Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Conditional Average Treatment Effects with Missing Treatment Information

Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where unique challenges arise in the form of covariate shifts. We identify two covariate shifts in our setting: (i) a covariate shift between the treated and control population; and (ii) a covariate shift between the observed and missing treatment population. We first theoretically show the effect of these covariate shifts by deriving a generalization bound for estimating CATE in our setting with missing treatments. Then, motivated by our bound, we develop the missing treatment representation network (MTRNet), a novel CATE estimation algorithm that learns a balanced representation of covariates using domain adaptation. By using balanced representations, MTRNet provides more reliable CATE estimates in the covariate domains where the data are not fully observed. In various experiments with semi-synthetic and real-world data, we show that our algorithm improves over the state-of-the-art by a substantial margin.
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning

We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimization. We design MORBiT, a novel single-loop gradient descent-ascent bilevel optimization algorithm, to solve the generic problem and present a novel analysis showing that MORBiT converges to the first-order stationary point at a rate of $\widetilde{\mathcal{O}}(n^{1/2} K^{-2/5})$ for a class of weakly convex problems with $n$ objectives upon $K$ iterations of the algorithm. Our analysis utilizes novel results to handle the non-smooth min-max multi-objective setup and to obtain a sublinear dependence in the number of objectives $n$. Experimental results on robust representation learning and robust hyperparameter optimization showcase (i) the advantages of considering the min-max multi-objective setup, and (ii) convergence properties of the proposed MORBiT. Our code is at https://github.com/minimario/MORBiT.
Optomechanical Simulation of a Parametric Oscillator

We study an optomechanical device supporting at least three optical modes in the infrared telecommunication band and three mechanical vibration modes. We model the coherent driving of each optical mode, independently of each other, to obtain an effective Hamiltonian showing the different types of parametric processes allowed in the device. We propose a bichromatic driving scheme, in the lossy optical cavity regime, under a mean field approximation, that provides the quantum simulation of a parametric oscillator with optical control of its parameters.
Deep Learning-Based Joint Control of Acoustic Echo Cancellation, Beamforming and Postfiltering

We introduce a novel method for controlling the functionality of a hands-free speech communication device which comprises a model-based acoustic echo canceller (AEC), minimum variance distortionless response (MVDR) beamformer (BF) and spectral postfilter (PF). While the AEC removes the early echo component, the MVDR BF and PF suppress the residual echo and background noise. As key innovation, we suggest to use a single deep neural network (DNN) to jointly control the adaptation of the various algorithmic components. This allows for rapid convergence and high steady-state performance in the presence of high-level interfering double-talk. End-to-end training of the DNN using a time-domain speech extraction loss function avoids the design of individual control strategies.
Scalable SoftGroup for 3D Instance Segmentation on Point Clouds

This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping instance segmentation results. Unfortunately, errors stemming from hard decisions propagate into the grouping, resulting in poor overlap between predicted instances and ground truth and substantial false positives. To address the abovementioned problems, SoftGroup allows each point to be associated with multiple classes to mitigate the uncertainty stemming from semantic prediction. It also suppresses false positive instances by learning to categorize them as background. Regarding scalability, the existing fast methods require computational time on the order of tens of seconds on large-scale scenes, which is unsatisfactory and far from applicable for real-time. Our finding is that the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="vu-ieq1-3326189.gif"/></alternatives></inline-formula>-Nearest Neighbor (<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="vu-ieq2-3326189.gif"/></alternatives></inline-formula>-NN) module, which serves as the prerequisite of grouping, introduces a computational bottleneck. SoftGroup is extended to resolve this computational bottleneck, referred to as SoftGroup++. The proposed SoftGroup++ reduces time complexity with octree <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="vu-ieq3-3326189.gif"/></alternatives></inline-formula>-NN and reduces search space with class-aware pyramid scaling and late devoxelization. Experimental results on various indoor and outdoor datasets demonstrate the efficacy and generality of the proposed SoftGroup and SoftGroup++. Their performances surpass the best-performing baseline by a large margin (6% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="vu-ieq4-3326189.gif"/></alternatives></inline-formula> 16%) in terms of AP<inline-formula><tex-math notation="LaTeX">$_{50}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mn>50</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="vu-ieq5-3326189.gif"/></alternatives></inline-formula>. On datasets with large-scale scenes, SoftGroup++ achieves a 6× speed boost on average compared to SoftGroup. Furthermore, SoftGroup can be extended to perform object detection and panoptic segmentation with nontrivial improvements over existing methods.
Interpretation of the puzzling gamma-ray spectrum of the Geminga halo

