Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls

Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied outside their training domain, NN potential predictions can be inaccurate, increasing the need for Uncertainty Quantification (UQ). Bayesian modeling provides the mathematical framework for UQ, but classical Bayesian methods based on Markov chain Monte Carlo (MCMC) are computationally intractable for NN potentials. By training graph NN potentials for coarse-grained systems of liquid water and alanine dipeptide, we demonstrate here that scalable Bayesian UQ via stochastic gradient MCMC (SG-MCMC) yields reliable uncertainty estimates for MD observables. We show that cold posteriors can reduce the required training data size and that for reliable UQ, multiple Markov chains are needed. Additionally, we find that SG-MCMC and the Deep Ensemble method achieve comparable results, despite shorter training and less hyperparameter tuning of the latter. We show that both methods can capture aleatoric and epistemic uncertainty reliably, but not systematic uncertainty, which needs to be minimized by adequate modeling to obtain accurate credible intervals for MD observables. Our results represent a step toward accurate UQ that is of vital importance for trustworthy NN potential-based MD simulations required for decision-making in practice.
Search for dark matter lines at the Galactic Center with 14 years of Fermi data

Dark matter (DM) in the Milky Way halo may annihilate or decay to photons, producing monochromatic gamma rays. We search for DM-induced spectral lines using 14 years of data from the Large Area Telescope onboard the Fermi Gamma-ray Space Telescope ($\textit{Fermi}$-LAT) between $10\,\mathrm{GeV}$ and $2\,\mathrm{TeV}$ in the inner Milky Way leveraging both the spatial and spectral morphology of an expected signal. We present new constraints as strong as $\langle \sigma v \rangle \lesssim 6\times 10^{-30}\, \mathrm{cm}^3/\mathrm{s}$ for the two-to-two annihilations and $\tau \gtrsim 10^{30}\,\mathrm{s}$ for one-to-two decays, representing leading sensitivity between $10\,\mathrm{GeV}$ and $\sim$$500\,\mathrm{GeV}$. We consider the implications of our line-constraints on the Galactic Center Excess (GCE), which is a previously-observed excess of continuum $\sim$GeV gamma-rays that may be explained by DM annihilation. The Higgs portal and neutralino-like DM scenarios, which have been extensively discussed as possible origins of the GCE, are constrained by our work because of the lack of observed one-loop decays to two photons. More generally, we interpret our null results in a variety of annihilating and decaying DM models, such as neutralinos, gravitinos, and glueballs, showing that in many cases the line search is more powerful than the continuum, despite the continuum annihilation being at tree level.
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation

Traditional approaches to extrinsic calibration use fiducial markers and learning-based approaches rely heavily on simulation data. In this work, we present a learning-based markerless extrinsic calibration system that uses a depth camera and does not rely on simulation data. We learn models for end-effector (EE) segmentation, single-frame rotation prediction and keypoint detection, from automatically generated real-world data. We use a transformation trick to get EE pose estimates from rotation predictions and a matching algorithm to get EE pose estimates from keypoint predictions. We further utilize the iterative closest point algorithm, multiple-frames, filtering and outlier detection to increase calibration robustness. Our evaluations with training data from multiple camera poses and test data from previously unseen poses give sub-centimeter and sub-deciradian average calibration and pose estimation errors. We also show that a carefully selected single training pose gives comparable results.
Synthesizing Research on Programmers' Mental Models of Programs, Tasks and Concepts - a Systematic Literature Review

