values but not their free variables. However, there are many desirable safety properties one could guarantee if one could track how references can escape. For example, one may implement algebraic effect handlers using capabilities -- a value which permits one to perform the effect -- safely if one can guarantee that the capability itself does not escape the scope bound by the effect handler. To this end, we study the $\textrm{CF}_{<:}$ calculus, a conservative and lightweight extension of $\textrm{System F}_{<:}$, to track how values and their references can be captured and escape. We show that existing terms in $\textrm{System F}_{<:}$ embed naturally in our calculus, and that many natural problems can be expressed in a system that tracks variable references like we do in $\textrm{CF}_{<:}$. We also give mechanized proofs of the soundness properties of $\textrm{CF}_{<:}$ in Coq. The type system presented in $\textrm{CF}_{<:}$ is powerful enough to reason about safety in the context of many natural extensions of $\textrm{CF}_{<:}$ such as region-based memory-management, non-local returns, and effect handlers.
Personalized Transformer for Explainable Recommendation

Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendation-explanation pipeline. Extensive experiments show that our small unpretrained model outperforms fine-tuned BERT on the generation task, in terms of both effectiveness and efficiency, which highlights the importance and the nice utility of our design.
Improving THz Coverage for 6G URLLC Services via Exploiting Mobile Computing

Terahertz (THz) communication (0.1-10 THz) is regarded as a promising technology, which provides rich available bandwidth and high data rate of terahertz bit per second (Tbps). However, THz signals suffer from high path loss, which profoundly decreases the transmission distance. To improve THz coverage, we consider the aid of mobile computing. Specifically, job offloading decision in mobile computing and frequency allocation in communication are co-designed to maximize distance and concurrently support ultra-reliable low-latency communications (URLLC) services for the sixth-generation (6G) mobile communication. Further, the above optimization problem is non-convex, then an effective and low-complexity method is proposed via exploiting the special structure of this problem. Finally, numerical results verify the effectiveness of our work.
On the Erdős-Pósa property for long holes in C4-free graphs

We prove that there exists a function $f(k)=\mathcal{O}(k^2 \log k)$ such that for every $C_4$-free graph $G$ and every $k \in \mathbb{N}$, $G$ either contains $k$ vertex-disjoint holes of length at least $6$, or a set $X$ of at most $f(k)$ vertices such that $G-X$ has no hole of length at least $6$. This answers a question of Kim and Kwon [Erd\H{o}s-P\'osa property of chordless cycles and its applications. JCTB 2020].
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems

We consider the setting of vector valued non-linear dynamical systems $X_{t+1} = \phi(A^* X_t) + \eta_t$, where $\eta_t$ is unbiased noise and $\phi : \mathbb{R} \to \mathbb{R}$ is a known link function that satisfies certain {\em expansivity property}. The goal is to learn $A^*$ from a single trajectory $X_1,\cdots,X_T$ of {\em dependent or correlated} samples. While the problem is well-studied in the linear case, where $\phi$ is identity, with optimal error rates even for non-mixing systems, existing results in the non-linear case hold only for mixing systems. In this work, we improve existing results for learning nonlinear systems in a number of ways: a) we provide the first offline algorithm that can learn non-linear dynamical systems without the mixing assumption, b) we significantly improve upon the sample complexity of existing results for mixing systems, c) in the much harder one-pass, streaming setting we study a SGD with Reverse Experience Replay ($\mathsf{SGD-RER}$) method, and demonstrate that for mixing systems, it achieves the same sample complexity as our offline algorithm, d) we justify the expansivity assumption by showing that for the popular ReLU link function -- a non-expansive but easy to learn link function with i.i. d. samples -- any method would require exponentially many samples (with respect to dimension of $X_t$) from the dynamical system. We validate our results via. simulations and demonstrate that a naive application of SGD can be highly sub-optimal. Indeed, our work demonstrates that for correlated data, specialized methods designed for the dependency structure in data can significantly outperform standard SGD based methods.
The Launching of Cosmic Ray Driven Outflows

Cosmic rays (CRs) are thought to be an important feedback mechanism in star-forming galaxies. They can provide an important source of pressure support and possibly drive outflows. We perform multidimensional CRmagnetohydrodynamic simulations including transport by streaming and diffusion to investigate wind launching from an initially hydrostatic atmosphere by CRs. We estimate a characteristic Eddington limit on the CR flux for which the CR force exceeds gravity and compare it to simulated systems. Scaling our results to conditions in starforming galaxies, we find that CRs are likely to contribute to driving outflows for a broad range of star formation environments. We quantify the momentum and energy transfer between CRs and gas, along with the associated mass outflow rates under different assumptions about the relative importance of streaming and diffusion for transport. In simulations with streaming, we observe the growth and saturation of the CR acoustic instability, but the CRs and gas remain well coupled, with CR momentum transferred efficiently to the gas even when this instability is present. Higher CR fluxes transferr more energy to the gas and drive stronger outflows. When streaming is present, most of the transferred energy takes the form of Alfvén wave heating of the gas, raising its pressure and internal energy, with a lower fractional contribution to the kinetic energy of the outflow. We also consider runs with radiative cooling, which modifies gas temperature and pressure profiles but does not seem to have a large impact on the mass outflow for super-Eddington CR fluxes.
Hyperparameter Selection for Imitation Learning

