Miracle-dz/UniFAR-FTUs huggingface dataset:
Compartmentalizing the cuprate strange metal

High Field Magnet Laboratory (HFML-EMFL) and Institute for Molecules and Materials, Radboud University, Toernooiveld 7, 6525 ED Nijmegen, Netherlands H. H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom LNCMI-EMFL, CNRS UPR3228, Univ. Grenoble Alpes, Univ. Toulouse, INSA-T, Grenoble and Toulouse, France Van der Waals-Zeeman Institute, University of Amsterdam, Postbus 94485, 1090 GL Amsterdam, Netherlands Institute for Solid State Physics, University of Tokyo, Kashiwa-no-ha, Kashiwa, Japan Toyota Technological Institute, Nagoya 468-8511, Japan Department of Physics, University of Cambridge, Madingley Road, Cambridge, CB3 0HE, United Kingdom
Jointly Modeling Rating Responses and Times with Fuzzy Numbers: An Application to Psychometric Data

In several research areas, ratings data and response times have been successfully used to unfold the stagewise process through which human raters provide their responses to questionnaires and social surveys. A limitation of the standard approach to analyze this type of data is that it requires the use of independent statistical models. Although this provides an effective way to simplify the data analysis, it could potentially involve difficulties with regard to statistical inference and interpretation. In this sense, a joint analysis could be more effective. In this research article, we describe a way to jointly analyze ratings and response times by means of fuzzy numbers. A probabilistic tree model framework has been adopted to fuzzify ratings data and four-parameters triangular fuzzy numbers have been used in order to integrate crisp responses and times. Finally, a real case study on psychometric data is discussed in order to illustrate the proposed methodology. Overall, we provide initial findings to the problem of using fuzzy numbers as abstract models for representing ratings data with additional information (i.e., response times). The results indicate that using fuzzy numbers leads to theoretically sound and more parsimonious data analysis methods, which limit some statistical issues that may occur with standard data analysis procedures.
A cocyclic construction of $S^1$-equivariant homology and application to string topology

Given a space with a circle action, we study certain cocyclic chain complexes and prove a theorem relating cyclic homology to $S^1$-equivariant homology, in the spirit of celebrated work of Jones. As an application, we describe a chain level refinement of the gravity algebra structure on the (negative) $S^1$-equivariant homology of the free loop space of a closed oriented smooth manifold, based on work of Irie on chain level string topology and work of Ward on an $S^1$-equivariant version of operadic Deligne's conjecture.
Constraining axion-like particles with HAWC observations of TeV blazars

Axion-like particles (ALPs) are a broad class of pseudo-scalar bosons that generically arise from broken symmetries in extensions of the standard model. In many scenarios, ALPs can mix with photons in regions with high magnetic fields. Photons from distant sources can mix with ALPs, which then travel unattenuated through the Universe, before they mix back to photons in the Milky Way galactic magnetic field. Thus, photons can traverse regions where their signals would normally be blocked or attenuated. In this paper, we study TeV γ-ray observations from distant blazars, utilizing the significant γ-ray attenuation expected from such signals to look for excess photon fluxes that may be due to ALP-photon mixing. We find no such excesses among a stacked population of seven blazars and constrain the ALP-photon coupling constant to fall below ∼4.5×10-11 GeV-1 for ALP masses below 300 neV. These results are competitive with, or better than, leading terrestrial and astrophysical constraints in this mass range.
Modular probabilistic models via algebraic effects

Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both simulation and inference. We also demonstrate how simulation and inference can be expressed naturally as composable program transformations using algebraic effect handlers.
Image Steganography based on Style Transfer

Image steganography is the art and science of using images as cover for covert communications. With the development of neural networks, traditional image steganography is more likely to be detected by deep learning-based steganalysis. To improve upon this, we propose image steganography network based on style transfer, and the embedding of secret messages can be disguised as image stylization. We embed secret information while transforming the content image style. In latent space, the secret information is integrated into the latent representation of the cover image to generate the stego images, which are indistinguishable from normal stylized images. It is an end-to-end unsupervised model without pre-training. Extensive experiments on the benchmark dataset demonstrate the reliability, quality and security of stego images generated by our steganographic network.
Cooperative Trajectory Planning in Uncertain Environments With Monte Carlo Tree Search and Risk Metrics

