Miracle-dz/UniFAR-FTUs huggingface dataset:
Radio Linear Polarization of GRB Afterglows: Instrumental Systematics in ALMA Observations of GRB 171205A

Polarization measurements of gamma-ray burst (GRB) afterglows are a promising means of probing the structure, geometry, and magnetic composition of relativistic GRB jets. However, a precise treatment of instrumental calibration is vital for a robust physical interpretation of polarization data, requiring tests of and validations against potential instrumental systematics. We illustrate this with Atacama Large Millimeter/Sub-millimeter Array (ALMA) Band 3 (97.5 GHz) observations of GRB 171205A taken ≈5.19 days after the burst, where a detection of linear polarization was recently claimed. We describe a series of tests for evaluating the stability of polarization measurements with ALMA. Using these tests to reanalyze and evaluate the archival ALMA data, we uncover systematics in the polarization calibration at the ≈0.09% level. We derive a 3σ upper limit on the linearly polarized intensity of P < 97.2 μJy, corresponding to an upper limit on the linear fractional polarization of ΠL < 0.30%, in contrast to the previously claimed detection. Our upper limit improves upon existing constraints on the intrinsic polarization of GRB radio afterglows by a factor of 3. We discuss this measurement in the context of constraints on the jet magnetic field geometry. We present a compilation of polarization observations of GRB radio afterglows, and demonstrate that a significant improvement in sensitivity is desirable for eventually detecting signals polarized at the ≈0.1% level from typical radio afterglows.
Quantum computation of three-wave interactions with engineered cubic couplings

Quantum simulation hardware usually lacks native cubic couplings, which are essential building blocks in many physics applications. Nevertheless, we demonstrate that effective three-wave vertices can be realized without hardware modification. In particular, for the three-wave Hamiltonian of laser-plasma interactions, we show that its Hilbert space can be decomposed into a direct sum of D-dimensional subspaces. Within each subspace, physical states are readily mapped to quantum memory, and the Hamiltonian matrix becomes tridiagonal. The resultant unitary evolution is realized using two qubits on state-of-the-art hardware through quantum cloud services, which approximate the three-wave gate as products of ~20 standard gates. This trotterization approach allows ~10 repetitions of the three-wave gate before results are corrupted by decoherence. As an alternative approach, the unitary evolution is also realized as a single gate using customized control pulses on a tramsnon qudit. Utilizing the lowest three levels of the qudit, high-fidelity results are obtained for ~100 three-wave gate repetitions. Moreover, reliable control pulses may also be synthesized cheaply using interpolation when parameters of the Hamiltonian deviate from those used in numerical optimization. Our results highlight the advantage of using customized gates in physics applications. The generalized multi-wave gates are potentially valuable tools for computing a large class of problems in nonlinear optics, weak turbulence, and lattice gauge theories.
Coreferential Reasoning Learning for Language Representation

Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning. However, existing language representation models seldom consider coreference explicitly, the relationship between noun phrases referring to the same entity, which is essential to a coherent understanding of the whole discourse. To address this issue, we present CorefBERT, a novel language representation model designed to capture the relations between noun phrases that co-refer to each other. According to the experimental results, compared with existing baseline models, the CorefBERT model has made significant progress on several downstream NLP tasks that require coreferential reasoning, while maintaining comparable performance to previous models on other common NLP tasks.
Dark Experience for General Continual Learning: a Strong, Simple Baseline

Neural networks struggle to learn continuously, as they forget the old knowledge catastrophically whenever the data distribution changes over time. Recently, Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable. We work towards General Continual Learning (GCL), where task boundaries blur and the domain and class distributions shift either gradually or suddenly. We address it through Dark Experience Replay, namely matching the network's logits sampled throughout the optimization trajectory, thus promoting consistency with its past. By conducting an extensive analysis on top of standard benchmarks, we show that such a seemingly simple baseline outperforms consolidated approaches and leverages limited resources. To provide a better understanding, we further introduce MNIST-360, a novel GCL evaluation setting.
A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box

