Miracle-dz/UniFAR-FTUs huggingface dataset:
Simons Observatory: Constraining inflationary gravitational waves with multitracer 
B
-mode delensing

We introduce and validate a delensing framework for the Simons Observatory (SO), which will be used to improve constraints on inflationary gravitational waves (IGWs) by reducing the lensing noise in measurements of the $B$-modes in CMB polarization. SO will initially observe CMB by using three small aperture telescopes and one large-aperture telescope. While polarization maps from small-aperture telescopes will be used to constrain IGWs, the internal CMB lensing maps used to delens will be reconstructed from data from the large-aperture telescope. Since lensing maps obtained from the SO data will be noise-dominated on sub-degree scales, the SO lensing framework constructs a template for lensing-induced $B$-modes by combining internal CMB lensing maps with maps of the cosmic infrared background from Planck as well as galaxy density maps from the LSST survey. We construct a likelihood for constraining the tensor-to-scalar ratio $r$ that contains auto- and cross-spectra between observed $B$-modes and the lensing $B$-mode template. We test our delensing analysis pipeline on map-based simulations containing survey non-idealities, but that, for this initial exploration, does not include contamination from Galactic and extragalactic foregrounds. We find that the SO survey masking and inhomogeneous and atmospheric noise have very little impact on the delensing performance, and the $r$ constraint becomes $\sigma(r)\approx 0.0015$ which is close to that obtained from the idealized forecasts in the absence of the Galactic foreground and is nearly a factor of two tighter than without delensing. We also find that uncertainties in the external large-scale structure tracers used in our multi-tracer delensing pipeline lead to bias much smaller than the $1\,\sigma$ statistical uncertainties.
Gradient-Based Mixed Planning with Discrete and Continuous Actions

Dealing with planning problems with both discrete logical relations and continuous numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex quadratic constraints on numeric variables, which harms the performance when solving the problem. In this paper, we propose a novel algorithm framework to solve the numeric planning problems mixed with discrete and continuous actions based on gradient descent. We cast the numeric planning with discrete and continuous actions as an optimization problem by integrating a heuristic function based on discrete effects. Speciﬁcally, we propose a gradient-based framework to simultaneously optimize continuous parameters and actions of candidate plans. The framework is combined with a heuristic module to estimate the best plan candidate to transit initial state to the goal based on relaxation. We repeatedly update numeric parameters and compute candidate plan until it converges to a valid plan to the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efﬁcient, especially when solving non-convex planning problems.
Biparametric persistence for smooth filtrations

The goal of this note is to define biparametric persistence diagrams for smooth generic mappings h = (f,g) : M → V ∼ = R for smooth compact manifoldM. Existing approaches to multivariate persistence are mostly centered on the workaround of absence of reasonable algebraic theories for quiver representations for lattices of rank 2 or higher, or similar artificial obstacles. We approach the problem from the Whitney theory perspective, similar to how single parameter persistence can be viewed through the lens of Morse theory.
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters

Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant features are not readily apparent and accessible. To mine informative latent representations from multichannel recordings of ongoing EEG activity, we propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset, as well as on a new EEG dataset of unprecedented size (i.e., 761 subjects), where we identify consistent trends of music perception and related individual differences. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the respective specialisation of the temporal lobes regarding music perception proposed in the literature.
Kernel Minimum Divergence Portfolios

Portfolio optimization is a key challenge in finance with the aim of creating portfolios matching the investors' preference. The target distribution approach relying on the Kullback-Leibler or the $f$-divergence represents one of the most effective forms of achieving this goal. In this paper, we propose to use kernel and optimal transport (KOT) based divergences to tackle the task, which relax the assumptions and the optimization constraints of the previous approaches. In case of the kernel-based maximum mean discrepancy (MMD) we (i) prove the analytic computability of the underlying mean embedding for various target distribution-kernel pairs, (ii) show that such analytic knowledge can lead to faster convergence of MMD estimators, and (iii) extend the results to the unbounded exponential kernel with minimax lower bounds. Numerical experiments demonstrate the improved performance of our KOT estimators both on synthetic and real-world examples.
Balancing Value Underestimation and Overestimationwith Realistic Actor-Critic

