Miracle-dz/UniFAR-FTUs huggingface dataset:
Null geodesics and improved unique continuation for waves in asymptotically anti-de Sitter spacetimes

We consider the question of whether solutions of Klein–Gordon equations on asymptotically anti-de Sitter spacetimes can be uniquely continued from the conformal boundary. Positive answers were first given in [, ], under suitable assumptions on the boundary geometry and with boundary data imposed over a sufficiently long timespan. The key step was to establish Carleman estimates for Klein–Gordon operators near the conformal boundary. In this article, we further improve upon the above-mentioned results. First, we establish new Carleman estimates—and hence new unique continuation results—for Klein–Gordon equations on a larger class of spacetimes than in [, ], in particular with more general boundary geometries. Second, we argue for the optimality, in many respects, of our assumptions by connecting them to trajectories of null geodesics near the conformal boundary; these geodesics play a crucial role in the construction of counterexamples to unique continuation. Finally, we develop a new covariant formalism that will be useful—both presently and more generally beyond this article—for treating tensorial objects with asymptotic limits at the conformal boundary.
Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

Abstract. Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy δ1:25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions.
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation

Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous driving, robotics, and augmented reality, where efficiency is paramount. This paper proposes Mesorasi, an algorithm-architecture co-designed system that simultaneously improves the performance and energy efficiency of point cloud analytics while retaining its accuracy.Our extensive characterizations of state-of-the-art point cloud algorithms show that, while structurally reminiscent of convolutional neural networks (CNNs), point cloud algorithms exhibit inherent compute and memory inefficiencies due to the unique characteristics of point cloud data. We propose delayed-aggregation, a new algorithmic primitive for building efficient point cloud algorithms. Delayed-aggregation hides the performance bottlenecks and reduces the compute and memory redundancies by exploiting the approximately distributive property of key operations in point cloud algorithms. Delayed-aggregation let point cloud algorithms achieve 1.6× speedup and 51.1% energy reduction on a mobile GPU while retaining the accuracy (-0.9% loss to 1.2% gains). To maximize the algorithmic benefits, we propose minor extensions to contemporary CNN accelerators, which can be integrated into a mobile Systems-on-a-Chip (SoC) without modifying other SoC components. With additional hardware support, Mesorasi achieves up to 3.6× speedup.
A Biomimetic Tactile Fingerprint Induces Incipient Slip

We present a modified TacTip biomimetic optical tactile sensor design which demonstrates the ability to induce and detect incipient slip, as confirmed by recording the movement of markers on the sensor's external surface. Incipient slip is defined as slippage of part, but not all, of the contact surface between the sensor and object. The addition of ridges - which mimic the friction ridges in the human fingertip - in a concentric ring pattern allowed for localised shear deformation to occur on the sensor surface for a significant duration prior to the onset of gross slip. By detecting incipient slip we were able to predict when several differently shaped objects were at risk of falling and prevent them from doing so. Detecting incipient slip is useful because a corrective action can be taken before slippage occurs across the entire contact area thus minimising the risk of objects been dropped.
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?

How can we make software analytics simpler and faster? One method is to match the complexity of analysis to the intrinsic complexity of the data being explored. For example, hyperparameter optimizers find the control settings for data miners that improve the predictions generated via software analytics. Sometimes, very fast hyperparameter optimization can be achieved by “DODGE-ing”; i.e., simply steering way from settings that lead to similar conclusions. But when is it wise to use that simple approach and when must we use more complex (and much slower) optimizers? To answer this, we applied hyperparameter optimization to 120 SE data sets that explored bad smell detection, predicting Github issue close time, bug report analysis, defect prediction, and dozens of other non-SE problems. We find that the simple DODGE works best for data sets with low “intrinsic dimensionality” (<inline-formula><tex-math notation="LaTeX">$\mu _D\approx 3$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo>≈</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="menzies-ieq1-3073242.gif"/></alternatives></inline-formula>) and very poorly for higher-dimensional data (<inline-formula><tex-math notation="LaTeX">$\mu _D > 8$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo>></mml:mo><mml:mn>8</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="menzies-ieq2-3073242.gif"/></alternatives></inline-formula>). Nearly all the SE data seen here was intrinsically low-dimensional, indicating that DODGE is applicable for many SE analytics tasks.
Zero-Shot Domain Generalization

