Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under the standard i.i. d. , out-of-distribution, and low-resource settings validate GraphQ IR’s superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.
Can we accurately calculate viscosity in multicomponent metallic melts?

Calculating viscosity in multicompoinent metallic melts is a challenging task for both classical and ab initio molecular dynamics simulations methods. The former may not to provide enough accuracy and the latter is too resources de-manding. Machine learning potentials provide optimal balance between accuracy and computational eﬃciency and so seem very promising to solve this problem. Here we address simulating kinematic viscosity in ternary Al-Cu-Ni melts with using deep neural network potentials (DP) as implemented in the DeePMD-kit. We calculate both concentration and temperature dependencies of kinematic viscosity in Al-Cu-Ni and conclude that the developed potential allows one to simulate viscosity with high accuracy; the deviation from experimental data does not exceed 9% and is close to the uncertainty interval of experimental data. More importantly, our simulations reproduce minimum on concentration dependency of the viscosity at the eutectic point. Thus, we conclude that DP-based MD simulations is highly promising way to calculate viscosity in multicomponent metallic melts.
Clustering above Exponential Families with Tempered Exponential Measures

The link with exponential families has allowed $k$-means clustering to be generalized to a wide variety of data generating distributions in exponential families and clustering distortions among Bregman divergences. Getting the framework to work above exponential families is important to lift roadblocks like the lack of robustness of some population minimizers carved in their axiomatization. Current generalisations of exponential families like $q$-exponential families or even deformed exponential families fail at achieving the goal. In this paper, we provide a new attempt at getting the complete framework, grounded in a new generalisation of exponential families that we introduce, tempered exponential measures (TEM). TEMs keep the maximum entropy axiomatization framework of $q$-exponential families, but instead of normalizing the measure, normalize a dual called a co-distribution. Numerous interesting properties arise for clustering such as improved and controllable robustness for population minimizers, that keep a simple analytic form.
When Geometry is not Enough: Using Reflector Markers in Lidar SLAM

Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.
CMB anisotropy due to filamentary gas: power spectrum and cosmological parameter bias

Hot gas in filamentary structures induces CMB aniostropy through the SZ effect. Guided by results from N-body simulations, we model the morphology and gas properties of filamentary gas and determine the power spectrum of the anisotropy. Our treatment suggests that power levels can be an appreciable fraction of the cluster contribution at multipoles l1500. Its spatially irregular morphology and larger characteristic angular scales can help to distinguish this SZ signature from that of clusters. In addition to intrinsic interest in this most extended SZ signal as a probe of filaments, its impact on cosmological parameter estimation should also be assessed. We find that filament 'noise' can potentially bias determination of As, ns, and w (the normalization of the primordial power spectrum, the scalar index, and the dark energy equation of state parameter, respectively) by more than the nominal statistical uncertainty in Planck SZ survey data. More generally, when inferred from future optimal cosmic-variance-limited CMB experiments, we find that virtually all parameters will be biased by more than the nominal statistical uncertainty estimated for these next generation CMB experiments.
From Denoising Diffusions to Denoising Markov Models

Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution and then learning to reverse this noising process to obtain synthetic datapoints. The denoising diffusion relies on approximations of the logarithmic derivatives of the noised data densities using score matching. Such models can also be used to perform approximate posterior simulation when one can only sample from the prior and likelihood. We propose a unifying framework generalising this approach to a wide class of spaces and leading to an original extension of score matching. We illustrate the resulting models on various applications.
A Note on Approximate Inverse Iteration

Different variants of approximate inverse iteration like the locally optimal block preconditioned conjugate gradient method became in recent years increasingly popular for the solution of the large matrix eigenvalue problems arising from the discretization of selfadjoint elliptic partial differential equations, in particular for the calculation of the minimum eigenvalue. We extend in this little note the classical convergence theory of D'yakonov and Orekhov [Math. Notes 27 (1980)] to the case of operators with an essential spectrum on infinite dimensional Hilbert spaces and allow for arbitrary, sufficiently small perturbations of the solutions of the equation that links the iterates. The note complements the much more elaborate convergence theory of Neymeyr and Knyazev and Neymeyr for the matrix case (see [Knyazev and Neymeyr, SIAM J. Matrix Anal. Appl. 31 (2009)] and the references therein) and is suitable for classroom presentation.
Localization and the link Floer homology of doubly-periodic knots