The gamma-ray halo around Geminga is formed owing to the slow diffusion of the electrons released by the Geminga PWN. The latest HAWC and HESS observations exhibit complex features in the TeV gamma-ray spectrum of the Geminga halo. We first show that the new results cannot be interpreted by the commonly used simple model, where a single power-law injection spectrum and an energy index of $\delta=1/3$ for the diffusion coefficient are assumed. We then propose a two-population electron injection model based on the x-ray observations of the Geminga PWN, which consists of a population of freshly accelerated electrons escaping from the PWN through rapid outflows and a population trapped longer inside the PWN before escaping. The two-population model interprets the HAWC and HESS data well, and the goodness of fit improves significantly compared with the single power-law injection model. It also predicts a different energy dependency of the gamma-ray profile from the single power-law model, which could be tested by LHAASO in the coming future. We note that a $\delta$ slightly larger than 1 is needed to fit the HAWC and HESS data consistently. We also discuss the possible improvements by adopting the two-zone diffusion model.
The Kawahara Equation: Traveling Wave Solutions Joining Periodic Waves

The Kawahara equation is a weakly nonlinear long-wave model of dispersive waves that emerges when leading order dispersive effects are in balance with the next order correction. Traveling wave solutions of the Kawahara equation satisfy a fourth-order ordinary differential equation in which the traveling wave speed is a parameter. The fourth order equation has Hamiltonian structure and admits a two-parameter family of single-phase periodic solutions with varying speed and Hamiltonian. A set of jump conditions is derived for pairs of periodic solutions with equal speed and Hamiltonian. These are necessary conditions for the existence of traveling waves that asymptote to the periodic orbits at ±∞. Bifurcation theory and parameter continuation are used to construct multiple solution branches of the jump conditions. For each pair of compatible periodic solutions, the heteroclinic orbit representing the traveling wave is constructed from the intersection of stable and unstable manifolds of the periodic orbits. Each branch terminates at an equilibrium-to-periodic solution in which the equilibrium is the background for a solitary wave that connects to the associated periodic solution.
Stochastic Model Predictive Control using Initial State Optimization

We propose a stochastic MPC scheme using an optimization over the initial state for the predicted trajectory. Considering linear discrete-time systems under unbounded additive stochastic disturbances subject to chance constraints, we use constraint tightening based on probabilistic reachable sets to design the MPC. The scheme avoids the infeasibility issues arising from unbounded disturbances by including the initial state as a decision variable. We show that the stabilizing control scheme can guarantee constraint satisfaction in closed loop, assuming unimodal disturbances. In addition to illustrating these guarantees, the numerical example indicates further advantages of optimizing over the initial state for the transient behavior.
Context Enhanced Short Text Matching using Clickthrough Data

The short text matching task employs a model to determine whether two short texts have the same semantic meaning or intent. Existing short text matching models usually rely on the content of short texts which are lack information or missing some key clues. Therefore, the short texts need external knowledge to complete their semantic meaning. To address this issue, we propose a new short text matching framework for introducing external knowledge to enhance the short text contextual representation. In detail, we apply a self-attention mechanism to enrich short text representation with external contexts. Experiments on two Chinese datasets and one English dataset demonstrate that our framework outperforms the state-of-the-art short text matching models.
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance Fields

We present a method for handling view-dependent information in radiance fields to help with convergence and quality of 3D reconstruction. Radiance fields with view-dependence suffers from the so called shape-radiance ambiguity, which can lead to incorrect geometry given a high angular resolution of view-dependent colors. We propose the addition of a difference plane in front of each camera, with the purpose of separating view-dependent and Lambertian components during training. We also propose an additional step where we train, but do not store, a low-resolution view-dependent function that helps to isolate the surface if such a separation is proven difficult. These additions have a small impact on performance and memory usage but enables reconstruction of scenes with highly specular components without any other explicit handling of view-dependence such as Spherical Harmonics.
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining

Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets. By leveraging the natural suitability of E-commerce, where different modalities capture complementary semantic information, we contribute a large-scale multi-modal pretraining dataset M5Product. The dataset comprises 5 modalities (image, text, table, video, and audio), covers over 6,000 categories and 5,000 attributes, and is 500× larger than the largest publicly available dataset with a similar number of modalities. Furthermore, M5Product contains incomplete modality pairs and noise while also having a long-tailed distribution, resembling most real-world problems. We further propose Self-harmonized ContrAstive LEarning (SCALE), a novel pretraining framework that integrates the different modalities into a unified model through an adaptive feature fusion mechanism, where the importance of each modality is learned directly from the modality embeddings and impacts the inter-modality contrastive learning and masked tasks within a multi-modal transformer model. We evaluate the current multi-modal pre-training state-of-the-art approaches and benchmark their ability to learn from unlabeled data when faced with the large number of modalities in the M5Product dataset. We conduct extensive experiments on four downstream tasks and demonstrate the superiority of our SCALE model, providing insights into the importance of dataset scale and diversity. Dataset and codes are available at 11https://xiaodongsuper.github. io/M5Product_dataset/.
Protection of quantum evolutions under parity-time-symmetric non-Hermitian Hamiltonians by dynamical decoupling

Parity-time (PT) symmetric non-Hermitian Hamiltonians bring about many novel features and interesting applications such as quantum gates faster than those in Hermitian systems, and topological state transfer. The performance of evolutions under $\mathcal{PT}$-symmetric Hamiltonians is degraded by the inevitable noise and errors due to system-environment interaction and experimental imperfections. In contrast to Hermitian Hamiltonians, the fluctuations in dissipative beams that are utilized to generate non-Hermitian contributions in the PT-symmetric Hamiltonians cause additional errors. Here we achieve the protection of PT-symmetric Hamiltonians against noise acting along the qubit's quantization axis by combining quantum evolutions with dynamical decoupling sequences. We demonstrate the performance of our method by numerical simulations. Realistic noise sources and parameters are chosen including: constant detuning error, time-varying detuning noise and dissipative-beam noise. The fidelities of the protected evolutions are well above the unprotected ones under all the above situations. Our work paves the way for further studies and applications of non-Hermitian $\mathcal{PT}$-symmetric physics in noisy quantum systems.
A theory of cut-restriction: first steps

Cut-elimination is the bedrock of proof theory. It is the algorithm that eliminates cuts from a sequent calculus proof that leads to cut-free calculi and applications. Cut-elimination applies to many logics irrespective of their semantics. Such is its influence that whenever cut-elimination is not provable in a sequent calculus the invariable response has been a move to a richer proof system to regain it. In this paper we investigate a radically different approach to the latter: adapting age-old cut-elimination to restrict the shape of the cut-formulas when elimination is not possible. We tackle the"first level"above cut-free: analytic cuts. Our methodology is applied to the sequent calculi for bi-intuitionistic logic and S5 where analytic cuts are already known to be required. This marks the first steps in a theory of cut-restriction.
Low-rank flat-field correction for artifact reduction in spectral computed tomography

Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat-field correction and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat-fields to reduce ring artifacts in the channel-wise reconstructions. The extended model relies on the assumption that the spectral flat-fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat-fields and can be combined with any existing reconstruction model, e.g., filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction.
Closed-Form Second-Order Partial Derivatives of Rigid-Body Inverse Dynamics

— Optimization-based control methods for robots often rely on ﬁrst-order dynamics approximation methods like in iLQR. Using second-order approximations of the dynamics is expensive due to the costly second-order partial derivatives of dynamics with respect to the state and control. Current approaches for calculating these derivatives typically use automatic differentiation (AD) and chain-rule accumulation or ﬁnite-difference. In this paper, for the ﬁrst time, we present closed-form analytical second-order partial derivatives of inverse dynamics for rigid-body systems with ﬂoating base and multi-DoF joints. A new extension of spatial vector algebra is proposed that enables the analysis. A recursive O ( Nd 2 ) algorithm is also provided where N is the number of bodies and d is the depth of the kinematic tree. A comparison with AD in CasADi shows speedups of 1.5-3 × for serial kinematic trees with N > 5 , and a C++ implementation shows runtimes of ≈ 400 µs for a quadruped.
Playable Environments: Video Manipulation in Space and Time

