Miracle-dz/UniFAR-FTUs huggingface dataset:
An energy-efficient GMRES-Multigrid solver for space-time finite element computation of dynamic poro- and thermoelasticity

We present families of space-time finite element methods (STFEMs) for a coupled hyperbolic-parabolic system of poro- or thermoelasticity. Well-posedness of the discrete problems is proved. Higher order approximations inheriting most of the rich structure of solutions to the continuous problem on computationally feasible grids are naturally embedded. However, the block structure and solution of the algebraic systems become increasingly complex for these members of the families. We present and analyze a robust geometric multigrid (GMG) preconditioner for GMRES iterations. The GMG method uses a local Vanka-type smoother. Its action is defined in an exact mathematical way. Due to nonlocal coupling mechanisms of unknowns, the smoother is applied on patches of elements. This ensures the damping of error frequencies. In a sequence of numerical experiments, including a challenging three-dimensional benchmark of practical interest, the efficiency of the solver for STFEMs is illustrated and confirmed. Its parallel scalability is analyzed. Beyond this study of classical performance engineering, the solver's energy efficiency is investigated as an additional and emerging dimension in the design and tuning of algorithms and their implementation on the hardware.
A Logistics Provider's Profit Maximization Facility Location Problem with Random Utility Maximizing Followers

We introduce a strategic decision-making problem faced by logistics providers (LPs) seeking facility location decisions that lead to profitable operations. The profitability depends on the revenue generated through agreements with shippers, and the costs arising when satisfying those agreements. The latter depend in turn on service levels and on characteristics of the shippers' customers. However, at a strategic level, LP has imperfect information thereof. We propose a stochastic bilevel formulation where a given LP (leader) anticipates the decisions of shippers (followers) arising from a random utility maximization model. Using a sample average approximation and properties of the associated optimal solutions, we introduce a non-conventional single-level mixed integer linear programming formulation that can be solved by a general-purpose solver. We can quickly identify situations that lead to zero expected profit for the LP. Experimental results show that optimal expected profit is highly dependent on shippers' price sensitivity. Underestimating it can lead to an overestimation of expected profits.
FaceChat: An Emotion-Aware Face-to-face Dialogue Framework

While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the overall user experience. We present FaceChat, a web-based dialogue framework that enables emotionally-sensitive and face-to-face conversations. By seamlessly integrating cutting-edge technologies in natural language processing, computer vision, and speech processing, FaceChat delivers a highly immersive and engaging user experience. FaceChat framework has a wide range of potential applications, including counseling, emotional support, and personalized customer service. The system is designed to be simple and flexible as a platform for future researchers to advance the field of multimodal dialogue systems. The code is publicly available at https://github.com/qywu/FaceChat.
Neural Partial Differential Equations with Functional Convolution

We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs. Our key idea is to leverage the prior of ``translational similarity'' of numerical PDE differential operators to drastically reduce the scale of learning model and training data. We implemented three central network components, including a neural functional convolution operator, a Picard forward iterative procedure, and an adjoint backward gradient calculator. Our novel paradigm fully leverages the multifaceted priors that stem from the sparse and smooth nature of the physical PDE solution manifold and the various mature numerical techniques such as adjoint solver, linearization, and iterative procedure to accelerate the computation. We demonstrate the efficacy of our method by robustly discovering the model and accurately predicting the solutions of various types of PDEs with small-scale networks and training sets. We highlight that all the PDE examples we showed were trained with up to 8 data samples and within 325 network parameters.
Active Visual Exploration Based on Attention-Map Entropy

Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment. To tackle this problem, we introduce a new technique called Attention-Map Entropy (AME). It leverages the internal uncertainty of the transformer-based model to determine the most informative observations. In contrast to existing solutions, it does not require additional loss components, which simplifies the training. Through experiments, which also mimic retina-like sensors, we show that such simplified training significantly improves the performance of reconstruction, segmentation and classification on publicly available datasets.
Probing the Origin of Changing-look Quasar Transitions with Chandra