Model-free deep reinforcement learning (RL) has been successfully applied to challenging continuous control domains. However, poor sample efﬁciency prevents these methods from being widely used in real-world domains. This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efﬁciency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. To learn such policies, we introduce uncertainty punished Q-learning, which uses uncertainty from the ensembling of multiple critics to build various conﬁdence-bounds of Q-function. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efﬁciency and 25% performance improvement on the most challenging Humanoid environment compared to SAC.
DPFM: Deep Partial Functional Maps

We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy manifold optimization in the spectral domain, given hand-crafted shape descriptors. In this paper, we propose the first learning method aimed directly at partial non-rigid shape correspondence. Our approach uses the functional map framework, can be trained in a supervised or unsupervised manner, and learns descriptors directly from the data, thus both improving robustness and accuracy in challenging cases. Furthermore, unlike existing techniques, our method is also applicable to partial-to-partial non-rigid matching, in which the common regions on both shapes are unknown a priori. We demonstrate that the resulting method is data-efficient, and achieves state-of-the-art results on several benchmark datasets. Our code and data can be found online: https://github.com/pvnieo/DPFM.
The Ionization and Destruction of Polycyclic Aromatic Hydrocarbons in Powerful Quasars

We reanalyze the mid-infrared (5–40 μm) Spitzer spectra of 86 low-redshift (z < 0.5) Palomar–Green quasars to investigate the nature of polycyclic aromatic hydrocarbon (PAH) emission and its utility as a star formation rate (SFR) indicator for the host galaxies of luminous active galactic nuclei (AGNs). We decompose the spectra with our recently developed template-fitting technique to measure PAH fluxes and upper limits, which we interpret using mock spectra that simulate the effects of AGN dilution. While luminous quasars can severely dilute and affect the detectability of emission lines, PAHs are intrinsically weak in some sources that are otherwise gas-rich and vigorously forming stars, conclusively demonstrating that powerful AGNs destroy PAH molecules. Comparing PAH-based SFRs with independent SFRs derived from the mid-infrared fine-structure neon lines and the total infrared luminosity reveals that PAHs can trace star formation activity in quasars with bolometric luminosities ≲1046 erg s−1, but increasingly underestimate the SFR for more powerful quasars, typically by ∼0.5 dex. Relative to star-forming galaxies and low-luminosity AGNs, quasars have a comparable PAH 11.3 μm/7.7 μm ratio but characteristically lower ratios of 6.2 μm/7.7 μm, 8.6 μm/7.7 μm, and 11.3 μm/17.0 μm. We suggest that these trends indicate that powerful AGNs preferentially destroy small grains and enhance the PAH ionization fraction.
Dark matter produced from neutrinos

In the presence of interactions between neutrinos and dark matter (DM), DM can potentially be produced via freeze-in from the neutrino sector. We investigate the implications of such a scenario for the evolution of both DM and neutrinos in the early Universe, and show that the future cosmic neutrino detection experiment PTOLEMY might be sensitive to neutrino signals that originate from DM annihilation in this model.
Randomized empirical processes by algebraic groups, and tests for weak null hypotheses