We present Playable Environments-a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a video by providing a sequence of desired actions. The actions are learnt in an unsupervised manner. The camera can be controlled to get the desired viewpoint. Our method builds an environment state for each frame, which can be manipulated by our proposed action mod-ule and decoded back to the image space with volumetric rendering. To support diverse appearances of objects, we extend neural radiance fields with style-based modulation. Our method trains on a collection of various monocular videos requiring only the estimated camera parameters and 2D object locations. To set a challenging benchmark, we in-troduce two large scale video datasets with significant cam-era movements. As evidenced by our experiments, playable environments enable several creative applications not at-tainable by prior video synthesis works, including playable 3D video generation, stylization and manipulation11willi-menapace.github. io/playable-environments-website.
Near zone dynamical effects in gravity

Victor Massart1∗ and M. B. Paranjape1,2† 1Groupe de physique des particules, Département de physique and 2Centre de recherche mathématiques, Université de Montréal, C.P. 6128, succ. centre-ville, Montréal, Québec, CANADA, H3C 3J7 Abstract Dynamical effects in general relativity have been finally, relatively recently observed by LIGO[1]. These effects correspond to gravitational waves created by the coalescence of black holes or neutron stars billions of years ago and and billions of light years away from their sources. To be able to measure these signals, great care has to be taken to minimize all sources of noise in the detector. One of the sources of noise is called Newtonian noise, the name based on the notion that close by sources would create essentially instantaneous Newtonian gravitational fields. In this article we present an analysis of the dynamical (time dependent) nature of the Newtonian noise. In that respect, it is a misnomer to call it Newtonian noise, the Newtonian theory does not afford any dynamical notion of the gravitational field. We will in fact do our analysis in the context of Einsteinian general relativity. The dynamical aspects of the nature of the Newtonian noise have heretofore been disregarded as they were considered negligible. However, we demonstrate that they are indeed not far from the realm of being measurable. They could be used to validate Einsteinian general relativity or to give valuable information on the true dynamical nature of gravity. One fundamental question, for example, is a direct measurement the speed of propagation of gravitational effects and the verification that it is indeed the same as the speed of light. We propose a simple laboratory experiment that could affirm or deny this proposition. We also analyze the possibility of the detection of large geophysical events, such as earthquakes. We find that large seismic events seem to be easily observable with the present ensemble of gravitational wave detectors,. The ensemble of gravitational wave detectors could easily serve as a system of early warning for otherwise catastrophic seismic events.
Nasal Microbiome Change During and After Exacerbation in Asthmatic Children

Airway and gut microbiota are important in asthma pathogenesis. Although several studies have revealed distinct microbiota in asthmatic airways at baseline compared to healthy controls, limited studies compared microbiota during acute exacerbation (AE) and in the recovery phase (RP) in the same asthmatic children. We aim to investigate association between microbiota and asthma status in children and explore their relationship with clinical features of asthma. We recruited 56 asthmatic children and investigated their nasal, throat, and stool microbiota during AE and in the RP. Totally, 320 samples were subjected to 16S rRNA sequencing. Although the microbial communities were clearly separated by body site, within each site the overall communities during AE and in the RP could not be distinguished. Most nasal microbiota were dominated by only one or two of six bacterial genera. The domination was associated with mite allergy and patient age only during AE but not in the RP. When moving into RP, the relative abundance of Staphylococcus increased while that of Moraxella decreased. Throat and stool microbiota were not associated with most of the clinical features. Interestingly, stool microbiota during AE was associated with ABO blood type and stool microbiota in the RP was associated with frequency of the subsequent exacerbations. In summary, the association between nasal microbiota and mite allergy only during AE suggests an altered local immunity and its interplay with nasal microbes. Our work provides a basis for studying microbes, and prevention or therapeutic strategy in childhood asthma, especially during AE.