Extremely variable quasars can also show strong changes in broad-line emission strength and are known as changing-look quasars (CLQs). To study the CLQ transition mechanism, we present a pilot sample of CLQs with X-ray observations in both the bright and faint states. From a sample of quasars with bright-state archival SDSS spectra and (Chandra or XMM-Newton) X-ray data, we identified five new CLQs via optical spectroscopic follow-up and then obtained new target-of-opportunity X-ray observations with Chandra. No strong absorption is detected in either the bright- or the faint-state X-ray spectra. The intrinsic X-ray flux generally changes along with the optical variability, and the X-ray power-law slope becomes harder in the faint state. Large-amplitude mid-infrared variability is detected in all five CLQs, and it echoes the variability in the optical with a time lag expected from the light-crossing time of the dusty torus for CLQs with robust lag measurements. The changing-obscuration model is not consistent with the observed X-ray spectra and spectral energy distribution changes seen in these CLQs. It is highly likely that the observed changes are due to the changing accretion rate of the supermassive black hole, so the multiwavelength emission varies accordingly, with promising analogies to the accretion states of X-ray binaries.
Assessing the performance of spatial cross-validation approaches for models of spatially structured data

Evaluating models fit to data with internal spatial structure requires specific cross-validation (CV) approaches, because randomly selecting assessment data may produce assessment sets that are not truly independent of data used to train the model. Many spatial CV methodologies have been proposed to address this by forcing models to extrapolate spatially when predicting the assessment set. However, to date there exists little guidance on which methods yield the most accurate estimates of model performance. We conducted simulations to compare model performance estimates produced by five common CV methods fit to spatially structured data. We found spatial CV approaches generally improved upon resubstitution and V-fold CV estimates, particularly when approaches which combined assessment sets of spatially conjunct observations with spatial exclusion buffers. To facilitate use of these techniques, we introduce the `spatialsample` package which provides tooling for performing spatial CV as part of the broader tidymodels modeling framework.
An efficient model algorithm for two-dimensional field-effect transistors

Two-dimensional materials-based field-effect transistors (2DM-FETs) exhibit both ambipolar and unipolar transport types. To physically and compactly cover both cases, we put forward a quasi-Fermi-level phase space (QFLPS) approach to model the ambipolar effect in our previous work. This work aims to further improve the QFLPS model's numerical aspect so that the model can be implanted into the standard circuit simulator. We first rigorously derive the integral-free formula for the drain-source current to achieve this goal. It is more friendly to computation than the integral form. Besides, it explicitly gives the correlation terms between the electron and hole components. Secondly, to work out the boundary values required by the new expressions, we develop a fast evaluation algorithm for the surface electrostatic potential based on the zero-temperature limit property of the 2DM-FET system. By calibrating the model with the realistic device data of black phosphorus (BP) and monolayer molybdenum disulfide (ML-MoS2) FETs, the completed algorithm is tested against practical cases. The results show a typical superiority to the benchmark algorithm by two orders of magnitude in time consumption can be achieved while keeping a high accuracy with 7 to 9 significant digits.
VMCDL: Vulnerability Mining Based on Cascaded Deep Learning Under Source Control Flow

With the rapid development of the computer industry and computer software, the risk of software vulnerabilities being exploited has greatly increased. However, there are still many shortcomings in the existing mining techniques for leakage source research, such as high false alarm rate, coarse-grained detection, and dependence on expert experience. In this paper, we mainly use the c/c++ source code data of the SARD dataset, process the source code of CWE476, CWE469, CWE516 and CWE570 vulnerability types, test the Joern vulnerability scanning function of the cutting-edge tool, and propose a new cascading deep learning model VMCDL based on source code control flow to effectively detect vulnerabilities. First, this paper uses joern to locate and extract sensitive functions and statements to form a sensitive statement library of vulnerable code. Then, the CFG flow vulnerability code snippets are generated by bidirectional breadth-first traversal, and then vectorized by Doc2vec. Finally, the cascade deep learning model based on source code control flow is used for classification to obtain the classification results. In the experimental evaluation, we give the test results of Joern on specific vulnerabilities, and give the confusion matrix and label data of the binary classification results of the model algorithm on single vulnerability type source code, and compare and verify the five indicators of FPR, FNR, ACC, P and F1, respectively reaching 10.30%, 5.20%, 92.50%,85.10% and 85.40%,which shows that it can effectively reduce the false alarm rate of static analysis.
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback

We study the adversarial online learning problem and create a completely online algorithmic framework that has data dependent regret guarantees in both full expert feedback and bandit feedback settings. We study the expected performance of our algorithm against general comparators, which makes it applicable for a wide variety of problem scenarios. Our algorithm works from a universal prediction perspective and the performance measure used is the expected regret against arbitrary comparator sequences, which is the difference between our losses and a competing loss sequence. The competition class can be designed to include fixed arm selections, switching bandits, contextual bandits, periodic bandits or any other competition of interest. The sequences in the competition class are generally determined by the specific application at hand and should be designed accordingly. Our algorithm neither uses nor needs any preliminary information about the loss sequences and is completely online. Its performance bounds are data dependent, where any affine transform of the losses has no effect on the normalized regret.
Necessity Feature Correspondence Estimation for Large-scale Global Place Recognition and Relocalization

Global place recognition and 3D relocalization are one of the most important components in the loop closing detection for 3D LiDAR Simultaneous Localization and Mapping (SLAM). In order to find the accurate global 6-DoF transform by feature matching approach, various end-to-end architectures have been proposed. However, existing methods do not consider the false correspondence of the features, thereby unnecessary features are also involved in global place recognition and relocalization. In this paper, we introduce a robust correspondence estimation method by removing unnecessary features and highlighting necessary features simultaneously. To focus on the necessary features and ignore the unnecessary ones, we use the geometric correlation between two scenes represented in the 3D LiDAR point clouds. We introduce the correspondence auxiliary loss that finds key correlations based on the point align algorithm and enables end-to-end training of the proposed networks with robust correspondence estimation. Since the ground with many plane patches acts as an outlier during correspondence estimation, we also propose a preprocessing step to consider negative correspondence by removing dominant plane patches. The evaluation results on the dynamic urban driving dataset, show that our proposed method can improve the performances of both global place recognition and relocalization tasks. We show that estimating the robust feature correspondence is one of the important factors in place recognition and relocalization.
Sparsity by Redundancy: Solving L 1 with SGD

We propose to minimize a generic diﬀerentiable loss function with L 1 penalty with a redundant reparametrization and straightforward stochastic gradient descent. Our proposal is the direct generalization of a series of previous ideas that the L 1 penalty may be equivalent to a diﬀerentiable reparametrization with weight decay. We prove that the proposed method, spred , is an exact solver of L 1 and that the reparametrization trick is completely “benign” for a generic nonconvex function. Practically, we demonstrate the usefulness of the method in (1) training sparse neural networks to perform gene selection tasks, which involves ﬁnding relevant features in a very high dimensional space, and (2) neural network compression task, to which previous attempts at applying the L 1 -penalty have been unsuccessful. Con-ceptually, our result bridges the gap between the sparsity in deep learning and conventional statistical learning.
Capillary–gravity waves of solitary type on deep water

On physical grounds it was recently suggested that limiting capillary–gravity waves of solitary type may exist on the surface of deep water (Longuet-Higgins 1988). This paper describes accurate numerical calculations which support the conjecture. The limiting wave has a phase speed c = 0.9267 (gτ)¼. It is one of a family of solitary waves having speeds c [les ] 1.30 (gτ)¼. The maximum angle of inclination αmax of the free surface is a monotonically decreasing function of the speed c. Physical arguments suggest that αmax has a positive lower bound.
Ins-ATP: Deep Estimation of ATP for Organoid Based on High Throughput Microscopic Images

Adenosine triphosphate (ATP) is a high-energy phosphate compound and the most direct energy source in organisms. ATP is an essential biomarker for evaluating cell viability in biology. Researchers often use ATP bioluminescence to measure the ATP of organoid after drug to evaluate the drug efficacy. However, ATP bioluminescence has some limitations, leading to unreliable drug screening results. Performing ATP bioluminescence causes cell lysis of organoids, so it is impossible to observe organoids' long-term viability changes after medication continually. To overcome the disadvantages of ATP bioluminescence, we propose Ins-ATP, a non-invasive strategy, the first organoid ATP estimation model based on the high-throughput microscopic image. Ins-ATP directly estimates the ATP of organoids from high-throughput microscopic images, so that it does not influence the drug reactions of organoids. Therefore, the ATP change of organoids can be observed for a long time to obtain more stable results. Experimental results show that the ATP estimation by Ins-ATP is in good agreement with those determined by ATP bioluminescence. Specifically, the predictions of Ins-ATP are consistent with the results measured by ATP bioluminescence in the efficacy evaluation experiments of different drugs.
Diffraction in nanoparticle lattices increases sensitivity of localized surface plasmon resonance to refractive index changes

