Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-differentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration

Exploiting sparsity is a key technique in accelerating quantized convolutional neural network (CNN) inference on mobile devices. Prior sparse CNN accelerators largely exploit unstructured sparsity and achieve significant speedups. Due to the unbounded, largely unpredictable sparsity patterns, however, exploiting unstructured sparsity requires complicated hardware design with significant energy and area overhead, which is particularly detrimental to mobile/IoT inference scenarios where energy and area efficiency are crucial.We propose to exploit structured sparsity, more specifically, Density Bound Block (DBB) sparsity for both weights and activations. DBB block tensors bound the maximum number of non-zeros per block. DBB thus exposes statically predictable sparsity patterns that enable lean sparsity-exploiting hardware and efficient memory access. We propose new hardware primitives to implement DBB sparsity for (static) weights and (dynamic) activations, respectively, with very low overheads.Building on top of the primitives, we describe S2TA, a systolic array-based CNN accelerator that exploits joint weight and activation DBB sparsity and new dimensions of data reuse unavailable on the traditional systolic array. S2TA in 16nm achieves more than 2× speedup and energy reduction compared to a strong baseline of a systolic array with zero-value clock gating, over five popular CNN benchmarks. Compared to two recent non-systolic sparse accelerators, Eyeriss v2 (65nm) and SparTen (45nm), S2TA in 65nm uses about 2.2× per and 3.1× less energy inference, respectively.
Attention-based Vehicle Self-Localization with HD Feature Maps

We present a vehicle self-localization method using point-based deep neural networks. Our approach processes measurements and point features, i.e. landmarks, from a high-definition digital map to infer the vehicle's pose. To learn the best association and incorporate local information between the point sets, we propose an attention mechanism that matches the measurements to the corresponding landmarks. Finally, we use this representation for the point-cloud registration and the subsequent pose regression task. Furthermore, we introduce a training simulation framework that artificially generates measurements and landmarks to facilitate the deployment process and reduce the cost of creating extensive datasets from real-world data. We evaluate our method on our dataset, as well as an adapted version of the Kitti odometry dataset, where we achieve superior performance compared to related approaches; and additionally show dominant generalization capabilities.
On universal approximation and error bounds for Fourier Neural Operators

Fourier neural operators (FNOs) have recently been proposed as an effective framework for learning operators that map between infinite-dimensional spaces. We prove that FNOs are universal, in the sense that they can approximate any continuous operator to desired accuracy. Moreover, we suggest a mechanism by which FNOs can approximate operators associated with PDEs efficiently. Explicit error bounds are derived to show that the size of the FNO, approximating operators associated with a Darcy type elliptic PDE and with the incompressible Navier-Stokes equations of fluid dynamics, only increases sub (log)-linearly in terms of the reciprocal of the error. Thus, FNOs are shown to efficiently approximate operators arising in a large class of PDEs.
Online Graph Topology Learning from Matrix-valued Time Series

This paper is concerned with the statistical analysis of matrix-valued time series. These are data collected over a network of sensors (typically a set of spatial locations) along time, where a vector of features is observed per time instant per sensor. Thus each sensor is characterized by a vectorial time series. We would like to identify the dependency structure among these sensors and represent it by a graph. When there is only one feature per sensor, the vector auto-regressive models have been widely adapted to infer the structure of Granger causality. The resulting graph is referred to as causal graph. Our first contribution is then extending VAR models to matrix-variate models to serve the purpose of graph learning. Secondly, we propose two online procedures respectively in low and high dimensions, which can update quickly the estimates of coefficients when new samples arrive. In particular in high dimensional regime, a novel Lasso-type is introduced and we develop its homotopy algorithms for the online learning. We also provide an adaptive tuning procedure for the regularization parameter. Lastly, we consider that, the application of AR models onto data usually requires detrending the raw data, however, this step is forbidden in online context. Therefore, we augment the proposed AR models by incorporating trend as extra parameter, and then adapt the online algorithms to the augmented data models, which allow us to simultaneously learn the graph and trend from streaming samples. In this work, we consider primarily the periodic trend. Numerical experiments using both synthetic and real data are performed, whose results support the effectiveness of the proposed methods.
Relative entropic uncertainty relation for scalar quantum fields