Programmers' mental models represent their knowledge and understanding of programs, programming concepts, and programming in general. They guide programmers' work and influence their task performance. Understanding mental models is important for designing work systems and practices that support programmers. Although the importance of programmers' mental models is widely acknowledged, research on mental models has decreased over the years. The results are scattered and do not take into account recent developments in software engineering. We analyze the state of research into programmers' mental models and provide an overview of existing research. We connect results on mental models from different strands of research to form a more unified knowledge base on the topic. We conducted a systematic literature review on programmers' mental models. We analyzed literature addressing mental models in different contexts, including mental models of programs, programming tasks, and programming concepts. Using nine search engines, we found 3678 articles (excluding duplicates). 84 were selected for further analysis. Using the snowballing technique, we obtained a final result set containing 187 articles. We show that the literature shares a kernel of shared understanding of mental models. By collating and connecting results on mental models from different fields of research, we uncovered some well-researched aspects, which we argue are fundamental characteristics of programmers' mental models. This work provides a basis for future work on mental models. The research field on programmers' mental models still faces many challenges rising from a lack of a shared knowledge base and poorly defined constructs. We created a unified knowledge base on the topic. We also point to directions for future studies. In particular, we call for studies that examine programmers working with modern practices and tools.
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles

Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR and radar are widely used in autonomous vehicles for environmental perception, navigation and assisted braking control. Despite their relative importance in making safer driving decisions, these devices are vulnerable to multiple attack types including spoofing, triggering and false data injection. When these attacks are successful they can compromise the security of autonomous vehicles leading to severe consequences for the driver, nearby vehicles and pedestrians. To handle these attacks and protect the measurement devices, we propose a spatial-temporal anomaly detection model STAnDS which incorporates a residual error spatial detector, with a time-based expected change detection. This approach is evaluated using a simulated quantitative environment and the results show that STAnDS is effective at detecting multiple attack types.
INFINITARY EXPANSIONS: THE MODELLING PROPERTY AND ORDERS ON RAMSEY CLASSES

Given a ﬁrst-order language L and an L -structure M (resp. a class of L -structures C ) we introduce the L ∞ , 0 and the quantiﬁer-free type Morleyisation of M (resp. C ). We show that these inﬁnitary-logic expansions are tame in the sense that they preserve and reﬂect both the Embedding Ramsey Property (ERP) and the Modelling Property (MP). We then turn our attention to Scow’s theorem connecting generalised indiscernibles with Ramsey classes, and show that by passing through inﬁnitary logic, one can obtain a stronger result, which does not require any technical assumptions. We also show that every class with ERP admits a linear order deﬁnable in L ∞ , 0 , eﬀectively proving that any Ramsey class, not necessarily countable, is “essentially” a class of ordered structures. In the ﬁnal sections of the paper, we also discuss a generalisation of ERP to classes that are no longer required to contain only ﬁnite structures, and some relevant weakenings of the ERP.
Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy

Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://github.com/cvg/DeepLSD.
Invariant Lipschitz Bandits: A Side Observation Approach

Symmetry arises in many optimization and decision-making problems, and has attracted considerable attention from the optimization community: By utilizing the existence of such symmetries, the process of searching for optimal solutions can be improved significantly. Despite its success in (offline) optimization, the utilization of symmetries has not been well examined within the online optimization settings, especially in the bandit literature. As such, in this paper we study the invariant Lipschitz bandit setting, a subclass of the Lipschitz bandits where the reward function and the set of arms are preserved under a group of transformations. We introduce an algorithm named \texttt{UniformMesh-N}, which naturally integrates side observations using group orbits into the \texttt{UniformMesh} algorithm (\cite{Kleinberg2005_UniformMesh}), which uniformly discretizes the set of arms. Using the side-observation approach, we prove an improved regret upper bound, which depends on the cardinality of the group, given that the group is finite. We also prove a matching regret's lower bound for the invariant Lipschitz bandit class (up to logarithmic factors). We hope that our work will ignite further investigation of symmetry in bandit theory and sequential decision-making theory in general.
Symmetry for elliptic equations in a half-space without strong maximum principle

For a wide class of nonlinearities f(u) satisfying but not necessarily Lipschitz continuous, we study the quasi-linear equation where T = {x = (x1, x2, …, xN) ∈ RN: x1 > 0} with N ≥ 2. By using a new approach based on the weak maximum principle, we show that any positive solution on T must be a function of x1 only. Under our assumptions, the strong maximum principle does not hold in general and the solution may develop a flat core; our symmetry result allows an easy and precise determination of the flat core.
Correlating Theory and Practice in Finding Clubs and Plexes

