Miracle-dz/UniFAR-FTUs huggingface dataset:
SCL(FOL) Revisited

This paper presents an up-to-date and refined version of the SCL calculus for first-order logic without equality. The refinement mainly consists of the following two parts: First, we incorporate a stronger notion of regularity into SCL(FOL). Our regularity definition is adapted from the SCL(T) calculus. This adapted definition guarantees non-redundant clause learning during a run of SCL. However, in contrast to the original presentation, it does not require exhaustive propagation. Second, we introduce trail and model bounding to achieve termination guarantees. In previous versions, no termination guarantees about SCL were achieved. Last, we give rigorous proofs for soundness, completeness and clause learning guarantees of SCL(FOL) and put SCL(FOL) into context of existing first-order calculi.
Optimal Weighted-Delay Scheduling in $2\times 2$ Input-Queued Switches

Motivated by few delay-optimal scheduling results, in comparison to results on throughput optimality, we investigate a canonical input-queued switch scheduling problem in which the objective is to minimize the discounted delay cost over an infinite time horizon. We derive an optimal scheduling policy and establish corresponding theoretical properties, which are expected to be of interest more broadly than input-queued switches. Computational experiments demonstrate and quantify the benefits of our optimal scheduling policy over alternative policies such as variants of MaxWeight scheduling, well-known to be throughput optimal and more recently shown to be delay optimal in the heavy-traffic regime limit.
An adaptive weighted self-representation method for incomplete multi-view clustering

For multi-view learning problems coming from reality, it is common that the data are incomplete due to lost files, poor data entry and so on. Thus, we consider the incomplete multi-view clustering problem. In the clustering process, the influence of each view of one object may be different and the data are often low rank. On the other hand, self-representation subspace method works well on multi-view clustering problem. Taking these factors into account, we propose an adaptive weighted self-representation subspace clustering (AWSR) method for solving the incomplete multi-view clustering problem. The AWSR method employs a weight matrix to measure the contribution of each missing view, and seeks for the low rank coefficient matrix to enhance the clustering effect. Moreover, the regularization terms are properly designed to eliminate the possible data noise and keep the convexity property of the objective function. The convergence analysis is provided for the alternate minimization algorithm of the AWSR model. Meanwhile, numerical experiments of AWSR method are implemented on five real datasets. The numerical performance shows that the AWSR method is effective and delivers superior results when compared to other six common approaches considering the clustering accuracy, normalized mutual information and purity.
Deep Graph-Level Orthogonal Hypersphere Compression for Anomaly Detection

Graph-level anomaly detection aims to identify anomalous graphs from a collection of graphs in an unsupervised manner. A common assumption of anomaly detection is that a reasonable decision boundary has a hypersphere shape, but may appear some non-conforming phenomena in high dimensions. Towards this end, we firstly propose a novel deep graph-level anomaly detection model, which learns the graph representation with maximum mutual information between substructure and global structure features while exploring a hypersphere anomaly decision boundary. The idea is to ensure the training data distribution consistent with the decision hypersphere via an orthogonal projection layer. Moreover, we further perform the bi-hypersphere compression to emphasize the discrimination of anomalous graphs from normal graphs. Note that our method is not confined to graph data and is applicable to anomaly detection of other data such as images. The numerical and visualization results on benchmark datasets demonstrate the effectiveness and superiority of our methods in comparison to many baselines and state-of-the-arts.
A Class of 5D Inhomogeneous Models with a Cosmological Constant

In this work we would like to address the problem of the effect of bulk matter on the brane cosmological evolution in a general way. We assume that the spatial part of the brane metric is not maximally symmetric, therefore spatially inhomogeneous. However we retain the conformal flatness property of the standard cosmological model (FRW) i.e. the Weyl tensor of the induced 4D geometry is zero. We refer to it as Spatially Inhomogeneous Irrotational (SII) brane. It is shown that the model can be regarded as the 5D generalization of the SII spacetimes found recently [1].
Hybrid simulation of energetic particle effects on tearing modes in tokamak plasmas

