Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation and visualization of treatment effects for multiple outcomes

We consider a randomized controlled trial between two groups. The objective is to identify a population with characteristics such that the test therapy is more effective than the control therapy. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying interactions between treatments and covariates. To date, many methods have been proposed to identify subgroups for a single outcome. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we propose a multivariate regression method that introduces latent variables to estimate the treatment effect on multiple outcomes simultaneously. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The framework of the generalized linear model makes it applicable to various types of outcomes. Interpretation of subgroups is made by visualizing treatment effects and latent variables. This allows us to identify subgroups with characteristics that make the test therapy more effective for each multiple outcomes. Simulation and real data examples demonstrate the effectiveness of the proposed method.
Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions

4D reconstruction of human-object interaction is critical for immersive VR/AR experience and human activity understanding. Recent advances still fail to recover fine geometry and texture results from sparse RGB inputs, especially under challenging human-object interactions scenarios. In this paper, we propose a neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of both human and objects under challenging interaction scenarios in arbitrary novel views, from only sparse RGB streams. To deal with complex occlusions raised by human-object interactions, we adopt a layer-wise scene decoupling strategy and perform volumetric reconstruction and neural rendering of the human and object. Specifically, for geometry reconstruction, we propose an interaction-aware human-object capture scheme that jointly considers the human reconstruction and object reconstruction with their correlations. Occlusion-aware human reconstruction and robust human-aware object tracking are proposed for consistent 4D human-object dynamic reconstruction. For neural texture rendering, we propose a layer-wise human-object rendering scheme, which combines direction-aware neural blending weight learning and spatial-temporal texture completion to provide high-resolution and photo-realistic texture results in the occluded scenarios. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality geometry and texture reconstruction in free viewpoints for challenging human-object interactions.
Enhanced Regular Corecursion for Data Streams

We propose a simple calculus for processing data streams (infinite flows of data series), represented by finite sets of equations built on stream operators. Furthermore, functions defining streams are regularly corecursive, that is, cyclic calls are detected, avoiding non-termination as happens with ordinary recursion in the call-by-value evaluation strategy. As we illustrate by several examples, the combination of such two mechanisms provides a good compromise between expressive power and decidability. Notably, we provide an algorithm to check that the stream returned by a function call is represented by a well-formed set of equations which actually admits a unique solution, hence access to an arbitrary element of the returned stream will never diverge.
Formation of cell assemblies with iterative winners-take-all computation and excitation-inhibition balance

This paper targets the problem of encoding information into binary cell assemblies. Spiking neural networks and k-winners-take-all models are two common approaches, but the first is hard to use for information processing and the second is too simple and lacks important features of the first. We present an intermediate model that shares the computational ease of kWTA and has more flexible and richer dynamics. It uses explicit inhibitory neurons to balance and shape excitation through an iterative procedure. This leads to a recurrent interaction between inhibitory and excitatory neurons that better adapts to the input distribution and performs such computations as habituation, decorrelation, and clustering. To show these, we investigate Hebbian-like learning rules and propose a new learning rule for binary weights with multiple stabilization mechanisms. Our source code is publicly available.
Musical Speech: A Transformer-based Composition Tool

In this paper, we propose a new compositional tool that will generate a musical outline of speech recorded/provided by the user for use as a musical building block in their compositions. The tool allows any user to use their own speech to generate musical material, while still being able to hear the direct connection between their recorded speech and the resulting music. The tool is built on our proposed pipeline. This pipeline begins with speech-based signal processing, after which some simple musical heuristics are applied, and finally these pre-processed signals are passed through Transformer models trained on new musical tasks. We illustrate the effectiveness of our pipeline -- which does not require a paired dataset for training -- through examples of music created by musicians making use of our tool.
Isotropic random spin weighted functions on $S^2$ vs isotropic random fields on $S^3$