Randomization tests are based on a re-randomization of existing data to gain data-dependent critical values that lead to exact hypothesis tests under special circumstances. However, it is not always possible to re-randomize data in accordance to the physical randomization from which the data has been obained. As a consequence, most statistical tests cannot control the type I error probability. Still, similarly as the bootstrap, data re-randomization can be used to improve the type I error control. However, no general asymptotic theory under weak null hypotheses has been developed for such randomization tests yet. It is the aim of this paper to provide a conveniently applicable theory on the asymptotic validity of randomization tests with asymptotically normal test statistics. Similarly, confidence intervals will be developed. This will be achieved by creating a link between two well-established fields in mathematical statistics: empirical processes and inference based on randomization via algebraic groups. A broadly applicable conditional weak convergence theorem is developed for empirical processes that are based on randomized observations. Random elements of an algebraic group are applied to the data vectors from which the randomized version of a statistic is derived. Combining a variant of the functional delta-method with a suitable studentization of the statistic, asymptotically exact hypothesis tests is deduced, while the finite sample exactness property under group-invariant sub-hypotheses is preserved. The methodology is exemplified with: the Pearson correlation coefficient, a Mann-Whitney effect based on right-censored paired data, and a competing risks analysis. The practical usefulness of the approaches is assessed through simulation studies and an application to data from patients suffering from diabetic retinopathy.
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

User interest exploration is an important and challenging topic in recommender systems, which alleviates the closed-loop effects between recommendation models and user-item interactions.Contextual bandit (CB) algorithms strive to make a good trade-off between exploration and exploitation so that users' potential interests have chances to expose. However, classical CB algorithms can only be applied to a small, sampled item set (usually hundreds), which forces the typical applications in recommender systems limited to candidate post-ranking, homepage top item ranking, ad creative selection, or online model selection (A/B test). In this paper, we introduce two simple but effective hierarchical CB algorithms to make a classical CB model (such as LinUCB and Thompson Sampling) capable to explore users' interest in the entire item space without limiting to a small item set. We first construct a hierarchy item tree via a bottom-up clustering algorithm to organize items in a coarse-to-fine manner. Then we propose ahierarchical CB (HCB) algorithm to explore users' interest on the hierarchy tree. HCB takes the exploration problem as a series of decision-making processes, where the goal is to find a path from the root to a leaf node, and the feedback will be back-propagated to all the nodes in the path. We further propose aprogressive hierarchical CB (pHCB) algorithm, which progressively extends visible nodes which reach a confidence level for exploration, to avoid misleading actions on upper-level nodes in the sequential decision-making process. Extensive experiments on two public recommendation datasets demonstrate the effectiveness and flexibility of our methods.
Massive higher-spin multiplets and asymptotic freedom in quantum gravity

We consider massive higher-spin multiplets coupled to quantum gravity and compute their contributions to the gravitational beta functions at one loop. Such theories, if quantized with the Feynman prescription, would contain ghosts. Instead, those degrees of freedom are turned into purely virtual particles by means of the fakeon prescription and the resulting theories are both renormalizable and unitary. We extract the necessary number of fields to obtain asymptotic freedom by solving the relevant renormalization-group equations in the ultraviolet limit. We show that in the case of irreducible rank-$s$ bosons with minimal kinetic term only a certain number of fields with $0\leq s \leq 3$ is compatible with asymptotic freedom. No solution involving fermionic rank-$s$ fields are found. We also consider the cases of rank-$1$ and symmetric rank-$2$ tensor fields with nonminimal kinetic terms and repeat the analysis, finding that the allowed number of fields is enlarged. Possible implications for metric affine theories of gravity are pointed out.
CycleFlow: Purify Information Factors by Cycle Loss

SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, however, is that if the IB channels are not well designed, the resultant factors cannot be well disentangled. In this study, we propose a CycleFlow model that combines random factor substitution and cycle loss to solve this problem. Experiments on voice conversion tasks demonstrate that this simple technique can effectively reduce mutual information among individual factors, and produce clearly better conversion than the IB-based SpeechFlow. CycleFlow can also be used as a powerful tool for speech editing. We demonstrate this usage by an emotion perception experiment.
Continuous Control with Action Quantization from Demonstrations

In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations. This discretization returns a set of plausible actions (in light of the demonstrations) for each input state, thus capturing the priors of the demonstrator and their multimodal behavior. By discretizing the action space, any discrete action deep RL technique can be readily applied to the continuous control problem. Experiments show that the proposed approach outperforms state-of-the-art methods such as SAC in the RL setup, and GAIL in the Imitation Learning setup. We provide a website with interactive videos: https://google-research.github. io/aquadem/ and make the code available: https://github.com/google-research/google-research/tree/master/aquadem.
Private measurement of nonlinear correlations between data hosted across multiple parties

