Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Feature Screening for Data Streams With Concept Drift

Screening feature selection methods are often used as a preprocessing step for reducing the number of variables before training a model. Traditional screening methods only focus on dealing with complete high dimensional datasets. However, modern datasets not only have higher dimensions and larger sample size, but also have properties such as streaming input, sparsity, and concept drift. Therefore a considerable number of online feature selection methods were introduced to handle these kinds of problems in recent years. Online screening methods are one of the categories of online feature selection methods. The methods that we propose in this paper are capable of handling all three situations mentioned above, in classification settings. Our experiments show that the proposed methods can generate the same feature importance as their offline versions with faster speed and less storage requirements. Furthermore, the results show that online screening methods with integrated model adaptation have a higher true feature detection rate than without model adaptation on data streams exhibiting concept drift. Among the three large real datasets that potentially have concept drift, online screening methods with model adaptation show advantages in either saving computation time and space, reducing model complexity, or improving prediction accuracy.
Zero-Gradient Constrained Optimization for Destriping of 3D Imaging Data

—This paper proposes an effective and efﬁcient de-striping method based on zero-gradient constraints, which are compatible with various regularization functions. Removing stripe noise, i.e., destriping, from three-dimensional (3D) imaging data is an essential task in terms of visual quality and subsequent processing. Stripe noise has ﬂat structures in the vertical and temporal directions, meaning that the vertical and temporal gradients are equal to zero. Exploiting this fact, we ﬁrst propose a new model for characterizing stripe noise. Our model constrains the stripe noise gradient to be zero, which we name the zero-gradient constraint, leading to effective destriping regardless of what regularization is applied to imaging data. Then, we formulate two types of convex optimization problems involving the zero-gradient constraints for destriping and develop efﬁcient solvers for the problems based on a diagonally preconditioned primal-dual splitting algorithm (DP-PDS). We demonstrate the advantages of our model through destriping experiments using hyperspectral images (HSI) and infrared (IR) videos.
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-Of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3x. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5x in memory efficiency improvement) and mitigate OOM problems during GNN inference.
Compatibility of transport effects in non-Hermitian nonreciprocal systems

Based on a general transport theory for non-reciprocal non-Hermitian systems and a topological model that encompasses a wide range of previously studied models, we (i) provide conditions for effects such as reflectionless and transparent transport, lasing, and coherent perfect absorption, (ii) identify which effects are compatible and linked with each other, and (iii) determine by which levers they can be tuned independently. For instance, the directed amplification inherent in the non-Hermitian skin effect does not enter the spectral conditions for reflectionless transport, lasing, or coherent perfect absorption, but allows to adjust the transparency of the system. In addition, in the topological model the conditions for reflectionless transport depend on the topological phase, but those for coherent perfect absorption do not. This then allows us to establish a number of distinct transport signatures of non-Hermitian, nonreciprocal, and topological behaviour, in particular (I) reflectionless transport in a direction that depends on the topological phase, (II) invisibility coinciding with the skin-effect phase transition of topological edge states, and (III) coherent perfect absorption in a system that is transparent when probed from one side.
The Dictionary for Double Holography and Graviton Masses in d Dimensions

Doubly-holographic models, also known as Karch-Randall brane worlds, have shown to be very useful for understanding recent developments around computing entropies in semi-classical gravity coupled to conformal matter. Although there cannot be a faithful bulk/brane dictionary, we show that there is a simple dictionary which relates brane fields to subleading coefficients of a near-brane expansion of bulk fields -- similar to the case of AdS/CFT. We use this dictionary to find a general formula for the leading order contribution to graviton masses in the $d$ dimensional Karch-Randall braneworld.
Ramified class field theory and duality over finite fields

We prove a duality theorem for the p-adic étale motivic cohomology of a variety U which is the complement of a divisor on a smooth projective variety over Fp. This extends the duality theorems of Milne and Jannsen-Saito-Zhao. The duality introduces a filtration on H ét(U,Q/Z). We identify this filtration to the classically known Matsuda filtration when the reduced part of the divisor is smooth. We prove a reciprocity theorem for the idele class groups with modulus introduced by Kerz-Zhao and Rülling-Saito. As an application, we derive the failure of Nisnevich descent for Chow groups with modulus.
Leveraging the ALMA Atacama Compact Array for Cometary Science: An Interferometric Survey of Comet C/2015 ER61 (PanSTARRS) and Evidence for a Distributed Source of Carbon Monosulfide