A knot \widetilde{K} \subset S^3 is q-periodic if there is a \mathbb Z_q-action preserving \widetilde{K} whose fixed set is an unknot U. The quotient of \widetilde{K} under the action is a second knot K. We construct equivariant Heegaard diagrams for q-periodic knots, and show that Murasugi's classical condition on the Alexander polynomials of periodic knots is a quick consequence of these diagrams. For \widetilde{K} a two-periodic knot, we show there is a spectral sequence whose E^1 page is \hat{\mathit{HFL}}(S^3,\widetilde{K}\cup U)\otimes V^{\otimes (2n-1)})\otimes \mathbb Z_2((\theta)) and whose E^{\infty} pages is isomorphic to (\hat{\mathit{HFL}}(S^3,K\cup U)\otimes V^{\otimes (n-1)})\otimes \mathbb Z_2((\theta)), as \mathbb Z_2((\theta))-modules, and a related spectral sequence whose E^1 page is (\hat{\mathit{HFK}}(S^3,\widetilde{K})\otimes V^{\otimes (2n-1)}\otimes W)\otimes \mathbb Z_2((\theta)) and whose E^{\infty} page is isomorphic to (\hat{\mathit{HFK}}(S^3,K)\otimes V^{\otimes (n-1)} \otimes W)\otimes \mathbb Z_2((\theta)). As a consequence, we use these spectral sequences to recover a classical lower bound of Edmonds on the genus of \widetilde{K}, along with a weak version of a classical fibredness result of Edmonds and Livingston.
BriFiSeg: a deep learning-based method for semantic and instance segmentation of nuclei in brightfield images

: Generally, microscopy image analysis in biology relies on the segmentation of individual nuclei, using a dedicated stained image, to identify individual cells. However stained nuclei have drawbacks like the need for sample preparation, and specific equipment on the microscope but most importantly, and as it is in most cases, the nuclear stain is not relevant to the biological questions of interest but is solely used for the segmentation task. In this study, we used non-stained brightfield images for nuclei segmentation with the advantage that they can be acquired on any microscope from both live or fixed samples and do not necessitate specific sample preparation. Nuclei semantic segmentation from brightfield images was obtained, on four distinct cell lines with U-Net-based architectures. We tested systematically deep pre-trained encoders to identify the best performing in combination with the different neural network architectures used. Additionally, two distinct and effective strategies were employed for instance segmentation, followed by thorough instance evaluation. We obtained effective semantic and instance segmentation of nuclei in brightfield images from standard test sets as well as from very diverse biological contexts triggered upon treatment with various small molecule inhibitor. The code used in this study was made public to allow further use by the community.
Small-scale dynamo in cool stars. II. The effect of metallicity

All cool main sequence stars including our Sun are thought to have magnetic fields. Observations of the Sun revealed that even in quiet regions small-scale turbulent magnetic fields are present. Simulations further showed that such magnetic fields affect the subsurface and photospheric structure, and thus the radiative transfer and emergent flux. Since small-scale turbulent magnetic fields on other stars cannot be directly observed, it is imperative to study their effects on the near surface layers numerically. Until recently comprehensive three-dimensional simulations capturing the effect of small-scale turbulent magnetic fields only exists for the solar case. A series of investigations extending SSD simulations for other stars has been started. Here we aim to examine small-scale turbulent magnetic fields in stars of solar effective temperature but different metallicity. We investigate the properties of three-dimensional simulations of the magneto-convection in boxes covering the upper convection zone and photosphere carried out with the MURaM code for metallicity values of $ \rm M/H = \{-1.0, 0.0, 0.5\}$ with and without a small-scale-dynamo. We find that small-scale turbulent magnetic fields enhanced by a small-scale turbulent dynamo noticeably affect the subsurface dynamics and significantly change the flow velocities in the photosphere. Moreover, significantly stronger magnetic field strengths are present in the convection zone for low metallicity. Whereas, at the optical surface the averaged vertical magnetic field ranges from 64G for M/H = 0.5 to 85G for M/H = -1.0.
An approach to standardize, automate omni-channel and AI transactional digital service creation