Standard supervised learning setting assumes that training data and test data come from the same distribution (domain). Domain generalization (DG) methods try to learn a model that when trained on data from multiple domains, would generalize to a new unseen domain. We extend DG to an even more challenging setting, where the label space of the unseen domain could also change. We introduce this problem as Zero-Shot Domain Generalization (to the best of our knowledge, the first such effort), where the model generalizes across new domains and also across new classes in those domains. We propose a simple strategy which effectively exploits semantic information of classes, to adapt existing DG methods to meet the demands of Zero-Shot Domain Generalization. We evaluate the proposed methods on CIFAR-10, CIFAR-100, F-MNIST and PACS datasets, establishing a strong baseline to foster interest in this new research direction.
An eclipsing M-dwarf close to the hydrogen burning limit from NGTS

We present the discovery of NGTS J0930−18, an extreme mass ratio eclipsing M-dwarf binary system with an early M-dwarf primary and a late M-dwarf secondary close to the hydrogen burning limit. Global modelling of photometry and radial velocities reveals that the secondary component (NGTS J0930−18 B) has a mass of M* = $0.0818 ^{+0.0040}_{-0.0015}$ M⊙ and radius of R* = $0.1059 ^{+0.0023}_{-0.0021}$ R⊙, making it one of the lowest mass stars with direct mass and radius measurements. With a mass ratio of q = $0.1407 ^{+0.0065}_{-0.017}$, NGTS J0930−18 has the lowest mass ratio of any known eclipsing M-dwarf binary system, posing interesting questions for binary star formation and evolution models. The mass and radius of NGTS J0930−18 B is broadly consistent with stellar evolutionary models. NGTS J0930−18 B lies in the sparsely populated mass radius parameter space close to the substellar boundary. Precise measurements of masses and radii from single lined eclipsing binary systems of this type are vital for constraining the uncertainty in the mass–radius relationship – of importance due to the growing number of terrestrial planets being discovered around low-mass stars.
We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation

Manual visual inspection, typically performed by certified inspectors, is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely on the inspector's experience. In this paper, we first introduce a disparity (or inverse depth) image processing module, named quasi inverse perspective transformation (QIPT), which can make the damaged road areas become highly distinguishable. Then, we propose a novel attention aggregation (AA) framework, which can improve the semantic segmentation networks for better road pothole detection, by taking the advantages of different types of attention modules. Moreover, we develop a novel training set augmentation technique based on adversarial domain adaptation, where synthetic road RGB images and transformed road disparity (or inverse depth) images are generated to enhance the training of semantic segmentation networks.The experimental results illustrate that, firstly, the disparity (or inverse depth) images transformed by our QIPT module become more informative; secondly, the adversarial domain adaptation can not only significantly improve the performance of the state-of-the-art semantic segmentation networks, but also accelerate their convergence. In addition, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection.
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition

Transformer-based models have achieved stateof-the-art results in many tasks in natural language processing. However, such models are usually slow at inference time, making deployment difficult. In this paper, we develop an efficient algorithm to search for fast models while maintaining model quality. We describe a novel approach to decompose the Transformer architecture into smaller components, and propose a sampling-based one-shot architecture search method to find an optimal model for inference. The model search process is more efficient than alternatives, adding only a small overhead to training time. By applying our methods to BERT-base architectures, we achieve 10% to 30% speedup for pre-trained BERT and 70% speedup on top of a previous state-of-the-art distilled BERT model on Cloud TPU-v2 with a generally acceptable drop in performance.
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy

Millions of drivers worldwide have enjoyed financial benefits and work schedule flexibility through a ride-sharing economy, but meanwhile they have suffered from the lack of a sense of identity and career achievement. Equipped with social identity and contest theories, financially incentivized team competitions have been an effective instrument to increase drivers' productivity, job satisfaction, and retention, and to improve revenue over cost for ride-sharing platforms. While these competitions are overall effective, the decisive factors behind the treatment effects and how they affect the outcomes of individual drivers have been largely mysterious. In this study, we analyze data collected from more than 500 large-scale team competitions organized by a leading ride-sharing platform, building machine learning models to predict individual treatment effects. Through a careful investigation of features and predictors, we are able to reduce out-sample prediction error by more than 24%. Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms. A simulated analysis demonstrates that by simply changing a few contest design options, the average treatment effect of a real competition is expected to increase by as much as 26%. Our procedure and findings shed light on how to analyze and optimize large-scale online field experiments in general.
Simulated diurnal pH fluctuations radically increase variance in—but not the mean of—growth in the barnacle Balanus improvisus

Shallow coastal waters are characterized by substantial diurnal fluctuations in pH, especially in nearshore environments. The biological effects of ocean acidification in combination with these natural fluctuations have received relatively little attention to date. We exposed multiple batches (≈ different genotypes) of newly settled barnacles, Balanus improvisus, to constant pH under “control” (pH 1⁄4 8.1) or “stable acidified” (pH 1⁄4 7.7) conditions, as well as a treatment that simulated the maximum diurnal pH fluctuations seen in the nearshore habitats where this barnacle lives (+0.2 pH units), superimposed on the stable acidified treatment (“fluctuating acidified”; 7.5 ≤ pH ≤ 7.9). We found that fluctuating acidification had no effect onmean response in growth and shell mineralogy, but caused an 20-fold increase in variance of responses, compared with stable acidification. In contrast to these results, we found no effect of fluctuating acidification on variances of response ratios for barnacle survival and shell strength. Similarly, mean survival did not vary significantly with pH. However, we observed a strong negative effect of stable and fluctuating acidification onmean shell strength.Our finding that barnacles responddifferently to fluctuating pH than to stable lowpH indicate the importance of including fluctuating acidification treatments when studying species that live in variable environments. Importantly, because phenotypic variance is the rawmaterial for natural selection, and thus lays at the heart of evolutionary responses to environmental variability and change, our findings also highlight the need to study changes in variance of—as well as mean—responses to changing ocean climates.
Coherent control with user-defined passage

Stimulated Raman adiabatic passage (STIRAP) is a standard technique to combat experimental imperfections and can be used to realize robust quantum state control, which has many applications in physics, chemistry, and beyond. However, STIRAP is susceptible to decoherence since it requires long evolution time. To overcome this problem, stimulated Raman ‘user-defined’ passage (STIRUP) is proposed, which allows users to design the passages unlike the STIRAP but fast and robust against both decoherence and experimental imperfections. Here, we further develop a more general STIRUP method. Comparing with shortcut to adiabaticity and its’ variants, the generalized STIRUP is more simpler and compatible with more complex energy-level structure and many-body systems. Furthermore, the generalized STIRUP has many important applications such as geometric phase measurement, coherent population transfer, and quantum state preparation. Specifically, as examples, we show how to realize the high-fidelity quantum state transfer and entangled state generation in a robust way via STIRUP with the state-of-the-art experimental superconducting circuits.
SGG: Spinbot, Grammarly and GloVe based Fake News Detection

Recently, news consumption using online news portals has increased exponentially due to several reasons, such as low cost and easy accessibility. However, such online platforms inadvertently also become the cause of spreading false information across the web. They are being misused quite frequently as a medium to disseminate misinformation and hoaxes. Such malpractices call for a robust automatic fake news detection system that can keep us at bay from such misinformation and hoaxes. We propose a robust yet simple fake news detection system, leveraging the tools for paraphrasing, grammar-checking, and word-embedding. In this paper, we try to the potential of these tools in jointly unearthing the authenticity of a news article. Notably, we leverage Spinbot (for paraphrasing), Grammarly (for grammar-checking), and GloVe (for word-embedding) tools for this purpose. Using these tools, we were able to extract novel features that could yield state-of-the-art results on the Fake News AMT dataset and comparable results on Celebrity datasets when combined with some of the essential features. More importantly, the proposed method is found to be more robust empirically than the existing ones, as revealed in our cross-domain analysis and multi-domain analysis.
POP909: A Pop-song Dataset for Music Arrangement Generation

