Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from (‘gold standard’) OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from ‘lab to field’.
Make-A-Video: Text-to-Video Generation without Text-Video Data

We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.
Bidirectional Language Models Are Also Few-shot Learners

Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitrary task can be reformulated as a natural language prompt, and a language model can be asked to generate the completion, indirectly performing the task in a paradigm known as prompt-based learning. To date, emergent prompt-based learning capabilities have mainly been demonstrated for unidirectional language models. However, bidirectional language models pre-trained on denoising objectives such as masked language modeling produce stronger learned representations for transfer learning. This motivates the possibility of prompting bidirectional models, but their pre-training objectives have made them largely incompatible with the existing prompting paradigm. We present SAP (Sequential Autoregressive Prompting), a technique that enables the prompting of bidirectional models. Utilizing the machine translation task as a case study, we prompt the bidirectional mT5 model (Xue et al., 2021) with SAP and demonstrate its few-shot and zero-shot translations outperform the few-shot translations of unidirectional models like GPT-3 and XGLM (Lin et al., 2021), despite mT5's approximately 50% fewer parameters. We further show SAP is effective on question answering and summarization. For the first time, our results demonstrate prompt-based learning is an emergent property of a broader class of language models, rather than only unidirectional models.
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives.
Minimax Optimal Kernel Operator Learning via Multilevel Training

Learning mappings between infinite-dimensional function spaces has achieved empirical success in many disciplines of machine learning, including generative modeling, functional data analysis, causal inference, and multi-agent reinforcement learning. In this paper, we study the statistical limit of learning a Hilbert-Schmidt operator between two infinite-dimensional Sobolev reproducing kernel Hilbert spaces. We establish the information-theoretic lower bound in terms of the Sobolev Hilbert-Schmidt norm and show that a regularization that learns the spectral components below the bias contour and ignores the ones that are above the variance contour can achieve the optimal learning rate. At the same time, the spectral components between the bias and variance contours give us flexibility in designing computationally feasible machine learning algorithms. Based on this observation, we develop a multilevel kernel operator learning algorithm that is optimal when learning linear operators between infinite-dimensional function spaces.
Quantum Interference and Coherent Population Trapping in a Double Quantum Dot

Quantum interference is a natural consequence of wave-particle duality in quantum mechanics, and is widely observed at the atomic scale. One interesting manifestation of quantum interference is coherent population trapping (CPT), ﬁrst proposed in three-level driven atomic systems and observed in quantum optical experiments. Here, we demonstrate CPT in a gate-deﬁned semiconductor double quantum dot (DQD), with some unique twists as compared to the atomic systems. Specif-ically, we observe CPT in both driven and non-driven situations. We further show that CPT in a driven DQD could be used to generate adiabatic state transfer. Moreover, our experiment reveals a non-trivial modulation to the CPT caused by the longitudinal driving ﬁeld, yielding an odd-even eﬀect and a tunable CPT.
GROOT: Corrective Reward Optimization for Generative Sequential Labeling

Sequential labeling is a fundamental NLP task, forming the backbone of many applications. Supervised learning of Seq2Seq models has shown great success on these problems. However, the training objectives are still significantly disconnected with the metrics and desiderata we care about in practice. For example, a practical sequence tagging application may want to optimize for a certain precision-recall trade-off (of the top-k predictions) which is quite different from the standard objective of maximizing the likelihood of the gold labeled sequence. Thus to bridge this gap, we propose GROOT -- a simple yet effective framework for Generative Reward Optimization Of Text sequences. GROOT works by training a generative sequential labeling model to match the decoder output distribution with that of the (black-box) reward function. Using an iterative training regime, we first generate prediction candidates, then correct errors in them, and finally contrast those candidates (based on their reward values). As demonstrated via extensive experiments on four public benchmarks, GROOT significantly improves all reward metrics. Furthermore, GROOT leads to improvements of the overall decoder distribution as evidenced by the quality gains of the top-$k$ candidates.
Local well-posedness for the compressible Navier-Stokes-BGK model in Sobolev spaces with exponential weight