We report the first survey of molecular emission from cometary volatiles using standalone Atacama Compact Array (ACA) observations from the Atacama Large Millimeter/Submillimeter Array (ALMA) toward comet C/2015 ER61 (PanSTARRS) carried out on UT 2017 April 11 and 15, shortly after its April 4 outburst. These measurements of HCN, CS, CH3OH, H2CO, and HNC (along with continuum emission from dust) probed the inner coma of C/2015 ER61, revealing asymmetric outgassing and discerning parent from daughter/distributed source species. This work presents spectrally integrated flux maps, autocorrelation spectra, production rates, and parent scale lengths for each molecule and a stringent upper limit for CO. HCN is consistent with direct nucleus release in C/2015 ER61, whereas CS, H2CO, HNC, and potentially CH3OH are associated with distributed sources in the coma. Adopting a Haser model, parent scale lengths determined for H2CO (L p ∼ 2200 km) and HNC (L p ∼ 3300 km) are consistent with previous work in comets, whereas significant extended source production (L p ∼ 2000 km) is indicated for CS, suggesting production from an unknown parent in the coma. The continuum presents a point-source distribution with a flux density implying an excessively large nucleus, inconsistent with other estimates of the nucleus size. It is best explained by the thermal emission of slowly moving outburst ejectas, with total mass 5–8 × 1010 kg. These results demonstrate the power of the ACA for revealing the abundances, spatial distributions, and locations of molecular production for volatiles in moderately bright comets such as C/2015 ER61.
Localization of Autonomous Vehicles: Proof of Concept for A Computer Vision Approach

This paper introduces a visual-based localization method for autonomous vehicles (AVs) that operate in the absence of any complicated hardware system but a single camera. Visual localization refers to techniques that aim to find the location of an object based on visual information of its surrounding area. The problem of localization has been of interest for many years. However, visual localization is a relatively new subject in the literature of transportation. Moreover, the inevitable application of this type of localization in the context of autonomous vehicles demands special attention from the transportation community to this problem. This study proposes a two-step localization method that requires a database of geotagged images and a camera mounted on a vehicle that can take pictures while the car is moving. The first step which is image retrieval uses SIFT local feature descriptor to find an initial location for the vehicle using image matching. The next step is to utilize the Kalman filter to estimate a more accurate location for the vehicle as it is moving. All stages of the introduced method are implemented as a complete system using different Python libraries. The proposed system is tested on the KITTI dataset and has shown an average accuracy of 2 meters in finding the final location of the vehicle.
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation

The promotion of construction robots can solve the problem of human resource shortage and improve the quality of decoration. To help the construction robots obtain environmental information, we need to use 3D point cloud, which is widely used in robotics, autonomous driving, and so on. With a good understanding of environmental information, construction robots can work better. However, the dynamic changes of 3D point cloud data may bring difficulties for construction robots to understand environmental information, such as when construction robots renovate houses. The paper proposes a semantic segmentation method for point cloud based on meta-learning. The method includes a basic learning module and a meta-learning module. The basic learning module is responsible for learning data features and evaluating the model, while the meta-learning module is responsible for updating the parameters of the model and improving the model generalization ability. In our work, we pioneered the method of producing datasets for meta-learning in 3D scenes, as well as demonstrated that the Model-Agnostic Meta-Learning (MAML) algorithm can be applied to process 3D point cloud data. At the same time, experiments show that our method can allow the model to be quickly applied to new environments with a few samples. Our method has important applications.
Blade Effective Wind Speed Estimation: A Subspace Predictive Repetitive Estimator Approach

Modern wind turbine control algorithms typically utilize rotor effective wind speed measured from an anemometer on the turbine’s nacelle. Unfortunately, the measured wind speed from such a single measurement point does not give a good representation of the effective wind speed over the blades, as it does not take the varying wind condition within the entire rotor area into account. As such, Blade Effective Wind Speed (BEWS) estimation can be seen as a more accurate alternative. This paper introduces a novel Subspace Predictive Repetitive Estimator (SPRE) approach to estimate the BEWS using blade load measurements. In detail, the azimuth-dependent cone coefficient is firstly formulated to describe the mapping between the out-of-plane blade root bending moment and the wind speed over blades. Then, the SPRE scheme, which is inspired by Subspace Predictive Repetitive Control (SPRC), is proposed to estimate the BEWS. Case studies exhibit the proposed method’s effectiveness at predicting BEWS and identifying wind shear in varying wind speed conditions. Moreover, this novel technique enables complicated wind inflow conditions, where a rotor is impinged and overlapped by wake shed from an upstream turbine, to be estimated.
Hierarchical compressed sensing

Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this work, we provide an overview over the theory of compressed sensing for a particularly rich family of such signals, namely those of hierarchically structured signals. Examples of such signals are constituted by blocked vectors, with only few non-vanishing sparse blocks. We present recovery algorithms based on efficient hierarchical hard-thresholding. The algorithms are guaranteed to converge, in a stable fashion both with respect to measurement noise as well as to model mismatches, to the correct solution provided the measurement map acts isometrically restricted to the signal class. We then provide a series of results establishing the required condition for large classes of measurement ensembles. Building upon this machinery, we sketch practical applications of this framework in machine-type communications and quantum tomography. Jens Eisert Dahlem Center for Complex Quantum Systems and Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. e-mail: jense@physics.fu-berlin.de Axel Flinth Institute for Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. email: flinth@chalmers.se Benedikt Groß Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany, e-mail: benedikt.gross@fu-berlin.de Ingo Roth Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE. Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Berlin, Germany. e-mail: i.roth@fu-berlin.de Gerhard Wunder Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. e-mail: g.wunder@fu-berlin.de 1 ar X iv :2 10 4. 02 72 1v 2 [ cs . I T ] 8 D ec 2 02 1 2 J. Eisert, A. Flinth, B. Groß, I. Roth, G. Wunder
Parameterized Complexity of Elimination Distance to First-Order Logic Properties

The elimination distance to some target graph property ${\mathcal{P}}$ is a general graph modification parameter introduced by Bulian and Dawar. We initiate the study of elimination distances to graph properties expressible in first-order logic. We delimit the problem’s fixed-parameter tractability by identifying sufficient and necessary conditions on the structure of prefixes of first-order logic formulas. Our main result is the following meta-theorem: For every graph property ${\mathcal{P}}$ expressible by a first order-logic formula φ ∈ Σ3, that is, of the form\begin{equation*}\varphi = \exists {x_1}\exists {x_2} \cdots \exists {x_r}\forall {y_1}\forall {y_2} \cdots \forall {y_s}\quad \exists {z_1}\exists {z_2} \cdots \exists {z_t}\,\psi ,\end{equation*}where ψ is a quantifier-free first-order formula, checking whether the elimination distance of a graph to ${\mathcal{P}}$ does not exceed k, is fixed-parameter tractable parameterized by k. Properties of graphs expressible by formulas from Σ3 include being of bounded degree, excluding a forbidden subgraph, or containing a bounded dominating set. We complement this theorem by showing that such a general statement does not hold for formulas with even slightly more expressive prefix structure: There are formulas φ ∈ Π3, for which computing elimination distance is W[2]-hard.
3D Beam Training in Terahertz Communication: A Quadruple-UPA Architecture

In this paper, we propose a quadruple-UPA architecture for realizing the beamforming with wide coverage in terahertz (THz) communication. Considering the severe path loss suffered in the THz wave propagation, the pilot signals of conventional channel estimation strategies may not be effectively detected. In sight of this, we propose a fast 3D beam training strategy to jointly realize the angle estimation and the beamforming. Specifically, we first develop a novel hierarchical codebook that predefines some codewords (including narrow beams and wide beams) stage by stage. The narrow beams are designed as the beamforming solution candidates, whereas the wide beams are designed for reducing the training complexity. Then, we propose a 3D beam training procedure to find the optimal beamforming solution, i.e., the optimal narrow-beam pair, in a fast manner. Numerical results are presented to validate the effectiveness of the proposed scheme.
Isotropic x-ray bound on primordial black hole dark matter