Entropic uncertainty is a well-known concept to formulate uncertainty relations for continuous variable quantum systems with finitely many degrees of freedom. Typically, the bounds of such relations scale with the number of oscillator modes, preventing a straight-forward generalization to quantum field theories. In this work, we overcome this difficulty by introducing the notion of a functional relative entropy and show that it has a meaningful field theory limit. We present the first entropic uncertainty relation for a scalar quantum field theory and exemplify its behavior by considering few particle excitations and the thermal state. Also, we show that the relation implies the multidimensional Heisenberg uncertainty relation.
Obtaining Causal Information by Merging Datasets with MAXENT

The investigation of the question"which treatment has a causal effect on a target variable? "is of particular relevance in a large number of scientific disciplines. This challenging task becomes even more difficult if not all treatment variables were or even cannot be observed jointly with the target variable. Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders. In this paper, we discuss how causal knowledge can be obtained without having observed all variables jointly, but by merging the statistical information from different datasets. We show how the maximum entropy principle can be used to identify edges among random variables when assuming causal sufficiency and an extended version of faithfulness, and when only subsets of the variables have been observed jointly.
Experimental snapshot verification of non-Markovianity by quantum probing of convex coefficients

We apply the recently proposed quantum probing protocols with an unknown system-probe coupling to probe the convex coefficients in mixtures of commuting states. By using two reference states instead of one as originally suggested, we are able to probe both lower and upper bounds for the convex coefficient. We perform extensive analysis for the roles of the parameters characterizing the double peaked Gaussian frequency spectrum in the Markovian-to-non-Markovian transition of the polarization dynamics of a single photon. We apply the probing of the convex coefficient to the transition-inducing frequency parameter and show that the non-Markovianity of the polarization dynamics can be confirmed with a single snapshot measurement of the polarization qubit performed at unknown time and even with unknown coupling. We also show how the protocol can identify Markovian and non-Markovian time intervals in the dynamics. The results are validated with single photon experiments.
Towards a Benchmark Set for Program Repair Based on Partial Fixes

Software bugs significantly contribute to software cost and increase the risk of system malfunctioning. In recent years, many automated program-repair approaches have been proposed to automatically fix undesired program behavior. Despite of their great success, specific problems such as fixing bugs with partial fixes still remain unresolved. A partial fix to a known software issue is a programmer's failed attempt to fix the issue the first time. Even though it fails, this fix attempt still conveys important information such as the suspicious software region and the bug type. In this work we do not propose an approach for program repair with partial fixes, but instead answer a preliminary question: Do partial fixes occur often enough, in general, to be relevant for the research area of automated program repair? We crawled 1500 open-source C repositories on GitHub for partial fixes. The result is a benchmark set of 2204 benchmark tasks for automated program repair based on partial fixes. The benchmark set is available open source and open to further contributions and improvement.
A resource efficient design strategy to optimise the temperature coefficient of capacitance of BaTiO3-based ceramics using finite element modelling

An in-house finite element modelling package is used to simulate the electrical response of core–shell microstructures of BaTiO3-based (BT) dielectric materials as a design strategy for multilayer ceramic capacitors. By combining this method with experimental material properties, both the temperature coefficient of capacitance (TCC) and the volume ratio of undoped BT ceramic (core phase) to chemically doped BT ceramic (shell phase) to optimise temperature stability can be predicted. This is a faster, more reliable and efficient design strategy than current techniques which are based on iterative experimental protocols. This methodology is illustrated using rare earth-free NaNbO3-doped BT (NNBT) ceramics as an example for which we predict and experimentally confirm an optimised volume fraction ratio of 0.66 undoped BT core to 0.34 of a 2.5% NNBT shell.
Privacy-Preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, in this article, a novel federated deep generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least square generative adversarial networks (LSGANs) for renewable scenario generation. Specifically, federated learning learns a shared global model in a central server from renewable sites at network edges, which enables the Fed-LSGAN to generate scenarios in a privacy-preserving manner without sacrificing the generation quality by transferring model parameters, rather than all data. Meanwhile, the LSGANs-based deep generative model generates scenarios that conform to the distribution of historical data through fully capturing the spatial-temporal characteristics of renewable powers, which leverages the least squares loss function to improve the training stability and generation quality. The simulation results demonstrate that the proposal manages to generate high-quality renewable scenarios and outperforms the state-of-the-art centralized methods. Besides, an experiment with different federated learning settings is designed and conducted to verify the robustness of our method.
Ethyl 2-(6-bromo-2-phenyl-3H-imidazo[4,5-b]pyridin-3-yl)acetate