We show that an isotropic random field on $SU(2)$ is not necessarily isotropic as a random field on $S^3$, although the two spaces can be identified. The ambiguity is due to the fact that the notion of isotropy on a group and on a sphere are different, the latter being much stronger. We show that any isotropic random field on $S^3$ is necessarily a superposition of uncorrelated random harmonic homogeneous polynomials, such that the one of degree $d$ is necessarily a superposition of uncorrelated random spin weighted functions of every possible spin weight in the range $\{-\frac{d}{2},\dots,\frac{d}{2}\}$, each of which is isotropic in the sense of $SU(2)$. Moreover, for a random field of fixed degree, each spin weight appears with the same magnitude, in a sense to be specified. In addition we will give an overview of the theory of spin weighted functions and Wigner $D$-matrices, with the purpose of gathering together many different points of view and adding ours. As a byproduct of this survey we will prove some new properties of the Wigner matrices and a formula relating the operators $\eth\overline{\eth}$ and the horizontal Laplacian of the Hopf fibration $S^3\to S^2$.
Gate-controlled quantum dots in monolayer WSe2

Quantum confinenement and manipulation of charge carriers are critical for achieving devices practical for quantum technologies. The interplay between electron spin and valley, as well as the possibility to address their quantum states electrically and optically, make two-dimensional (2D) transition metal dichalcogenides an emerging platform for the development of quantum devices. In this work, we fabricate devices based on heterostructures of layered 2D materials, in which we realize gate-controlled tungsten diselenide (WSe2) hole quantum dots. We discuss the observed mesoscopic transport features related to the emergence of quantum dots in the WSe2 device channel, and we compare them to a theoretical model.
The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions

The Doubly Robust (DR) estimation of ATE can be carried out in 2 steps, where in the first step, the treatment and outcome are modeled, and in the second step the predictions are inserted into the DR estimator. The model misspecification in the first step has led researchers to utilize Machine Learning algorithms instead of parametric algorithms. However, existence of strong confounders and/or Instrumental Variables (IVs) can lead the complex ML algorithms to provide perfect predictions for the treatment model which can violate the positivity assumption and elevate the variance of DR estimators. Thus the ML algorithms must be controlled to avoid perfect predictions for the treatment model while still learn the relationship between the confounders and the treatment and outcome. We use two Neural network architectures and investigate how their hyperparameters should be tuned in the presence of confounders and IVs to achieve a low bias-variance tradeoff for ATE estimators such as DR estimator. Through simulation results, we will provide recommendations as to how NNs can be employed for ATE estimation.
Scaling law and universal drop size distribution of coarsening in conversion-limited phase separation

Phase separation is not only ubiquitous in diverse physical systems, but also plays an important organizational role inside biological cells. However, experimental studies of intracellular condensates (drops with condensed concentrations of specific collections of proteins and nucleic acids) have challenged the standard coarsening theories of phase separation. Specifically, the coarsening rates observed are unexpectedly slow for many intracellular condensates. Recently, Folkmann, et al. [Science 373, 1218 (2021)] argued that the slow coarsening rate can be caused by the slow conversion of a condensate constituent between the state in the dilute phase and the condensate state. A consequence of this conversion-limited picture is that standard theories of coarsening in phase separation (Lifshitz-Slyozov–Wagner Ostwald ripening and drop coalescence schemes) no longer apply. Surprisingly, I show here that the model equations of conversion-limited phase separation can instead be mapped onto a grain growth model in a single-phase material in three dimensions. I further elucidate the universal coarsening behavior in the late stage using analytical and numerical methods.
Subgraph-Aware Few-Shot Inductive Link Prediction Via Meta-Learning

Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entities. The recently proposed subgraph-based models provide alternatives to predict links from the subgraph structure surrounding a candidate triplet. However, these methods require abundant known facts of training triplets and perform poorly on relationships that only have a few triplets. In this paper, we propose Meta-iKG, a novel subgraph-based meta-learner for few-shot inductive relation reasoning. Meta-iKG utilizes local subgraphs to transfer subgraph-specific information and to rapidly learn transferable patterns via meta-gradients. In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings. Moreover, we introduce a large-shot relation updating procedure to ensure that our model can generalize well to both few-shot and large-shot relations. We evaluate Meta-iKG on inductive benchmarks sampled from the NELL and Freebase, and the results show that Meta-iKG outperforms the currently state-of-the-art methods in both few-shot scenarios and standard inductive settings.
Sequoia: A Software Framework to Unify Continual Learning Research

The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting www.github. com/lebrice/Sequoia.
GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run