Automated vehicles require the ability to cooper-ate with humans for smooth integration into today's traffic. While the concept of cooperation is well known, developing a robust and efficient cooperative trajectory planning method is still a challenge. One aspect of this challenge is the uncertainty surrounding the state of the environment due to limited sensor accuracy. This uncertainty can be represented by a Partially Observable Markov Decision Process. Our work addresses this problem by extending an existing cooperative trajectory planning approach based on Monte Carlo Tree Search for continuous action spaces. It does so by explicitly modeling uncertainties in the form of a root belief state, from which start states for trees are sampled. After the trees have been constructed with Monte Carlo Tree Search, their results are aggregated into return distributions using kernel regression. We apply two risk metrics for the final selection, namely a Lower Confidence Bound and a Conditional Value at Risk. It can be demonstrated that the integration of risk metrics in the final selection policy consistently outperforms a baseline in uncertain environments, generating considerably safer trajectories.
Generating Stellar Obliquity in Systems with Broken Protoplanetary Disks

Recent advances in submillimeter observations of young circumstellar nebulae have opened an unprecedented window into the structure of protoplanetary disks that has revealed the surprising ubiquity of broken and misaligned disks. In this work, we demonstrate that such disks are capable of torquing the spin axis of their host star, representing a hitherto unexplored pathway by which stellar obliquities may be generated. The basis of this mechanism is a crossing of the stellar spin precession and inner disk regression frequencies, resulting in adiabatic excitation of the stellar obliquity. We derive analytical expressions for the characteristic frequencies of the inner disk and star as a function of the disk gap boundaries and place an approximate limit on the disk architectures for which frequency crossing and the resulting obliquity excitation are expected, thereby illustrating the efficacy of this model. Cumulatively, our results support the emerging consensus that significant spin–orbit misalignments are an expected outcome of planet formation.
Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It

the effects of and They there are patients for high-dimensional observational causal inference analysis, and randomized controlled trials cannot ethically be conducted. However, mechanistic knowledge is available, including how drugs are absorbed into the body, and the combination of this knowledge with the limited data could potentially sufﬁce – if we knew how to combine them. In this work, we present a framework for interpretable estimation of causal effects for critically ill patients under exactly these complex conditions: interactions between drugs and observations over time, patient data sets that are not large, and mechanistic knowledge that can substitute for lack of data. Our framework incorporates pharmacokinetics and pharmacodynamics with interpretable matching methods to adjust for confounders such as patients’ drug response, medical history, and demographic variables. We apply this framework to an extremely important problem affecting critically ill patients, namely the effect of seizures and other potentially harmful electrical events in the brain (called epileptiform activity – EA) on outcomes. EA is a key indicator of whether the patient will suffer long term severe neurological disability or death. Given the high-stakes involved, and the high noise in the data, interpretability is critical for troubleshooting such complex problems. Interpretablity of our matched groups allowed neurologists to perform chart review to verify the quality of our causal analysis. For instance, our work indicates that a patient who experiences a high level of seizure-like activity (75% high EA burden), and is untreated for a six-hour window, has, on average, a 16.7% increased chance of adverse outcomes such as severe brain damage, lifetime disability, or death. We were also able to show that patients with mild-but-long-lasting EA (average EA burden ≥ 50%) have their risk of an adverse outcome increased by 11.2%. This information is essential to any neurologist who treats critically-ill patients.
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation

Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision. Existing deep learning approaches for 6D pose estimation typically rely on the availability of 3D object models and 6D pose annotations. However, precise annotation of 6D poses in real data is intricate, time-consuming and not scalable, while synthetic data scales well but lacks realism. To avoid these problems, we present a weakly-supervised reconstruction-based pipeline, named NeRF-Pose, which needs only 2D bounding boxes and relative camera poses during training. Following the first-reconstruct-then-regress idea, we first reconstruct the objects from multiple views in the form of an implicit neural representation. Then, we train a pose regression network to predict pixel-wise 2D-3D correspondences between images and the reconstructed model. A NeRF-enabled PnP+RANSAC algorithm is used to estimate stable and accurate pose from the predicted correspondences. Experiments on LineMod and LineMod-Occlusion show that the proposed method has state-of-the-art accuracy in comparison to the best 6D pose estimation methods in spite of being trained only with weak labels. We extend the Homebrewed DB dataset with real training images to support the weakly supervised task and achieve compelling results. The extended dataset and code will be released soon.
Reproducible Subjective Evaluation

Human perceptual studies are the gold standard for the evaluation of many research tasks in machine learning, linguistics, and psychology. However, these studies require significant time and cost to perform. As a result, many researchers use objective measures that can correlate poorly with human evaluation. When subjective evaluations are performed, they are often not reported with sufficient detail to ensure reproducibility. We propose Reproducible Subjective Evaluation (ReSEval), an open-source framework for quickly deploying crowdsourced subjective evaluations directly from Python. ReSEval lets researchers launch A/B, ABX, Mean Opinion Score (MOS) and MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) tests on audio, image, text, or video data from a command-line interface or using one line of Python, making it as easy to run as objective evaluation. With ReSEval, researchers can reproduce each other's subjective evaluations by sharing a configuration file and the audio, image, text, or video files.
Rényi Fair Information Bottleneck for Image Classification