The title imidazo [4,5-b] pyridine derivative, C16H14BrN3O2, crystallizes with two independent mol­ecules (1 and 2) in the asymmetric unit. In mol­ecule 1, the pendant phenyl ring is inclined to the imidazo[4,5-b]pyridine core by 43.10 (4)° while in mol­ecule 2 the corresponding angle is 49.43 (4)°. The two mol­ecules differ primarily in the conformations of the ester substituents. In the crystal, mol­ecules are linked via C—H⋯N and C—H⋯O hydrogen bonds, forming sheets parallel to the ab plane.
Reinforcement Learning for Optimal Stationary Control of Linear Stochastic Systems

—This paper studies the optimal stationary control of continuous-time linear stochastic systems with both additive and multiplicative noises, using reinforcement learning techniques. Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed which is able to iteratively ﬁnd near-optimal policies of the optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy. The solutions given by the proposed optimistic least-squares-based policy iteration are proved to converge to a small neighborhood of the optimal solution with probability one, under mild conditions. The application of the proposed algorithm to a triple inverted pendulum example validates its feasibility and effectiveness.
BRR: Preserving Privacy of Text Data Efficiently on Device

With the use of personal devices connected to the Internet for tasks such as searches and shopping becoming ubiquitous, ensuring the privacy of the users of such services has become a requirement in order to build and maintain customer trust. While text privatization methods exist, they require the existence of a trusted party that collects user data before applying a privatization method to preserve users' privacy. In this work we propose an efficient mechanism to provide metric differential privacy for text data on-device. With our solution, sensitive data never leaves the device and service providers only have access to privatized data to train models on and analyze. We compare our algorithm to the state-of-the-art for text privatization, showing similar or better utility for the same privacy guarantees, while reducing the storage costs by orders of magnitude, enabling on-device text privatization.
Difference in the Conformation and Dynamics of Aspartic Acid on the Flat Regions, Step Edges, and Kinks of a Calcite Surface: A Molecular Dynamics Study

Molecular dynamics simulations were conducted to investigate the conformation and dynamics of an aspartic acid molecule near the {104} and {110} planes of a CaCO3 calcite crystal, near the acute and obtuse [114] step edges on the {104} plane, and near kinks formed at the step edges. The simulations indicate that the amino acid binds indirectly to the {104} plane, whereas it binds directly to the {110} plane. The simulations also indicate that the acid binds directly to the acute step edge but does not bind to the obtuse step edge. These differences in the binding of the acid to the planes and the step edges were attributed to a difference in the structure of the surrounding water. The simulations also indicate that the acid binds to the kink formed at both the acute and the obtuse step edges, if a Ca2+ ion is placed at the corner of the kink. The binding was much stronger at the kink than at the planes and the step edges.
Quantum nature of molecular vibrational quenching: Water-molecular hydrogen collisions.

Rates of conversions of molecular internal energy to and from kinetic energy by means of molecular collision allow us to compute collisional line shapes and transport properties of gases. Knowledge of ro-vibrational quenching rates is necessary to connect spectral observations to physical properties of warm astrophysical gasses, including exo-atmospheres. For a system of paramount importance in this context, the vibrational bending mode quenching of H2O by H2, we show here that the exchange of vibrational to rotational and kinetic energy remains a quantum process, despite the large numbers of quantum levels involved and the large vibrational energy transfer. The excitation of the quantized rotor of the projectile is by far the most effective ro-vibrational quenching path of water. To do so, we use a fully quantum first-principles computation, potential and dynamics, converging it at all stages, in a full coupled channel formalism. We present here rates for the quenching of the first bending mode of ortho-H2O by ortho-H2, up to 500 K, in a fully converged coupled channel formalism.
Photosystem I P700 chlorophyll a apoprotein A1 as PCR marker to identify diatoms and their associated lineage