The second Gravitational-Wave Transient Catalog reported on 39 compact binary coalescences observed by the Advanced LIGO and Advanced Virgo detectors between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC. We present GWTC-2.1, which reports on a deeper list of candidate events observed over the same period. We analyze the final version of the strain data over this period with improved calibration and better subtraction of excess noise, which has been publicly released. We employ three matched-filter search pipelines for candidate identification, and estimate the astrophysical probability for each candidate event. While GWTC-2 used a false alarm rate threshold of 2 per year, we include in GWTC-2.1, 1201 candidates that pass a false alarm rate threshold of 2 per day. We calculate the source properties of a subset of 44 high-significance candidates that have an astrophysical probability greater than 0.5. Of these candidates, 36 have been reported in GWTC-2. If the 8 additional high-significance candidates presented here are astrophysical, the mass range of events that are unambiguously identified as binary black holes (both objects $\geq 3M_\odot$) is increased compared to GWTC-2, with total masses from $\sim 14 M_\odot$ for GW190924_021846 to $\sim 182 M_\odot$ for GW190426_190642. The primary components of two new candidate events (GW190403_051519 and GW190426_190642) fall in the mass gap predicted by pair instability supernova theory. We also expand the population of binaries with significantly asymmetric mass ratios reported in GWTC-2 by an additional two events (the mass ratio is less than $0.65$ and $0.44$ at $90\%$ probability for GW190403_051519 and GW190917_114630 respectively), and find that 2 of the 8 new events have effective inspiral spins $\chi_\mathrm{eff}>0$ (at $90\%$ credibility), while no binary is consistent with $\chi_\mathrm{eff}<0$ at the same significance.
Varieties of chord diagrams, braid group cohomology and degeneration of equality conditions

For any finite-dimensional vector space ${\mathcal F}$ of continuous functions $f:{\mathbb R}^1 \to {\mathbb R}^1$ consider subspaces in ${\mathcal F}$ defined by systems of {\em equality conditions} $f(a_i) = f(b_i)$, where $(a_i, b_i)$, $i=1, \dots, n$, are some pairs of points in ${\mathbb R}^1$. It is proved that if $\dim {\mathcal F}<2n-I(n)$, where $I(n)$ is the number of ones in the binary notation of $n$, then there necessarily are independent systems of $n$ equality conditions defining the subspaces of codimension greater than $n$ in ${\mathcal F}$. We also prove lower estimates of the sizes of the inevitable drops of the codimensions of these subspaces. These estimates are then applied to knot theory (in which systems of equality conditions are known as {\em chord diagrams}). The inevitable presence of complicated non-stable terms in sequences of spectral sequences calculating the cohomology groups of spaces of knots is proved. Keywords: Chord diagram, configuration space, characteristic class
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data

Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for machine learning models. They cause significant gaps between model performance in the lab and in the real world. Our work is a solution to prevalence bias. Prevalence bias is the discrepancy between the prevalence of a pathology and its sampling rate in the training dataset, introduced upon collecting data or due to the practioner rebalancing the training batches. This paper lays the theoretical and computational framework for training models, and for prediction, in the presence of prevalence bias. Concretely a bias-corrected loss function, as well as bias-corrected predictive rules, are derived under the principles of Bayesian risk minimization. The loss exhibits a direct connection to the information gain. It offers a principled alternative to heuristic training losses and complements test-time procedures based on selecting an operating point from summary curves. It integrates seamlessly in the current paradigm of (deep) learning using stochastic backpropagation and naturally with Bayesian models.
Optimizing Cost per Click for Digital Advertising Campaigns

Cost per click is a common metric to judge digital advertising campaign performance. In this paper we discuss an approach that generates a feature targeting recommendation to optimise cost per click. We also discuss a technique to assign bid prices to features without compromising on the number of features recommended. Our approach utilises impression and click stream data sets corresponding to real time auctions that we have won. The data contains information about device type, website, RTB Exchange ID. We leverage data across all campaigns that we have access to while ensuring that recommendations are sensitive to both individual campaign level features and globally well performing features as well. We model Bid recommendation around the hypothesis that a click is a Bernoulli trial and click stream follows Binomial distribution which is then updated based on live performance ensuring week over week improvement. This approach has been live tested over 10 weeks across 5 campaigns. We see Cost per click gains of 16-60% and click through rate improvement of 42-137%. At the same time, the campaign delivery was competitive.
SPEAR : Semi-supervised Data Programming in Python