Currently, reliable and accurate ship detection in optical remote sensing images is still challenging. Even the state-of-the-art convolutional neural network (CNN) based methods cannot obtain very satisfactory results. To more accurately locate the ships in diverse orientations, some recent methods conduct the detection via the rotated bounding box. However, it further increases the difficulty of detection, because an additional variable of ship orientation must be accurately predicted in the algorithm. In this paper, a novel CNN-based ship detection method is proposed, by overcoming some common deficiencies of current CNN-based methods in ship detection. Specifically, to generate rotated region proposals, current methods have to predefine multi-oriented anchors, and predict all unknown variables together in one regression process, limiting the quality of overall prediction. By contrast, we are able to predict the orientation and other variables independently, and yet more effectively, with a novel dual-branch regression network, based on the observation that the ship targets are nearly rotation-invariant in remote sensing images. Next, a shape-adaptive pooling method is proposed, to overcome the limitation of typical regular ROI-pooling in extracting the features of the ships with various aspect ratios. Furthermore, we propose to incorporate multilevel features via the spatially-variant adaptive pooling. This novel approach, called multilevel adaptive pooling, leads to a compact feature representation more qualified for the simultaneous ship classification and localization. Finally, detailed ablation study performed on the proposed approaches is provided, along with some useful insights. Experimental results demonstrate the great superiority of the proposed method in ship detection.
Standardizing and Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing

Time-critical analysis of social media streams is important for humanitarian organizations to plan rapid response during disasters. The crisis informatics research community has developed several techniques and systems to process and classify big crisis related data posted on social media. However, due to the dispersed nature of the datasets used in the literature, it is not possible to compare the results and measure the progress made towards better models for crisis informatics. In this work, we attempt to bridge this gap by standardizing various existing crisis-related datasets. We consolidate labels of eight annotated data sources and provide 166.1k and 141.5k tweets for informativeness and humanitarian classification tasks, respectively. The consolidation results in a larger dataset that affords the ability to train more sophisticated models. To that end, we provide baseline results using CNN and BERT models. We make the dataset available at this https URL.
A Habitable-zone Earth-sized Planet Rescued from False Positive Status

We report the discovery of an Earth-sized planet in the habitable zone of a low-mass star called Kepler-1649. The planet, Kepler-1649 c, is 1.06 times the size of Earth and transits its 0.1977 ± 0.0051 “mid” M-dwarf host star every 19.5 days. It receives 74% ± 3% the incident flux of Earth, giving it an equilibrium temperature of 234 ± 20 K and placing it firmly inside the circumstellar habitable zone. Kepler-1649 also hosts a previously known inner planet that orbits every 8.7 days and is roughly equivalent to Venus in size and incident flux. Kepler-1649 c was originally classified as a false positive (FP) by the Kepler pipeline, but was rescued as part of a systematic visual inspection of all automatically dispositioned Kepler FPs. This discovery highlights the value of human inspection of planet candidates even as automated techniques improve, and hints that terrestrial planets around mid to late M-dwarfs may be more common than those around more massive stars.
Resolution-dependent variations of sinking Lagrangian particles in general circulation models

Any type of non-buoyant material in the ocean is transported horizontally by currents during its sinking journey. This lateral transport can be far from negligible for small sinking velocities. To estimate its magnitude and direction, the material is often modelled as a set of Lagrangian particles advected by current velocities that are obtained from Ocean General Circulation Models (OGCMs). State-of-the-art OGCMs are strongly eddying, providing results with a spatial resolution in the order of 10 km on a daily frequency. Many long term climate modelling simulations (e.g. in paleoclimate) rely on lower spatial resolution models that do not capture mesoscale features. It remains unclear how much the absence of mesoscale features in low-resolution models influences the Lagrangian particle transport. In this study, we simulate the transport of sinking Lagrangian particles using low- and high-resolution OGCMs, and assess the lateral transport differences resulting from the difference in spatial and temporal model resolution. We find major differences between the transport in the non-eddying OGCM and in the eddying OGCM. Addition of stochastic noise to the particle trajectories in the non-eddying OGCM parameterises the effect of eddies well in some cases (e.g. in the North Pacific gyre). The effect of a coarser temporal resolution (5-daily versus monthly) is smaller compared to a coarser spatial resolution (0.1$^{\circ}$ versus 1$^{\circ}$ horizontally). We recommend to apply sinking Lagrangian particles only with velocity fields from eddying OGCMs, requiring high-resolution models in e.g. paleoceanographic studies. To increase the accessibility of our particle trace simulations, we launch this http URL, an online tool to reconstruct the surface origin of sedimentary particles in a specific location.
Imaging metasurfaces based on graphene-loaded slot antennas.