Finding large"cliquish"subgraphs is a classic NP-hard graph problem. In this work, we focus on finding maximum $s$-clubs and $s$-plexes, i.e., graphs of diameter $s$ and graphs where each vertex is adjacent to all but $s$ vertices. Preprocessing based on Turing kernelization is a standard tool to tackle these problems, especially on sparse graphs. We provide a new parameterized analysis for the Turing kernelization and demonstrate their usefulness in practice. Moreover, we provide evidence that the new theoretical bounds indeed better explain the observed running times than the existing theoretical running time bounds. To this end, we suggest a general method to compare how well theoretical running time bounds fit to measured running times.
Fully conservative hydraulic jumps and solibores in two-layer Boussinesq fluids

Abstract We consider a special type of hydraulic jumps (internal bores) which, in the vertically bounded system of two immiscible fluids with slightly different densities, conserve not only the mass and impulse but also the circulation and energy. This is possible only at specific combinations of the upstream and downstream states. Two such combinations are identified with arbitrary upstream and downstream interface heights. The first has a cross-symmetry between the interface height and shear on both sides of the jump. This symmetry, which is due to the invariance of the two-layer shallow-water system with swapping the interface height and shear, ensures the automatic conservation of the impulse and energy as well as the continuity of characteristic velocities across the jump. The speed at which such jumps propagate is defined uniquely by the conservation of the mass and circulation. The other possibility is a marginally stable shear flow that can have fully conservative jumps with discontinuous characteristic velocities. Both types of conservative jumps are shown to represent a long-wave approximation to the so-called solibores that appear as smooth permanent-shape solutions in a weakly non-hydrostatic model. A new analytical solution for solibores is obtained and found to agree very well with the previous DNS results for partial-depth lock release flow. The finding that certain large-amplitude hydraulic jumps can be fully conservative, while most are not such even in the inviscid approximation, points towards the wave dispersion as a primary mechanism behind the lossy nature of internal bores.
Hierarchical Codebook Design for Near-Field mmWave MIMO Communications Systems

Communications system with analog or hybrid analog/digital architectures usually relies on a pre-defined codebook to perform beamforming. With the increase in the size of the antenna array, the characteristics of the spherical wavefront in the near-field situation are not negligible. Therefore, it is necessary to design a codebook that is adaptive to near-field scenarios. In this letter, we investigate the hierarchical codebook design method in the near-field situation. We develop a steering beam gain approximation method and design the lower-layer codebook to provide coverage for the Fresnel region. For the upper-layer codebook, we propose beam rotation and beam relocation methods to place an arbitrary beam pattern to a target location. The simulation results show the superiority of the proposed near-field hierarchical codebook design.
Interpolation with the polynomial kernels

The polynomial kernels are widely used in machine learning and they are one of the default choices to develop kernel-based classification and regression models. However, they are rarely used and considered in numerical analysis due to their lack of strict positive definiteness. In particular they do not enjoy the usual property of unisolvency for arbitrary point sets, which is one of the key properties used to build kernel-based interpolation methods. This paper is devoted to establish some initial results for the study of these kernels, and their related interpolation algorithms, in the context of approximation theory. We will first prove necessary and sufficient conditions on point sets which guarantee the existence and uniqueness of an interpolant. We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters. With these spaces at hand, it will be further possible to derive generic error estimates which apply to sufficiently smooth functions, thus escaping the native space. Finally, we will show how to employ an efficient stable algorithm to these kernels to obtain accurate interpolants, and we will test them in some numerical experiment. After this analysis several computational and theoretical aspects remain open, and we will outline possible further research directions in a concluding section. This work builds some bridges between kernel and polynomial interpolation, two topics to which the authors, to different extents, have been introduced under the supervision or through the work of Stefano De Marchi. For this reason, they wish to dedicate this work to him in the occasion of his 60th birthday.
Perception of Persian uvular and velar stop consonants by speakers of American English