Music arrangement generation is a subtask of automatic music generation, which involves reconstructing and re-conceptualizing a piece with new compositional techniques. Such a generation process inevitably requires reference from the original melody, chord progression, or other structural information. Despite some promising models for arrangement, they lack more refined data to achieve better evaluations and more practical results. In this paper, we propose POP909, a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, we provide the annotations of tempo, beat, key, and chords, where the tempo curves are hand-labeled and others are done by MIR algorithms. Finally, we conduct several baseline experiments with this dataset using standard deep music generation algorithms.
SARS-CoV-2 antibody responses in children with MIS-C and mild and severe COVID-19

SARS-CoV-2 antibody responses in children remain poorly characterized. Here, we show that pediatric patients with multisystem inflammatory syndrome in children (MIS-C) possess higher SARS-CoV-2 spike IgG titers compared to those with severe coronavirus disease 2019 (COVID-19), likely reflecting a longer time since onset of infection in MIS-C patients.
Assessing the Overhead of Offloading Compression Tasks

Exploring compression is increasingly promising as trade-off between computations and data movement. There are two main reasons: First, the gap between processing speed and I/O continues to grow, and technology trends indicate a continuation of this. Second, performance is determined by energy efficiency, and the overall power consumption is dominated by the consumption of data movements. For these reasons there is already a plethora of related works on compression from various domains. Most recently, a couple of accelerators have been introduced to offload compression tasks from the main processor, for instance by AHA, Intel and Microsoft. Yet, one lacks the understanding of the overhead of compression when offloading tasks. In particular, such offloading is most beneficial for overlap with other tasks, if the associated overhead on the main processor is negligible. This work evaluates the integration costs compared to a solely software-based solution considering multiple compression algorithms. Among others, High Energy Physics data are used as a prime example of big data sources. The results imply that on average the zlib implementation on the accelerator achieves a comparable compression ratio to zlib level 2 on a CPU, while having up to 17 times the throughput and utilizing over 80 % less CPU resources. These results suggest that, given the right orchestration of compression and data movement tasks, the overhead of offloading compression is limited but present. Considering that compression is only a single task of a larger data processing pipeline, this overhead cannot be neglected.
Validating Measures of Electrodermal Activity and Heart Rate Variability Derived From the Empatica E4 Utilized in Research Settings That Involve Interactive Dyadic States

Portable and wireless devices that collect physiological data are becoming more and more sought after in clinical and psychophysiological research as technology swiftly advances. These devices allow for data collection in interactive states, such as dyadic therapy, with reduced restraints compared to traditional laboratory devices. One such portable device is the Empatica E4 wristband (Empatica Srl, Milan, Italy) which allows quantifying cardiac interbeat intervals (IBIs), heart rate variability (HRV), and electro-dermal activity (EDA), as well as several other acceleration and temperature measures. In the current study, we aimed to assess IBI, HRV, and EDA measures, against the same data collected from the well-validated MindWare mobile impedance cardiograph device (MindWare Technology, Gahanna, OH, United States). We assessed the E4 strictly as a research instrument and not as a clinical tool. We were specifically interested in the wristbands’ performance during naturalistic interactive face-to-face conversations which inherently involve more hand movements. We collected data from 30 participants, nested in 15 dyads, which were connected to both devices simultaneously, during rest and during a social conversation. After preprocessing and analyses, we found that mean IBIs obtained by the E4 and the MindWare device, were highly similar during rest and during conversation. Medium to high correlations were found between the devices with respect to several HRV measures, with higher correlations during rest compared to conversation. The E4 failed to produce reliable EDA data. We conclude by discussing the strengths and limitations of the E4 during seated conversational states and suggest optimal ways to collect and analyze data with the E4.