Spectral imagers, the classic example being the color camera, are ubiquitous in everyday life. However, most such imagers rely on filter arrays that absorb light outside each spectral channel, yielding ∼1/N efficiency for an N-channel imager. This is especially undesirable in thermal infrared (IR) wavelengths, where sensor detectivities are low. We propose an efficient and compact thermal infrared spectral imager comprising a metasurface composed of sub-wavelength-spaced, differently-tuned slot antennas coupled to photosensitive elements. Here, we demonstrate this idea using graphene, which features a photoresponse up to thermal IR wavelengths. The combined antenna resonances yield broadband absorption in the graphene exceeding the 1/N efficiency limit. We establish a circuit model for the antennas' optical properties and demonstrate consistency with full-wave simulations. We also theoretically demonstrate ∼58% free space-to-graphene photodetector coupling efficiency, averaged over the 1050 cm-1 to 1700 cm-1 wavenumber range, for a four-spectral-channel gold metasurface with a 0.883 µm by 6.0 µm antenna pitch. This research paves the way towards compact CMOS-integrable thermal IR spectral imagers.
A Simple Transfer Learning Baseline for Ellipsis Resolution.

Most, if not all, forms of ellipsis (e.g., 'so does Mary') are similar to reading comprehension questions ('what does Mary do'), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. We present a strong baseline for English ellipsis resolution that exploits this similarity by relying on architectures developed for machine reading comprehension. We present both single-task transfer learning models and joint models, trained on machine reading comprehension and coreference resolution datasets, clearly outperforming the current (from 2016-18) state of the art for Sluice Ellipsis (from 0.67 to 0.86 F1) and Verb Phrase Ellipsis (from 0.65 to 0.79 F1).
A cold and diffuse giant molecular filament in the region of l = 41°, b = −1°

Data of $^{12}$CO/$^{13}$CO/C$^{18}$O $J=1\to0$ emission toward the Galactic plane region of $l=35^\circ$ to $45^\circ$ and $b= -5^\circ$ to $+5^\circ$ are available with the Milky Way Imaging Scroll Painting (MWISP) project. Using the data, we found a giant molecular filament (GMF) around $l\approx38\sim42^\circ$, $b\approx-3.5\sim0^\circ$, $V_{LSR} \approx 27 \sim 40$ km~s$^{-1}$, named the GMF MWISP G041-01. At a distance of 1.7 kpc, the GMF is about 160 pc long. With a median excitation temperature about 7.5 K and a median column density about $10^{21}$ cm$^{-2}$, this GMF is very cold and very diffuse compared to known GMFs. Using the morphology in the data cube, the GMF is divided into four components among which three show filamentary structure. Masses of the components are $ 10^3 \sim 10^4 M_\odot$, with a total mass for the whole filament being about $7\times10^4 M_\odot$ from the LTE method. $^{13}$CO cores inside each component are searched. Virial parameters are about 2.5 for these cores and have a power-law index of -0.34 against the mass. The mass fraction of dense cores traced by $^{13}$CO to the diffuse clouds traced by $^{12}$CO are about 7% for all components of the GMF. We found signatures of possible large scale filament-filament collision in the GMF.
Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images

The neural network needs excessive costs of time because of the complexity of architecture when trained on images. Transfer learning and fine-tuning can help improve time and cost efficiency when training a neural network. Yet, Transfer learning and fine-tuning needs a lot of experiment to try with. Therefore, a method to find the best architecture for transfer learning and fine-tuning is needed. To overcome this problem, neuro-evolution using a genetic algorithm can be used to find the best architecture for transfer learning. To check the performance of this study, dataset ChestX-Ray 14 and DenseNet-121 as a base neural network model are used. This study used the AUC score, differences in execution time for training, and McNemar's test to the significance test. In terms of result, this study got a 5% difference in the AUC score, 3 % faster in terms of execution time, and significance in most of the disease detection. Finally, this study gives a concrete summary of how neuro-evolution transfer learning can help in terms of transfer learning and fine-tuning.
Musical Features for Automatic Music Transcription Evaluation

This technical report gives a detailed, formal description of the features introduced in the paper: Adrien Ycart, Lele Liu, Emmanouil Benetos and Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription", Transactions of the International Society for Music Information Retrieval (TISMIR), Accepted, 2020.
WhiteLab 2.0. A web interface for corpus exploitation