Abstract. Comparing predicted and measured spectra from isolated and ordered nanoparticles (NPs) indicates that ordering NPs into lattices can blueshift the localized surface plasmon resonant (LSPR) spectral feature and increase its wavelength sensitivity to local changes in refractive index. This occurs at lattice constants at or above the resonant wavelength. Numerical analysis indicates its results from effects of diffractive modes on LSPR features that are distinct from Fano resonances, which arise separately due to coupling between diffractive modes and localized plasmons. Refractive index sensitivity of the aggregate LSPR peak from NPs in a square lattice (314 nm RIU−1) was 5.8-fold higher than a comparable peak from random NPs (54 nm RIU−1). Measured sensitivities of Fano resonance features in two ordered samples were 127% and 312%, respectively, of the highest LSPR sensitivity from a random assembly of NPs.
The flux integral for axisymmetric perturbations of static space-times

The axisymmetric perturbations of static space-times with prevailing sources (a Maxwell field or a perfect fluid) are considered; and it is shown how a flux integral can be derived directly from the relevant linearized equations. The flux integral ensures the conservation of energy in the attendant scattering of radiation and the sometimes accompanying transformation of one kind of radiation into another. The flux integral derived for perturbed Einstein-Maxwell space-times will be particularly useful in this latter context (as in the scattering of radiation by two extreme Reissner-Nordström black-holes) and in the setting up of a scattering matrix. And the flux integral derived for a space-time with a perfect-fluid source will be directly applicable to the problem of the non-radial oscillations of a star with accompanying emission of gravitational radiation and enable its reformulation as a problem in scattering theory.
Molecular property prediction by semantic-invariant contrastive learning

Abstract Motivation Contrastive learning has been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, existing methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. Results To address this problem, in this article, we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models. Availability and implementation The code is publicly available at https://github.com/ZiqiaoZhang/FraSICL.
Evolution of many-body systems under ancilla quantum measurements

Measurement-induced phase transitions are the subject of intense current research, both from an experimental and a theoretical perspective. We explore the concept of implementing quantum measurements by coupling a many-body lattice system to an ancillary degree of freedom (implemented using two additional sites), on which projective measurements are performed. We analyze the effect of repeated (``stroboscopic'') measurements on the dynamical correlations of interacting hard-core bosons in a one-dimensional chain. An important distinctive ingredient of the protocol is the fact that the detector ancillas are not re-initialized after each measurement step. The detector thus maintains memory of the accumulated influence by the measured correlated system. Initially, we consider a model in which the ancilla is coupled to a single lattice site. This setup allows obtaining information about the system through Rabi oscillations in the ancillary degrees of freedom, modulated by the ancilla-system interaction. The statistics of quantum trajectories exhibits a ``quantum-Zeno-valve effect'' that occurs when the measurement becomes strong, with sharp branching between low and high entanglement. We proceed by extending numerical simulations to the case of two ancillas and, then, to measurements on all sites. With this realistic measurement apparatus, we find evidence of a disentangling-entangling measurement-induced transition as was previously observed in more abstract models. The dynamics features a broad distribution of the entanglement entropy.
Are consumers ready to pay extra for crowd-shipping e-groceries and why? A hybrid choice analysis for developing economies

This paper presents the behavioral study's results on willingness-to-pay the extra money by the customers for e-groceries deliveries based on crowd-shipping. The proposed methodology was tested for Ukraine, i.e., a developing country where the crowd-shipping services are under development conditions. To account for the behavior complexity of the consumers who have not faced the crowd-shipping services in the past, the choice model was enhanced with a latent variable. The findings indicate the revealed readiness of the e-shoppers to pay extra money for crowd-shipping delivery if it provides more flexible and consumer-oriented service. The expected environmental impact of the crowd-shipping delivery was not considered as important by the e-shoppers, which is explained by low concerns about the environment and car-oriented mobility in the considered case study.