We present SPEAR, an open-source python library for data programming with semi supervision. The package implements several recent data programming approaches including facility to programmatically label and build training data. SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of noisy labels to the training dataset. These noisy labels are aggregated to assign labels to the unlabeled data for downstream tasks. We have implemented several label aggregation approaches that aggregate the noisy labels and then train using the noisily labeled set in a cascaded manner. Our implementation also includes other approaches that jointly aggregate and train the model for text classification tasks. Thus, in our python package, we integrate several cascade and joint data-programming approaches while also providing the facility of data programming by letting the user define labeling functions or rules. The code and tutorial notebooks are available at https://github.com/decile-team/spear. Further, extensive documentation can be found at https://spear-decile.readthedocs. io/. Video tutorials demonstrating the usage of our package are available here. We also present some real-world use cases of SPEAR.
Variance Estimates in Linnik’s Problem

We evaluate the variance of the number of lattice points in a small randomly rotated spherical ball on a surface of 3-dimensional sphere centered at the origin. Previously, Bourgain, Rudnick, and Sarnak showed conditionally on the Generalized Lindelöf Hypothesis that the variance is bounded from above by $\sigma (\Omega ){N_n}^{1+\varepsilon }$, where $\sigma (\Omega )$ is the area of the ball $\Omega $ on the unit sphere and $N_n$ is the total number of solutions of Diophantine equation $x^2 + y^2 + z^2 = n$. Assuming the Grand Riemann Hypothesis and using the moments method of Soundararajan and Harper, we establish the upper bound of the form $c\sigma (\Omega ) N_n$, where $c$ is an absolute constant. This bound is of the conjectured order of magnitude.
First-principles estimation of the superconducting transition temperature of a metallic hydrogen liquid

We present a first-principles implementation of the stochastic path-integral approach proposed by Liu et al. [1] for estimating the superconducting transition temperature (Tc) of a liquid. The implementation is based on the all-electron projector-augmented-wave (PAW) method. We generalize Liu et al.’s formalism to accommodate the pseudo-description of electron states in the PAW method. A formula for constructing the overlap operator of the PAW method is proposed to eliminate errors due to the incompleteness of a pseudo-basis set. We apply the implementation to estimate Tc’s of metallic hydrogen liquids. It confirms Liu et al.’s prediction that metallic hydrogen can form a superconducting liquid.
Coupled and uncoupled sign-changing spikes of singularly perturbed elliptic systems

We study the existence and asymptotic behavior of solutions having positive and sign-changing components to the singularly perturbed system of elliptic equations in a bounded domain Ω in R N , with N ≥ 4, ε > 0, µ i > 0, λ ij = λ ji < 0, α ij , β ij > 1, α ij α ij + β ij = p ∈ (2 , 2 ∗ ), and 2 ∗ := 2 NN − 2 . If Ω is the unit ball we obtain solutions with a prescribed combination of positive and nonradial sign-changing components exhibiting two diﬀerent types of asymptotic behavior as ε → 0: solutions whose limit proﬁle is a rescaling of a solution with positive and nonradial sign-changing components of the limit system and solutions whose limit proﬁle is a solution of the uncoupled system, i.e., after rescaling and translation, the limit proﬁle of the i -th component is a positive or a nonradial sign-changing solution to the equation MSC2020: 35J57 (35B06, 35B25, 35B40, 47J30).
SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours

The surprisingness of a song is an essential and seemingly subjective factor in determining whether the listener likes it. With the help of information theory, it can be described as the transition probability of a music sequence modeled as a Markov chain. In this study, we introduce the concept of deriving entropy variations over time, so that the surprise contour of each chord sequence can be extracted. Based on this, we propose a user-controllable framework that uses a conditional variational autoencoder (CVAE) to harmonize the melody based on the given chord surprise indication. Through explicit conditions, the model can randomly generate various and harmonic chord progressions for a melody, and the Spearman's correlation and p-value significance show that the resulting chord progressions match the given surprise contour quite well. The vanilla CVAE model was evaluated in a basic melody harmonization task (no surprise control) in terms of six objective metrics. The results of experiments on the Hooktheory Lead Sheet Dataset show that our model achieves performance comparable to the state-of-the-art melody harmonization model.