We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms in the context of continuous-control, when the underlying reward function of the demonstrating expert cannot be observed at any time. The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting. Indeed, would this reward function be available, it could then directly be used for policy training and imitation would not be necessary. To tackle this mostly ignored problem, we propose a number of possible proxies to the external reward. We evaluate them in an extensive empirical study (more than 10'000 agents across 9 environments) and make practical recommendations for selecting HPs. Our results show that while imitation learning algorithms are sensitive to HP choices, it is often possible to select good enough HPs through a proxy to the reward function.
Investigaton of Kpong carbonatite as a potential source for rare earth elements (REEs) using instrumental neutron activation analysis (INAA)

Abstract Instrumental neutron activation analysis (INAA) was used to investigate REEs in carbonatite from Kpong southeastern, Ghana. Total rare earth element (TREEs) obtain were in the range of 540 mg/kg to 705 mg/kg. The total number of rare earth elements (REEs) determined by INAA in the carbonatite rocks from Kpong were 11, namely; La, Ce, Nd, Sm, Eu, Tb, Dy, Ho, Tm, Yb, Lu. The INAA results from the carbonatite show a high enrichment of light rare earth elements (LREEs) deposits, marking the Kpong carbonatite as a potential REE source.
Convergence of the height process of supercritical Galton-Watson forests with an application to the configuration model in the critical window

We show joint convergence of the Lukasiewicz path and height process for slightly supercritical Galton-Watson forests. This shows that the height processes for supercritical continuous state branching processes as constructed by Lambert (2002) are the limit under rescaling of their discrete counterpart. Unlike for (sub-)critical Galton-Watson forests, the height process does not encode the entire metric structure of a supercritical GaltonWatson forest. We demonstrate that this result can nevertheless be used, by applying it to the configuration model with an i.i. d. power-law degree sequence in the critical window, of which we obtain the metric space scaling limit in the Gromov-Hausdorff-Prokhorov product topology, which is of individual interest.
An Integrated Dynamic Method for Allocating Roles and Planning Tasks for Mixed Human-Robot Teams

This paper proposes a novel dynamic method based on Behavior Trees (BTs) that integrates planning and allocation of tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal and logic constraints. In this way, instead of formulating an offline centralized optimization problem, the role allocation problem is solved with multiple simplified online optimization sub-problems, without complex and cross-schedule task dependencies. These sub-problems are defined as Mixed-Integer Linear Programs (MILPs), that, according to the worker-actions related costs and the workers’ availability, allocate the yet-to-execute tasks among the available workers. To characterize the behavior of the developed method, we opted to perform different simulation experiments, in which the results of the action-worker allocation and the computational complexity are evaluated. The obtained results, due to the nature of the algorithm and to the possibility of simulating the agents’ behavior, illustrate adequately also how the algorithm performs in real experiments.
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem

Growing advancements in reinforcement learning has led to advancements in control theory. Reinforcement learning has effectively solved the inverted pendulum problem and more recently the double inverted pendulum problem. In reinforcement learning, our agents learn by interacting with the control system with the goal of maximizing rewards. In this paper, we explore three such reward functions in the cart position problem. This paper concludes that a discontinuous reward function that gives non-zero rewards to agents only if they are within a given distance from the desired position gives the best results.
The Brittle Boulders of Dwarf Planet Ceres

We mapped all boulders larger than 105 m on the surface of dwarf planet Ceres using images of the Dawn framing camera acquired in the Low Altitude Mapping Orbit. We find that boulders on Ceres are more numerous toward high latitudes and have a maximum lifetime of 150 ± 50 Ma, based on crater counts. These characteristics are distinctly different from those of boulders on asteroid (4) Vesta, an earlier target of Dawn, which implies that Ceres’ boulders are mechanically weaker. Clues to their properties can be found in the composition of Ceres’ complex crust, which is rich in phyllosilicates and salts. As water ice is thought to be present only meters below the surface, we suggest that boulders also harbor ice. Furthermore, the boulder size–frequency distribution is best fit by a Weibull distribution rather than the customary power law, just like for Vesta boulders. This finding is robust in light of possible types of size measurement error.
VAD360: Viewport Aware Dynamic 360-Degree Video Frame Tiling