Our work is at the crossroads of two categories of technologies. On the one hand, omnichannel digit services, to address the needs of users in the most seamless way. On the other hand, low code approaches, to build simply even complex software applications. In this twofold context, we propose DSUL (Digital Service Universal Language). It allows to build omnichannel services with minimal work from their designers. We describe precisely how DSUL operates, and its innovation in regard to the state of the art. We also consider the various methods to evaluate this framework.
De novo PROTAC design using graph-based deep generative models

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic modality for degrading a protein of interest (POI) by marking it for degradation by the proteasome. Recent developments in artificial intelligence (AI) suggest that deep generative models can assist with the de novo design of molecules with desired properties, and their application to PROTAC design remains largely unexplored. We show that a graph-based generative model can be used to propose novel PROTAC-like structures from empty graphs. Our model can be guided towards the generation of large molecules (30--140 heavy atoms) predicted to degrade a POI through policy-gradient reinforcement learning (RL). Rewards during RL are applied using a boosted tree surrogate model that predicts a molecule's degradation potential for each POI. Using this approach, we steer the generative model towards compounds with higher likelihoods of predicted degradation activity. Despite being trained on sparse public data, the generative model proposes molecules with substructures found in known degraders. After fine-tuning, predicted activity against a challenging POI increases from 50% to>80% with near-perfect chemical validity for sampled compounds, suggesting this is a promising approach for the optimization of large, PROTAC-like molecules for targeted protein degradation.
Metric geometry of normal K\"ahler spaces, energy properness, and existence of canonical metrics

Let $(X,\omega)$ be a compact normal K\"ahler space, with Hodge metric $\omega$. In this paper, the last in a sequence of works studying the relationship between energy properness and canonical K\"ahler metrics, we introduce a geodesic metric structure on $\mathcal H_{\omega}(X)$, the space of K\"ahler potentials, whose completion is the finite energy space $\mathcal E^1_{\omega}(X)$. Using this metric structure and the results of Berman-Boucksom-Eyssidieux-Guedj-Zeriahi as ingredients in the existence/properness principle of Rubinstein and the author, we show that existence of K\"ahler-Einstein metrics on log Fano pairs is equivalent to properness of the K-energy in a suitable sense. To our knowledge, this result represents the first characterization of general log Fano pairs admitting K\"ahler-Einstein metrics. We also discuss the analogous result for K\"ahler-Ricci solitons on Fano varieties.
GmGM: a Fast Multi-Axis Gaussian Graphical Model

This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the ungeneralized case. This allows the use of our methodology on large multi-modal datasets such as single-cell multi-omics data, which was challenging with previous approaches. We validate our model on synthetic data and five real-world datasets.
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the interpretability of latent variables as biological processes that define a cell's identity. Outside of biological applications, this problem is commonly referred to as learning disentangled representations. Although several disentanglement-promoting variants of the VAE were introduced, and applied to single-cell genomics data, this task has been shown to be infeasible from independent and identically distributed measurements, without additional structure. Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic. Here, we extend the application of these methodological advances to the analysis of single-cell genomics data with genetic or chemical perturbations. More precisely, we propose a deep generative model of single-cell gene expression data for which each perturbation is treated as a stochastic intervention targeting an unknown, but sparse, subset of latent variables. We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization. Finally, we apply those approaches to two real-world large-scale gene perturbation data sets and find that models that exploit the sparse mechanism shift hypothesis surpass contemporary methods on a transfer learning task. We implement our new model and benchmarks using the scvi-tools library, and release it as open-source software at https://github.com/Genentech/sVAE.
Integrating Voice-Based Machine Learning Technology into Complex Home Environments

To demonstrate the value of machine learning based smart health technologies, researchers have to deploy their solutions into complex real-world environments with real participants. This gives rise to many, oftentimes unexpected, challenges for creating technology in a lab environment that will work when deployed in real home environments. In other words, like more mature disciplines, we need solutions for what can be done at development time to increase success at deployment time. To illustrate an approach and solutions, we use an example of an ongoing project that is a pipeline of voice based machine learning solutions that detects the anger and verbal conflicts of the participants. For anonymity, we call it the XYZ system. XYZ is a smart health technology because by notifying the participants of their anger, it encourages the participants to better manage their emotions. This is important because being able to recognize one’s emotions is the first step to better managing one’s anger. XYZ was deployed in 6 homes for 4 months each and monitors the emotion of the caregiver of a dementia patient. In this paper we demonstrate some of the necessary steps to be accomplished during the development stage to increase deployment time success, and show where continued work is still necessary. Note that the complex environments arise both from the physical world and from complex human behavior. a voice-based machine learning solutions in-home monitoring
Observations of Transiting Exoplanets with the James Webb Space Telescope (JWST)