The OpenSoNaR-CGN project set out to develop WhiteLab 2.0 for the online exploitation of the SoNaR-500 and CGN corpora. Important changes in comparison to the first version of WhiteLab are the addition of audio support and support for multiple corpora. The web interface has been redeveloped and adapted to accommodate these changes. At the backend, WhiteLab 2.0 comes with a new data importer and plugin for Neo4j, while also remaining compatible with BlackLab. Although performance of the new backend is not yet up to par with BlackLab, the investment in new technology that will likely be further developed is expected to make the application more future-proof and a great addition to the set of tools available to the humanities.
Primal–Dual Methods for Large-Scale and Distributed Convex Optimization and Data Analytics

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given “difficult” (constrained) problem via finding solutions of a sequence of “easier” (often unconstrained) subproblems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables. ALM is highly flexible with respect to how primal subproblems can be solved, giving rise to a plethora of different primal–dual methods. The powerful ALM mechanism has recently proved to be very successful in various large-scale and distributed applications. In addition, several significant advances have appeared, primarily on precise complexity results with respect to computational and communication costs in the presence of inexact updates and design and analysis of novel optimal methods for distributed consensus optimization. We provide a tutorial-style introduction to ALM and its variants for solving convex optimization problems in large-scale and distributed settings. We describe control-theoretic tools for the algorithms’ analysis and design, survey recent results, and provide novel insights into the context of two emerging applications: federated learning and distributed energy trading.
Non-Classical Semiconductor Photon Sources Enhancing the Performance of Classical Target Detection Systems

We demonstrate and analyze how deploying non-classical intensity correlations obtained from a monolithic semiconductor quantum photon source can enhance classical target detection systems. This is demonstrated by examining the advantages offered by the utilization of the non-classical correlations in a correlation based target detection protocol. We experimentally demonstrate that under the same condition, the target contrast obtained from the protocol when non-classical correlations are utilized exhibits an improvement of up to 17.79 dB over the best classical intensity correlation-based target detection protocol [1], under 29.69dB channel loss and excess noise 13.40dB stronger than the probe signal. We also assessed how the strong frequency correlations within the non-classical photon pairs can be used to further enhance this protocol.
Speech Enhancement of Noisy and Reverberant Speech for Text-to-Speech

Text-to-speech voices created from noisy and reverberant recordings are of lower quality. A simple way to improve this is to increase the quality of the recordings prior to text-to-speech training with speech enhancement methods such as noise suppression and dereverberation. In this paper, we opted for this approach and to perform the enhancement, we used a recurrent neural network. The network is trained with parallel data of clean and lower quality recordings of speech. The lower quality data was artificially created by adding recordings of environmental noise to studio-quality recordings of speech and by convolving room impulse responses with these clean recordings. We trained separate networks with noise-only, reverberation-only, and both reverberation and additive noise data. The quality of voices trained with lower quality data that has been enhanced using these networks was significantly higher in all cases. For the noise-only case, the enhanced synthetic voice ranked as high as the voice trained with clean data. For the most realistic and challenging scenario, when both noise and reverberation were present, the improvements were more modest, but still significant.
Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks

Most V2X (Vehicle-to-Everything) applications rely on broadcasting awareness messages known as CAM (Cooperative Awareness Messages) in ETSI or BSM (Basic Safety Message) in SAE standards. A large number of studies have been devoted to guarantee their reliable transmission. However, to date, the studies are generally based on simplified data traffic models that generate awareness messages at periodic intervals or with a constant message size. These models do not accurately represent the real generation of CAM messages that follow specific mobility-based rules. Using simplified and unrealistic traffic models can significantly impact the results and validity of the studies, and hence accurate models for the generation of awareness messages are necessary. This paper proposes the first set of models that can realistically generate CAM messages. The models have been created from real traces collected by two car manufacturers in urban, sub-urban and highway test drives. The models are based on mth order Markov sources, and model the size of CAMs and the time interval between CAMs. The models are openly provided to the community and can be easily integrated into any simulator.
Artificial Intelligence Control Applied in Wind Energy Conversion System

The objective of this paper is to study the dynamic response of the wind energy conversion system (WECS) based on the Doubly Fed Induction Generator (DFIG). The DFIG rotor is connected to the grid via a converter. The active and reactive power control is realized by the DFIG rotor variables control, using the field oriented control (FOC). The vector control of DFIG is applied by the use of tow regulators PI and the neural network regulator (NN). The generator mathematical model is implemented in Matlab/ Simulink software to simulate a DFIG of 1.5 MW in order to show the efficiency of the performances and robustness of the studied control systems. The simulation obtained results shows that the robustness and response time of the neural network regulator is better than those obtained by the PI classical regulator.
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making

There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.