We develop a novel method for ensuring fairness in machine learning which we term as the Rényi Fair Information Bottleneck (RFIB). We consider two different fairness constraints - demographic parity and equalized odds - for learning fair representations and derive a loss function via a variational approach that uses Renyi's divergence with its tunable parameter $\alpha$ and that takes into account the triple constraints of utility, fairness, and compactness of representation. We then evaluate the performance of our method for image classification using the EyePACS medical imaging dataset, showing it outperforms competing state of the art techniques with performance measured using a variety of compound utility/fairness metrics, including accuracy gap and Rawls' minimal accuracy.
ASET: Ad-hoc Structured Exploration of Text Collections [Extended Abstract]

In this paper, we propose a new system called ASET that allows users to perform structured explorations of text collections in an ad-hoc manner. The main idea of ASET is to use a new two-phase approach that first extracts a superset of information nuggets from the texts using existing extractors such as named entity recognizers and then matches the extractions to a structured table definition as requested by the user based on embeddings. In our evaluation, we show that ASET is thus able to extract structured data from real-world text collections in high quality without the need to design extraction pipelines upfront.
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding

With substantial recent developments in aviation technologies, Unmanned Aerial Vehicles (UAVs) are becoming increasingly integrated in commercial and military operations internationally. Research into the applications of aircraft data is essential in improving safety, reducing operational costs, and developing the next frontier of aerial technology. Having an outlier detection system that can accurately identify anomalous behaviour in aircraft is crucial for these reasons. This paper proposes a system incorporating a Long Short-Term Memory (LSTM) Deep Learning Autoencoder based method with a novel dynamic thresholding algorithm and weighted loss function for anomaly detection of a UAV dataset, in order to contribute to the ongoing efforts that leverage innovations in machine learning and data analysis within the aviation industry. The dynamic thresholding and weighted loss functions showed promising improvements to the standard static thresholding method, both in accuracy-related performance metrics and in speed of true fault detection.
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube

Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content should receive an additional level of scrutiny to ensure children's safety. While research on detecting offensive or inappropriate content for kids is gaining momentum, little or no current work exists that investigates to what extent AI applications can (accidentally) introduce content that is inappropriate for kids. In this paper, we present a novel (and troubling) finding that well-known automatic speech recognition (ASR) systems may produce text content highly inappropriate for kids while transcribing YouTube Kids' videos. We dub this phenomenon as inappropriate content hallucination. Our analyses suggest that such hallucinations are far from occasional, and the ASR systems often produce them with high confidence. We release a first-of-its-kind data set of audios for which the existing state-of-the-art ASR systems hallucinate inappropriate content for kids. In addition, we demonstrate that some of these errors can be fixed using language models.
High sensitivity air-coupled MHz frequency ultrasound detection using an on-chip microtoroid cavity

Hao Yang, 2 Zhi-Gang Hu, 2 Yuechen Lei, 2 Xuening Cao, 2 Min Wang, 2 Jialve Sun, Changhui Li, Zhanchun Zuo, 4 Xiulai Xu, and Bei-Bei Li 4, ∗ Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100094, P. R. China University of Chinese Academy of Sciences, Beijing 100049, P. R. China Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China Songshan Lake Materials Laboratory, Dongguan 523808, Guangdong, P. R. China (Dated: March 10, 2022)
UENAS: A Unified Evolution-based NAS Framework

Neural architecture search (NAS) has gained signiﬁcant attention for automatic network design in recent years. Previous NAS methods suffer from limited search spaces, which may lead to sub-optimal results. In this paper, we propose UENAS, an evolution-based NAS framework with a broader search space that supports optimizing network architectures, pruning strategies, and hyperparameters simultaneously. To alleviate the huge search cost caused by the expanded search space, three strategies are adopted: First, an adaptive pruning strategy that iteratively trims the average model size in the population without compromising performance. Second, child networks share weights of overlapping layers with pre-trained parent networks to reduce the training epochs. Third, an online predictor scores the joint representations of architecture, pruning strategy, and hyperparameters to ﬁlter out inferior combos. By the proposed three strategies, the search efﬁciency is signiﬁcantly improved and more well-performed compact networks with tailored hyper-parameters are derived. In experiments, UENAS achieves error rates of 2 . 81% on CIFAR-10, 20 . 24% on CIFAR-100, and 33% on Tiny-ImageNet, which shows the effectiveness of our method.
Variational Inference with Locally Enhanced Bounds for Hierarchical Models