Sprays are complex flows constituted of dispersed particles in an underlying gas. In this paper, we are interested in the equations for moderately thick sprays consisting of the compressible Navier-Stokes equations and Boltzmann BGK equation. Here the coupling of two equations is through a friction (or drag) force which depends on the density of compressible fluid and the relative velocity between particles and fluid. For the Navier-Stokes-BGK system, we establish the existence and uniqueness of solutions in Sobolev spaces with exponential weight.
Improved nearly minimax prediction for independent Poisson processes under Kullback-Leibler loss

The problem of predicting independent Poisson random variables is commonly encountered in real-life practice. Simultaneous predictive distributions for independent Poisson observables are investigated, and the performance of predictive distributions is evaluated using the Kullback-Leibler (K-L) loss. This study introduces intuitive sufficient conditions, based on superharmonicity of priors, to improve the Bayesian predictive distribution based on the Jeffreys prior. The sufficient conditions exhibit a certain analogy with those known for the multivariate normal distribution. Additionally, this study examines the case where the observed data and target variables to be predicted are independent Poisson processes with different durations. Examples that satisfy the sufficient conditions are provided, including point and subspace shrinkage priors. The K-L risk of the improved predictions is demonstrated to be less than 1.04 times a minimax lower bound.
Study of transversity GPDs from pseudoscalar mesons production at EIC of China

The exclusive η and π 0 electroproduction is studied in the handbag approach based on the generalized parton distributions (GPDs) factorization. Predictions of π 0 and η mesons are calculated for future electron-ion collider in China (EicC) energy ranges, using obtained cross sections we extract information on the transversity GPDs contributions to these processes.
Cosmic Reionization on Computers: Baryonic Effects on Halo Concentrations during the Epoch of Reionization

Baryons both increase halo concentration through adiabatic contraction and expel mass through feedback processes. However, it is not well understood how the radiation fields prevalent during the epoch of reionization affect the evolution of concentration in dark matter halos. We investigate how baryonic physics during the epoch of reionization modify the structure of dark matter halos in the Cosmic Reionization On Computers (CROC) simulations. We use two different measures of halo concentration to quantify the effects. We compare concentrations of halos matched between full-physics simulations and dark-matter-only simulations with identical initial conditions between 5 ≤ z ≤ 9. Baryons in full-physics simulations do pull matter toward the center, increasing the maximum circular velocity compared to dark-matter-only simulations. However, their overall effects are much less than if all the baryons were simply centrally concentrated indicating that heating processes efficiently counteract cooling effects. Finally, we show that the baryonic effects on halo concentrations at z ≈ 5 are relatively insensitive to environmental variations of reionization history. These results are pertinent to models of galaxy–halo connection during the epoch of reionization.
Mahler measures and $L$-values of elliptic curves over real quadratic fields

A famous formula of Rodriguez Villegas shows that the Mahler measures $m(k)$ of $P_k(x,y)=x+1/x+y+1/y+k$ can be written as a Kronecker-Eisenstein series. We prove that the degree of $k$ in Villegas' formula can be bounded by the class numbers of CM points. This fact allows us to systematically derive $28$ new identities linking $m(k)$ to $L$-values of cusp forms. Guided by Beilinson's conjecture, we also prove $5$ formulas that express $L$-values of CM elliptic curves over real quadratic fields to some $2\times 2$ determinants of $m(k)$. This extends a recent work of Guo (the second author of this paper), Ji, Liu, and Qin, in which they dealt with the cases when $k=4\pm 4\sqrt{2}$.
Deep probing of the photospheric sunspot penumbra: no evidence of field-free gaps