We revisit the constraints on evaporating primordial black holes (PBHs) from the isotropic X-ray and soft gamma-ray background in the mass range $10^{16}-10^{18}$ g. We find that they are stronger than usually inferred due to two neglected effects: i) The contribution of the annihilation radiation due to positrons emitted in the evaporation process. ii) The high-latitude, Galactic contribution to the measured isotropic flux. We study the dependence of the bounds from the datasets used, the positron annihilation conditions, and the inclusion of the astrophysical background. We push the exclusion limit for non-spinning PBH with monochromatic mass function as the totality of dark matter to 1.5$\times 10^{17}\,$g, which represents a $\sim$15$\%$ improvement with respect to earlier bounds and translates into almost an order of magnitude improvement in the PBH fraction in the already probed region. We also show that the inclusion of spin and/or an extended, log-normal mass function lead to tighter bounds. Our study suggests that the isotropic flux is an extremely promising target for future missions in improving the sensitivity to PBHs as candidates for dark matter.
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics

Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only simulate rigid body physics. Additionally, the simulation process usually does not provide gradients that might be useful for planning and control optimizations. We introduce a new differentiable physics benchmark called PasticineLab, which includes a diverse collection of soft body manipulation tasks. In each task, the agent uses manipulators to deform the plasticine into the desired configuration. The underlying physics engine supports differentiable elastic and plastic deformation using the DiffTaichi system, posing many under-explored challenges to robotic agents. We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark. Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning. We expect that PlasticineLab will encourage the development of novel algorithms that combine differentiable physics and RL for more complex physics-based skill learning tasks.
Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation

In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional approaches utilize frame-wise matching criteria to extract shared information between co-occurring audio and video. Thus, their performance heavily depends on the accuracy of audio-visual synchronization and the effectiveness of their representations. To overcome the frame discontinuity problem between two modalities due to transmission delay mismatch or jitter, we propose a cross-modal affinity network (CaffNet) that learns global correspondence as well as locally-varying affinities between audio and visual streams. Given that the global term provides stability over a temporal sequence at the utterance-level, this resolves the label permutation problem characterized by inconsistent assignments. By extending the proposed cross-modal affinity on the complex network, we further improve the separation performance in the complex spectral domain. Experimental results verify that the proposed methods outperform conventional ones on various datasets, demonstrating their advantages in real-world scenarios.
A Unified Model for Fingerprint Authentication and Presentation Attack Detection

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this paper, we reformulate the workings of a typical fingerprint recognition system. In particular, we posit that both spoof detection and fingerprint recognition are correlated tasks. Therefore, rather than performing the two tasks separately, we propose a joint model for spoof detection and matching1 to simultaneously perform both tasks without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain an authentication accuracy (1:1 matching) of TAR = 100% @ FAR = 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LiveDet 2015 dataset, both maintaining the performance of stand-alone methods. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50% and 40%, respectively; a significant advantage for recognition systems running on resource-constrained devices and communication channels.
Characterization and ammonia adsorption of biochar prepared from distillers' grains anaerobic digestion residue with different pyrolysis temperatures

BACKGROUND Increasing attention is being paid to find an alternative way to handle biogas residue from biogas production which is important for sustainable development of biogas project. In this study, biochars from distillers’ grains anaerobic digestion residue (BRC) in the pyrolysis temperature range of 300 to 800 °C were prepared. Subsequently, elemental analysis, thermal stability, specific surface area (SSA), scanning electron microscope (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and the performance of ammonia (NH4+) adsorption were studied. RESULTS BRC produced at 700 °C exhibited the highest SSA (251.47 m2 g−1). Generally, the pH (6.2-8.4), ash (17.58-36.93%), carbon (48.09-56.18%) and the NH4+ adsorption capacity (1.95-5.86 mg N g−1) of BRC increased with pyrolysis temperature increasing (300-700 °C). However, for pyrolysis yield (76.39-40.44%), volatile matter (48.72-4.69%), phenolic (0.85-0.01 mmol g−1), total acidity (1.39-0.21 mmol g−1) and the contribution of H (4.92-1.13%), O (29.41-5.76%), O/C (0.61-0.10), H/C (0.10-0.02), (O + N)/C (0.65-0.13), an opposite trend was found. CONCLUSIONS Pyrolysis temperature has a significant effect on the physicochemical properties of BRC and thus on their quantity of NH4+ adsorbed. The BRC, especially produced at 700 °C, could serve as a highly efficient material for NH4+ removal, therefore present a high value-added application for biogas residues.