The morphological characteristics of diatoms are useful for studying their taxonomy. However, the distinction between closely related diatom taxa can be very difficult, especially when the morphological characters are modified by environmental constraints. In the present study, 13 fresh water diatoms were identified morphologically and cultured under axenic conditions. To check this, PCR primers specific for multilocus genes were designed to amplify and screen 13 fresh water diatom monocultures. Multilocus PCR primers (DRR3, scfcpA, Lhcf11, SIT1, SIT3, SIT4, LOC101218388, COI‐5P, rbcL, rbcL‐3P, LSU D2/D3, UPA, psaA, and 18S rRNA) were tested. It was found that psaA gene, a plant pigment chlorophyll‐based PCR marker, amplified in all the diatoms. Out of 13 diatom amplicons, only two fresh water diatoms DNA were sequenced. This included Cyclotella meneghiniana and Sellaphora pupula. The Sanger sequencing results thus established that morphologically identified diatom, Sellaphora pupula, exhibited close phylogeny to Sellaphora whereas fresh water Cyclotella meneghiniana has close lineage to marine diatom Thallosiosira.
Functional characterization of novel variants in SMPD1 in Indian patients with acid sphingomyelinase deficiency

Pathogenic variations in SMPD1 lead to acid sphingomyelinase deficiency (ASMD), that is, Niemann‐Pick disease (NPD) type A and B (NPA, NPB), which is a recessive lysosomal storage disease. The knowledge of variant spectrum in Indian patients is crucial for early and accurate NPD diagnosis and genetic counseling of families. In this study, we recruited 40 unrelated pediatric patients manifesting symptoms of ASMD and subnormal ASM enzyme activity. Variations in SMPD1 were studied using Sanger sequencing for all exons, followed by interpretation of variants based on American College of Medical Genetics and Genomics & Association for Molecular Pathology (ACMG/AMP) criteria. We identified 18 previously unreported variants and 21 known variants, including missense, nonsense, deletions, duplications, and splice site variations with disease‐causing potential. Eight missense variants were functionally characterized using in silico molecular dynamic simulation and in vitro transient transfection in HEK293T cells, followed by ASM enzyme assay, immunoblot, and immunofluorescence studies. All the variants showed reduced ASM activity in transfected cells confirming their disease‐causing potential. The study provides data for efficient prenatal diagnosis and genetic counseling of families with NPD type A and B.
Return of the Lemnaceae: Duckweed as a model plant system in the genomics and post-genomics era.

The aquatic Lemnaceae family, commonly called duckweed, comprise some of the smallest and fastest growing angiosperms known on Earth. Their tiny size, rapid growth by clonal propagation, and facile uptake of labeled compounds from the media were attractive features that made them a well-known model for plant biology from 1950-1990. Interest in duckweed has steadily regained momentum over the past decade, driven in part by the growing need to identify alternative plants from traditional agricultural crops that can help tackle urgent societal challenges such as climate change and rapid population expansion. Propelled by rapid advances in genomic technologies, recent studies with duckweed again highlight the potential of these small plants to enable discoveries in diverse fields from ecology to chronobiology. Building on established community resources, duckweed is re-emerging as a platform to study plant processes at the systems level and to translate knowledge gained for field deployment to address some of society's pressing needs. This review details the anatomy, development, physiology, and molecular characteristics of the Lemnaceae to introduce them to the broader plant research community. We highlight recent research enabled by Lemnaceae to demonstrate how these plants can be used for quantitative studies of complex processes and for revealing potentially novel strategies in plant defense and genome maintenance.
Increased mortality of tropical tree seedlings during the extreme 2015–16 El Niño

As extreme climate events are predicted to become more frequent because of global climate change, understanding their impacts on natural systems is crucial. Tropical forests are vulnerable to droughts associated with extreme El Niño events. However, little is known about how tropical seedling communities respond to El Niño–related droughts, even though patterns of seedling survival shape future forest structure and diversity. Using long‐term data from eight tropical moist forests spanning a rainfall gradient in central Panama, we show that community‐wide seedling mortality increased by 11% during the extreme 2015–16 El Niño, with mortality increasing most in drought‐sensitive species and in wetter forests. These results indicate that severe El Niño–related droughts influence understory dynamics in tropical forests, with effects varying both within and across sites. Our findings suggest that predicted increases in the frequency of extreme El Niño events will alter tropical plant communities through their effects on early life stages.