Context. Some models for the topology of the magnetic field in sunspot penumbrae predict regions free of magnetic fields or with only dynamically weak fields in the deep photosphere. Aims. We aim to confirm or refute the existence of weak-field regions in the deepest photospheric layers of the penumbra. Methods. We investigated the magnetic field at log τ 5 = 0 is by inverting spectropolarimetric data of two different sunspots located very close to disk center with a spatial resolution of approximately 0.4−0.45′′. The data have been recorded using the GRIS instrument attached to the 1.5-m solar telescope GREGOR at the El Teide observatory. The data include three Fe i lines around 1565 nm, whose sensitivity to the magnetic field peaks half a pressure scale height deeper than the sensitivity of the widely used Fe i spectral line pair at 630 nm. Before the inversion, the data were corrected for the effects of scattered light using a deconvolution method with several point spread functions. Results. At log τ 5 = 0 we find no evidence of regions with dynamically weak ( B < 500 Gauss) magnetic fields in sunspot penumbrae. This result is much more reliable than previous investigations made on Fe i lines at 630 nm. Moreover, the result is independent of the number of nodes employed in the inversion, is independent of the point spread function used to deconvolve the data, and does not depend on the amount of stray light (i.e., wide-angle scattered light) considered.
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms

Partial Observability -- where agents can only observe partial information about the true underlying state of the system -- is ubiquitous in real-world applications of Reinforcement Learning (RL). Theoretically, learning a near-optimal policy under partial observability is known to be hard in the worst case due to an exponential sample complexity lower bound. Recent work has identified several tractable subclasses that are learnable with polynomial samples, such as Partially Observable Markov Decision Processes (POMDPs) with certain revealing or decodability conditions. However, this line of research is still in its infancy, where (1) unified structural conditions enabling sample-efficient learning are lacking; (2) existing sample complexities for known tractable subclasses are far from sharp; and (3) fewer sample-efficient algorithms are available than in fully observable RL. This paper advances all three aspects above for Partially Observable RL in the general setting of Predictive State Representations (PSRs). First, we propose a natural and unified structural condition for PSRs called \emph{B-stability}. B-stable PSRs encompasses the vast majority of known tractable subclasses such as weakly revealing POMDPs, low-rank future-sufficient POMDPs, decodable POMDPs, and regular PSRs. Next, we show that any B-stable PSR can be learned with polynomial samples in relevant problem parameters. When instantiated in the aforementioned subclasses, our sample complexities improve substantially over the current best ones. Finally, our results are achieved by three algorithms simultaneously: Optimistic Maximum Likelihood Estimation, Estimation-to-Decisions, and Model-Based Optimistic Posterior Sampling. The latter two algorithms are new for sample-efficient learning of POMDPs/PSRs.
Temperature dependence of upper critical fields and coherence lengths for optimally-doped YBa2Cu3O7–δ thin films

We report the comprehensive comparative analysis of the upper critical magnetic fields μ Hc2(0) obtained within Ginzburg–Landau (GL) and Werthamer–Helfand–Hohenberg (WHH) theories for optimally-doped YBa2Cu3O7–δ thin films. For different orientations of the magnetic field, our calculations give 638 and 153 T for μ Hc2(0), H || ab and μ Hc2(0), H || c, respectively, when using Hc2(0). For the first time, the temperature dependences of coherence lengths ξ ab( T) and ξ c( T) within proposed theories were determined using 50 and 90% criteria of the normal state resistivity value ρ N. The GL (0.9ρ N) approach gives ξ ab(0) = 11.8 Å and ξ c(0) = 3.0 Å which are in a good agreement with literature data. The implications of very short coherence lengths in HTSCs are discussed.
Compositional Semantic Parsing with Large Language Models

Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN. In this work, we identify additional challenges in more realistic semantic parsing tasks with larger vocabulary and refine these prompting techniques to address them. Our best method is based on least-to-most prompting: it decomposes the problem using prompting-based syntactic parsing, then uses this decomposition to select appropriate exemplars and to sequentially generate the semantic parse. This method allows us to set a new state of the art for CFQ while requiring only 1% of the training data used by traditional approaches. Due to the general nature of our approach, we expect similar efforts will lead to new results in other tasks and domains, especially for knowledge-intensive applications.
Low-field magnetic anomalies in single crystals of the A-type square-lattice antiferromagnet 
EuGa4