We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provide utility guarantees of our private estimator. Ours is the first such private estimator of nonlinear correlations, to the best of our knowledge within a multi-party setup. The important measure of nonlinear correlation we consider is distance correlation. This work has direct applications to private feature screening, private independence testing, private k-sample tests, private multi-party causal inference and private data synthesis in addition to exploratory data analysis. Code access: A link to publicly access the code is provided in the supplementary file.
Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail, unlike any virus before it. On the one hand, this will help biologists, policymakers, and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task, given the size of the data. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence—the relatively short region which codes for the spike protein(s). In this paper, we propose a robust feature-vector representation of biological sequences that, when combined with the appropriate feature selection method, allows different downstream clustering approaches to perform well on a variety of different measures. We use such proposed approach with an array of clustering techniques to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at a very high rate throughout the world. We use a k-mers based approach first to generate a fixed-length feature vector representation of the spike sequences. We then show that we can efficiently and effectively cluster the spike sequences based on the different variants with the appropriate feature selection. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that, with our feature selection methods, we can achieve higher F1 scores for the clusters and also better clustering quality metrics compared to baselines.
On random walks and switched random walks on homogeneous spaces

We prove new mixing rate estimates for the random walks on homogeneous spaces determined by a probability distribution on a finite group $G$ . We introduce the switched random walk determined by a finite set of probability distributions on $G$ , prove that its long-term behaviour is determined by the Fourier joint spectral radius of the distributions, and give Hermitian sum-of-squares algorithms for the effective estimation of this quantity.
The Impossibility Region for Detecting Sparse Mixtures using the Higher Criticism

Consider a multiple hypothesis testing setting involving rare/weak effects: relatively few tests, out of possibly many, deviate from their null hypothesis behavior. Summarizing the significance of each test by a P-value, we construct a global test against the null using the Higher Criticism (HC) statistics of these P-values. We calibrate the rare/weak model using parameters controlling the asymptotic distribution of non-null P-values near zero. We derive a region in the parameter space where the HC test is asymptotically powerless. Our derivation involves very different tools than previously used to show the powerlessness of HC, relying on properties of the empirical processes underlying HC. In particular, our result applies to situations where HC is not asymptotically optimal, or when the asymptotically detectable region of the parameter space is unknown.
Diffuse goitre enlargement after immunotherapy for non‐small cell lung cancer

It is well known that a phenomenon called hyperprogressive disease (HPD) often occurs during immunotherapy with immune checkpoint inhibitors. In the present case, we experienced a case of HPD in a potential metastatic thyroid tumour during immunotherapy. HPD can be life‐threatening depending on where it appears, so clinicians need to be careful.
Voice Analysis of Cancer Experiences Among Patients With Breast Cancer: VOICE-BC

Patient experience literature in early-stage breast cancer (eBC) is limited. This study used a mixed-methods approach to examine patient conversations from public online forums to identify and evaluate eBC-related themes. Among 60,000 eBC-related posts published September 2014–2019, text from a random subset of 15,000 posts was extracted and grouped into linguistically similar, mutually exclusive clusters using an advanced natural language processing (NLP) algorithm. Clusters were characterized using four quantitative metrics: betweenness centrality (linguistic similarity to other areas of the cluster network), sentiment (general attitude toward a topic), recency (average date of posts), and volume (total number of posts). This analysis represented 3906 unique users (67% and 33% obtained from cancer–specific and general health/nonhealth forums, respectively). Of the 27 clusters identified, most important were “discussing recurrence & progression,” “understanding diagnosis & prognosis,” and “understanding cancer, biomarkers, and treatments. ” Several major themes related to recurrence risk, diagnosis, monitoring, and treatment were identified. Additional emphasis on communicating the disease recurrence risk and shared decision-making could strengthen patient-clinician partnerships.