Hierarchical models represent a challenging setting for inference algorithms. MCMC methods struggle to scale to large models with many local variables and observations, and variational inference (VI) may fail to provide accurate approximations due to the use of simple variational families. Some variational methods (e.g. importance weighted VI) integrate Monte Carlo methods to give better accuracy, but these tend to be unsuitable for hierarchical models, as they do not allow for subsampling and their performance tends to degrade for high dimensional models. We propose a new family of variational bounds for hierarchical models, based on the application of tightening methods (e.g. importance weighting) separately for each group of local random variables. We show that our approach naturally allows the use of subsampling to get unbiased gradients, and that it fully leverages the power of methods that build tighter lower bounds by applying them independently in lower dimensional spaces, leading to better results and more accurate posterior approximations than relevant baselines.
A Giant Shell of Ionized Gas Discovered near M82 with the Dragonfly Spectral Line Mapper Pathfinder

We present the discovery of a giant cloud of ionized gas in the field of the starbursting galaxy M82. Emission from the cloud is seen in Hα and [N ii] λ6583 in data obtained though a small pathfinder instrument used to test the key ideas that will be implemented in the Dragonfly Spectral Line Mapper, an upcoming ultranarrow-bandpass imaging version of the Dragonfly Telephoto Array. The discovered cloud has a shell-like morphology with a linear extent of 0. °8 and is positioned 0. °6 northwest of M82. At the heliocentric distance of the M81 group, the cloud’s longest angular extent corresponds to 55 kpc and its projected distance from the nucleus of M82 is 40 kpc. The cloud has an average Hα surface brightness of 2 × 10−18 ergcm−2s−1arcsec−2 . The [N ii] λ6583/Hα line ratio varies from [N ii]/Hα ∼ 0.2 to [N ii]/Hα ∼ 1.0 across the cloud, with higher values found in its eastern end. Follow-up spectra obtained with Keck LRIS confirm the existence of the cloud and yield line ratios of [N ii] λ6583/Hα = 0.340 ± 0.003 and [S ii] λλ6716, 6731/Hα = 0.64 ± 0.03 in the cloud. This giant cloud of material could be lifted from M82 by tidal interactions or by its powerful starburst. Alternatively, it may be gas infalling from the cosmic web, potentially precipitated by the superwinds of M82. Deeper data are needed to test these ideas further. The upcoming Dragonfly Spectral Line Mapper will have 120 lenses, 40× more than in the pathfinder instrument used to obtain the data presented here.
Quantum advantage for multi-option portfolio pricing and valuation adjustments

A critical problem in the financial world deals with the management of risk, from regulatory risk to portfolio risk. Many such problems involve the analysis of securities modelled by complex dynamics that cannot be captured analytically, and hence rely on numerical techniques that simulate the stochastic nature of the underlying variables. These techniques may be computationally difficult or demanding. Hence, improving these methods offers a variety of opportunities for quantum algorithms. In this work, we study the problem of Credit Valuation Adjustments (CVAs) which have significant importance in the valuation of derivative portfolios. We propose quantum algorithms that accelerate statistical sampling processes to approximate the CVA under different measures of dispersion, using known techniques in Quantum Monte Carlo (QMC) and analyse the conditions under which we may employ these techniques.
Autonomous soft hand grasping - Literature review

Autonomous grasping remains challenging as unlike humans, robots do not possess a sophisticated sensing nor delicate interaction capability with the real environment. Among other efforts that tried to close the gap between them, anthropomorphic robotic hands is the most prominent direction. However, exactly following human hand design might be unnecessary as it will significantly increase the mechanical complexity and hence make it less economically feasible. Recently, soft robotic hands, a new trend has emerged, aiming to make the design adequately complex and affordable while requiring much less effort to control. In the first part of this article, we will lay out several prominent designs in this direction and their applications in real world scenarios. Having a suitable hardware simplified the complexity of software designing. However, manually controlling the hand for one task requires a significantly large amount of time and effort and doing it repeatedly is unsurprisingly tedious. Therefore, in the second part, we will show some recent techniques for soft hand autonomous control. We start by briefly discussing the analytic methods that mainly exploit the hand dynamic information. Then, data-driven approaches will be our main focus. It is the trending research topic for soft hand grasping in recent years as it has shown a high performance when dealing with a large number of various objects.