The body-centered-tetragonal antiferromagnet EuGa$_4$ was recently identified as a Weyl nodal-line semimetal that exhibits the topological Hall effect below its reported antiferromagnetic (AFM) ordering temperature $T_{\rm N}= 15$-16.5 K which we find to be $T_{\rm N} = 16.4(2)$ K. The Eu$^{+2}$ ions are located at the corners and body center of the unit cell. EuGa$_4$ exhibits A-type AFM order below $T_{\rm N}$, where the Eu$^{2+}$ spin-7/2 moments are ferromagnetically aligned in the $ab$ plane with the Eu moments in adjacent Eu planes along the $c$ axis aligned antiferromagnetically. Low-field magnetization versus field $M(H_{ab})$ data at $T=2$ K with the field aligned in the $ab$ plane are reported that exhibit anomalous positive curvature up to a critical field $H_{c1}$ at which a second-order transition occurs with $H_{c1}\approx 0.85$ kOe for ${\bf H}\parallel [1,1,0]$ and $\approx 4.8$ kOe for ${\bf H}\parallel [1,0,0]$. For larger fields, a linear behavior $M_{ab} = \chi(T_{\rm N})H_{ab}$ is followed until the previously-reported critical field $H^{\rm c}_{ab} = 71$ kOe is reached at which all moments become aligned with the applied field. A theory is formulated for $T=0$ K that fits the observed $M(H_{ab})$ behavior at $T=2$ K well, where domains of A-type AFM order with fourfold rotational symmetry occur in the AFM state in zero field. The moments in the four domains reorient to become almost perpendicular to ${\bf H}_{ab}$ at $H_{c1}$, followed by increasing canting of all moments toward the field with increasing field up to $H^{\rm c}_{ab}$. A first-order transition in $M(H_{ab})$ at $H_{ab}=H_{\rm c1}$ is predicted by the theory for $T=0$ K when ${\bf H}_{ab}$ is at a small angle from the [1,0,0] or [1,1,0] directions.
Sparse PCA With Multiple Components

Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensional datasets in an interpretable manner. This involves solving a sparsity and orthogonality constrained convex maximization problem, which is extremely computationally challenging. Most existing works address sparse PCA via methods-such as iteratively computing one sparse PC and deflating the covariance matrix-that do not guarantee the orthogonality, let alone the optimality, of the resulting solution when we seek multiple mutually orthogonal PCs. We challenge this status by reformulating the orthogonality conditions as rank constraints and optimizing over the sparsity and rank constraints simultaneously. We design tight semidefinite relaxations to supply high-quality upper bounds, which we strengthen via additional second-order cone inequalities when each PC's individual sparsity is specified. Further, we derive a combinatorial upper bound on the maximum amount of variance explained as a function of the support. We exploit these relaxations and bounds to propose exact methods and rounding mechanisms that, together, obtain solutions with a bound gap on the order of 0%-15% for real-world datasets with p = 100s or 1000s of features and r \in {2, 3} components. Numerically, our algorithms match (and sometimes surpass) the best performing methods in terms of fraction of variance explained and systematically return PCs that are sparse and orthogonal. In contrast, we find that existing methods like deflation return solutions that violate the orthogonality constraints, even when the data is generated according to sparse orthogonal PCs. Altogether, our approach solves sparse PCA problems with multiple components to certifiable (near) optimality in a practically tractable fashion.
Multiple Modes for Continual Learning

Adapting model parameters to incoming streams of data is a crucial factor to deep learning scalability. Interestingly, prior continual learning strategies in online settings inadvertently anchor their updated parameters to a local parameter subspace to remember old tasks, else drift away from the subspace and forget. From this observation, we formulate a trade-oﬀ between constructing multiple parameter modes and allocating tasks per mode. Mode-Optimized Task Allocation (MOTA), our contributed adaptation strategy, trains multiple modes in parallel, then optimizes task allocation per mode. We empirically demonstrate improvements over baseline continual learning strategies and across varying distribution shifts, namely sub-population, domain, and task shift.