This article summarizes a workshop held on March, 2014, on the potential of the James Webb Space Telescope (JWST) to revolutionize our knowledge of the physical properties of exoplanets through transit observations. JWST’s unique combination of high sensitivity and broad wavelength coverage will enable the accurate measurement of transits with high signal-to-noise ratio (S/N). Most importantly, JWST spectroscopy will investigate planetary atmospheres to determine atomic and molecular compositions, to probe vertical and horizontal structure, and to follow dynamical evolution, i.e., exoplanet weather. JWST will sample a diverse population of planets of varying masses and densities in a wide variety of environments characterized by a range of host star masses and metallicities, orbital semi-major axes, and eccentricities. A broad program of exoplanet science could use a substantial fraction of the overall JWST mission.
Developing Decentralised Resilience to Malicious Influence in Collective Perception Problem

—In collective decision-making, designing algorithms that use only local information to effect swarm-level behaviour is a non-trivial problem. We used machine learning techniques to teach swarm members to map their local perceptions of the environment to an optimal action. A curriculum inspired by Machine Education approaches was designed to facilitate this learning process and teach the members the skills required for optimal performance in the collective perception problem. We extended upon previous approaches by creating a curriculum that taught agents resilience to malicious inﬂuence. The exper-imental results show that well-designed rules-based algorithms can produce effective agents. When performing opinion fusion, we implemented decentralised resilience by having agents dy- namically weight received opinion. We found a non-signiﬁcant difference between constant and dynamic weights, suggesting that momentum-based opinion fusion is perhaps already a resilience mechanism.
True optical spatial derivatives for direct phasegradient measurements

This paper shows analytically and numerically that a vortex plate coupled to a neutral density filter can deliver a true optical derivative when placed at the focal plane of a $2f$ lens pair. This technique turns spatial variations in intensity into an intensity, which square root is the spatial derivative of the initial intensity variation. More surprisingly, it also turns any spatial variations in phase into an intensity, which square root is the spatial derivative of the initial phase variation. Since the optical derivative drops the DC component of the signal, it is possible to measure the full electron plasma turbulence spectrum optically, without using any interferometer.
Numerical solution of fractional order diffusion problems with Neumann boundary conditions

A finite difference numerical method is investigated for fractional order diffusion problems in one space dimension. For this, a mathematical model is developed to incorporate homogeneous Dirichlet and Neumann type boundary conditions. The models are based on an appropriate extension of the initial values. The well-posedness of the obtained initial value problems is proved and it is pointed out that the extensions are compatible with the above boundary conditions. Accordingly, a finite difference scheme is constructed for the Neumann problem using the shifted Gr\"unwald--Letnikov approximation of the fractional order derivatives, which is based on infinite many basis points. The corresponding matrix is expressed in a closed form and the convergence of an appropriate implicit Euler scheme is proved.
Spin and orbital magnetic response in metals: Susceptibility and NMR shifts

A DFT-based method is presented which allows the computation of all-electron NMR shifts of metallic compounds with periodic boundary conditions. NMR shifts in metals measure two competing physical phenomena. Electrons interact with the applied magnetic field (i) as magnetic dipoles (or spins), resulting in the Knight shift, and (ii) as moving electric charges, resulting in the chemical (or orbital) shift. The latter is treated through an extension to metals of the gauge-invariant projector augmented wave developed for insulators. The former is modeled as the hyperfine interaction between the electronic spin polarization and the nuclear dipoles. NMR shifts are obtained with respect to the computed shieldings of reference compounds, yielding fully ab initio quantities which are directly comparable to experiment. The method is validated by comparing the magnetic susceptibility of interacting and noninteracting homogeneous gas with known analytical results, and by comparing the computed NMR shifts of simple metals with experiment.