360{\deg} videos a.k. a. spherical videos are getting popular among users nevertheless, omnidirectional view of these videos demands high bandwidth and processing power at the end devices. Recently proposed viewport aware streaming mechanisms can reduce the amount of data transmitted by streaming a limited portion of the frame covering the current user viewport (VP). However, they still suffer from sending a high amount of redundant data, as the fixed tile mechanisms can not provide finer granularity to the user VP. Though making the tiles smaller can provide a finer granularity for user viewport, high encoding overhead incurred. To overcome this trade-off, in this paper, we present a computational geometric approach based adaptive tiling mechanism named VAD360, which takes visual attention information on the 360{\deg} video frame as the input and provide a suitable non-overlapping variable size tile cover on the frame. Experimental results shows that VAD360 can save up to 31.1% of pixel redundancy before compression and 35.4% of bandwidth saving compared to recently proposed fixed tile configurations, providing tile schemes within 0.98($\pm$0.11)s time frame.
Self-consistent Stellar Radial Velocities from LAMOST Medium-resolution Survey DR7

Radial velocity (RV) is among the most fundamental physical quantities obtainable from stellar spectra and is rather important in the analysis of time-domain phenomena. LAMOST Medium-resolution Survey (MRS) DR7 contains five million single-exposure stellar spectra with spectral resolution R ∼ 7500. However, the temporal variation of the RV zero-points (RVZPs) of the MRS, which makes the RVs from multiple epochs inconsistent, has not been addressed. In this paper, we measure the RVs of 3.8 million single-exposure spectra (for 0.6 million stars) with signal-to-noise ratios (S/N) higher than 5 based on the cross-correlation function method, and propose a robust method to self-consistently determine the RVZPs exposure by exposure for each spectrograph with the help of Gaia DR2 RVs. Such RVZPs are estimated for 3.6 million RVs and can reach a mean precision of ∼0.38 km s−1. The result of the temporal variation of RVZPs indicates that our algorithm is efficient and necessary before we use the absolute RVs to perform time-domain analyses. Validating the results with APOGEE DR16 shows that our absolute RVs can reach an overall precision of 0.84/0.80 km s−1 in the blue/red arm at 50 < S/N < 100 and of 1.26/1.99 km s−1 at 5 < S/N < 10. The cumulative distribution function of the standard deviations of multiple RVs (N obs ≥ 8) for 678 standard stars reaches 0.45/0.54, 1.07/1.39, and 1.45/1.86 km s−1 in the blue/red arm at the 50%, 90%, and 95% levels, respectively. Catalogs of the RVs, RVZPs, and selected candidate RV standard stars are available at https://github.com/hypergravity/paperdata.
Predicting Links on Wikipedia with Anchor Text Information

Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of documents bound together by internal hyperlinks. These links form the building blocks of a large network whose structure contains important information on the concepts covered in this encyclopedia. The presence of a link between two articles, materialised by an anchor text in the source page pointing to the target page, can increase readers' understanding of a topic. However, the process of linking follows specific editorial rules to avoid both under-linking and over-linking. In this paper, we study the transductive and the inductive tasks of link prediction on several subsets of the English Wikipedia and identify some key challenges behind automatic linking based on anchor text information. We propose an appropriate evaluation sampling methodology and compare several algorithms. Moreover, we propose baseline models that provide a good estimation of the overall difficulty of the tasks.
OFEI: A Semi-Black-Box Android Adversarial Sample Attack Framework Against DLaaS

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are often deceived by the adversarial samples elaborated by attackers. In this paper, we propose a new semi-black-box attack framework called one-feature-each-iteration (OFEI) to craft Android adversarial samples. This framework modifies as few features as possible and requires less classifier information to fool the classifier. We conduct a controlled experiment to evaluate our OFEI framework by comparing it with the benchmark methods JSMF, GenAttack and pointwise attack. The experimental results show that our OFEI has a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the traditional white-box attack methods in the image field, such as fast gradient sign method (FGSM) and DeepFool, to craft adversarial samples for Android. Finally, to enhance the security of DLaaS, we use two uncertainties of the Bayesian neural network to construct the combined uncertainty, which is used to detect adversarial samples and achieves a high detection rate of 99.28%.
Logarithmic criticality in transverse thermoelectric conductivity of the ferromagnetic topological semimetal CoMnSb

Hiroto Nakamura, ∗ Susumu Minami, Takahiro Tomita, 3 Agustinus Agung Nugroho, and Satoru Nakatsuji 2, 3, 5, 6, † Institute for Solid State Physics, University of Tokyo, Kashiwa 277-8581, Japan Department of Physics, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan CREST, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesha 10, 40132 Bandung, Indonesia Trans-Scale Quantum Science Institute, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan Institute for Quantum Matter and Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA (Dated: May 26, 2021)