Recent cross‐language studies by Werker and Tees [J. Acoust. Soc. Am. 75, 1866–1878 (1984)] describe difficulties in English speakers' perception of a Thompson glottalized uvular‐velar stop contrast. However, the acoustic information that differentiated the contrasting phones was difficult to specify. The present study extends the examination of non‐English place contrasts by investigating native English speakers' ability to perceive a nonglottalized uvular‐velar contrast in Persian (Farsi). Multiple instances of velar and uvular stop consonants produced in several VCV contexts by a native speaker of Farsi were presented. Correct identification of the medial consonant varied systematically as a function of vowel context from very few errors to near change performance. Acoustic analysis revealed systematic F2 and F3 formant transition differences between uvular and velar consonants produced in the same vowel context. Variations in voicing offset (murmur) were also observed in several vowel contexts. These .
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query

We propose a sparse end-to-end multi-person pose regression framework, termed QueryPose, which can directly predict multi-person keypoint sequences from the input image. The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization. However, the dense paradigm introduces complex and redundant post-processes during inference. In our framework, each human instance is encoded by several learnable spatial-aware part-level queries associated with an instance-level query. First, we propose the Spatial Part Embedding Generation Module (SPEGM) that considers the local spatial attention mechanism to generate several spatial-sensitive part embeddings, which contain spatial details and structural information for enhancing the part-level queries. Second, we introduce the Selective Iteration Module (SIM) to adaptively update the sparse part-level queries via the generated spatial-sensitive part embeddings stage-by-stage. Based on the two proposed modules, the part-level queries are able to fully encode the spatial details and structural information for precise keypoint regression. With the bipartite matching, QueryPose avoids the hand-designed post-processes and surpasses the existing dense end-to-end methods with 73.6 AP on MS COCO mini-val set and 72.7 AP on CrowdPose test set. Code is available at https://github.com/buptxyb666/QueryPose.
X-Ray-Induced Thermally-Stimulated Currents in Polyvinylidene Fluoride

Thermally-stimulated currents (TSC) induced by a prior X-irradiation in polyvinylidene fluoride (PVDF) were observed and the nature of carrier traps was discussed. PVDF shows three split TSC peaks, the P1, P2 and P3 peaks in ascending order of temperature. Both P1 and P2 peaks are due to dipole orientation in amorphous regions associated with the γ- and β-relaxations, respectively. The P3 peak is due to carrier traps in the regions related to the α-type PVDF crystallites. The field dependence of the TSC revealed that at least two kinds of carrier traps are responsible for the P3 peak. The dependence of the irradiation temperature on TSC, together with the field dependence, suggests that the carrier release from one kind of carrier trap is strongly affected by the electric field.
The Complexity of the Shapley Value for Regular Path Queries

A path query extracts vertex tuples from a labeled graph, based on the words that are formed by the paths connecting the vertices. We study the computational complexity of measuring the contribution of edges and vertices to an answer to a path query, focusing on the class of conjunctive regular path queries. To measure this contribution, we adopt the traditional Shapley value from cooperative game theory. This value has been recently proposed and studied in the context of relational database queries and has uses in a plethora of other domains. We first study the contribution of edges and show that the exact Shapley value is almost always hard to compute. Specifically, it is #P-hard to calculate the contribution of an edge whenever at least one (non-redundant) conjunct allows for a word of length three or more. In the case of regular path queries (i.e., no conjunction), the problem is tractable if the query has only words of length at most two; hence, this property fully characterizes the tractability of the problem. On the other hand, if we allow for an approximation error, then it is straightforward to obtain an efficient scheme (FPRAS) for an additive approximation. Yet, a multiplicative approximation is harder to obtain. We establish that in the case of conjunctive regular path queries, a multiplicative approximation of the Shapley value of an edge can be computed in polynomial time if and only if all query atoms are finite languages (assuming non-redundancy and conventional complexity limitations). We also study the analogous situation where we wish to determine the contribution of a vertex, rather than an edge, and establish complexity results of similar nature.