The effects of energetic ions on stability of tearing mode are investigated by global kinetic/MHD hybrid simulations in a low beta tokamak plasma. The kinetic effects of counter circulating energetic ions from the non-adiabatic response are found to be strongly destabilizing while the effects from the adiabatic response are stabilizing. The net effect with both adiabatic and non-adiabatic contributions is destabilizing. On the other hand, the kinetic effects of co-circulating energetic ions from the non-adiabatic response are calculated to be weakly stabilizing while the corresponding adiabatic contribution is destabilizing for small energetic ion beta. The net effect is weakly stabilizing. The dependence of kinetic effects on energetic ion beta, gyroradius, and speed is studied systematically and the results agree in large part with the previous analytic results for the kinetic effects of circulating particles. For trapped energetic ions, their effects on tearing mode stability are dominated by the adiab.
Nonlocal-interaction vortices

We consider sequences of quadratic non-local functionals, depending on a small parameter $\e$, that approximate the Dirichlet integral by a well-known result by Bourgain, Brezis and Mironescu. Similarly to what is done for hard-core approximations to vortex energies in the case of the Dirichlet integral, we further scale such energies by $|\log\e|^{-1}$ and restrict them to $S^1$-valued functions. We introduce a notion of convergence of functions to integral currents with respect to which such energies are equi-coercive, and show the converge to a vortex energy, similarly to the limit behaviour of Ginzburg-Landau energies at the vortex scaling.
A Formal Geometric Blow-up Method for Pattern Forming Systems

We extend and apply a recently developed approach to the study of dynamic bifurcations in PDEs based on the geometric blow-up method. We show that this approach, which has so far only been applied to study a dynamic Turing bifurcation in a cubic Swift-Hohenberg equation, can be coupled with a fast-slow extension of the method of multiple scales. This leads to a formal but systematic method, which can be viewed as a fast-slow generalisation of the formal part of classical modulation theory. We demonstrate the utility and versatility of this method by using it to derive modulation equations, i.e. simpler closed form equations which govern the dynamics of the formal approximations near the underlying bifurcation point, in the context of model equations with dynamic bifurcations of (i) Turing, (ii) Hopf, (iii) Turing-Hopf, and (iv) stationary long-wave type. The modulation equations have a familiar form: They are of real Ginzburg-Landau (GL), complex GL, coupled complex GL and Cahn-Hilliard type respectively. In contrast to the modulation equations derived in classical modulation theory, however, they have time-dependent coefficients induced by the slow parameter drift, they depend on spatial and temporal scales which scale in a dependent and non-trivial way, and the geometry of the space in which they are posed is non-trivial due to the blow-up transformation. The formal derivation of the modulation equations provides the first steps toward the rigorous treatment of these challenging problems, which remains for future work.
Machine Learning Assisted Bad Data Detection for High-Throughput Substation Communication

Electrical substations are becoming more prone to cyber-attacks due to increasing digitalization. Prevailing defence measures based on cyber rules are often inadequate to detect attacks that use legitimate-looking measurements. In this work, we design and implement a bad data detection solution for electrical substations called ResiGate, that effectively combines a physics-based approach and a machine-learning-based approach to provide substantial speed-up in high-throughput substation communication scenarios, while still maintaining high detection accuracy and confidence. While many existing physics-based schemes are designed for deployment in control centers (due to their high computational requirement), ResiGate is designed as a security appliance that can be deployed on low-cost industrial computers at the edge of the smart grid so that it can detect local substation-level attacks in a timely manner. A key challenge for this is to continuously run the computationally demanding physics-based analysis to monitor the measurement data frequently transmitted in a typical substation. To provide high throughput without sacrificing accuracy, ResiGate uses machine learning to effectively filter out most of the non-suspicious (normal) data and thereby reducing the overall computational load, allowing efficient performance even with a high volume of network traffic. We implement ResiGate on a low-cost industrial computer and our experiments confirm that ResiGate can detect attacks with zero error while sustaining a high throughput.
Explicit3D: Graph Network with Spatial Inference \\for Single Image 3D Object Detection

Indoor 3D object detection is an essential task in single image scene understanding, impacting spatial cognition fundamentally in visual reasoning. Existing works on 3D object detection from a single image either pursue this goal through independent predictions of each object or implicitly reason over all possible objects, failing to harness relational geometric information between objects. To address this problem, we propose a dynamic sparse graph pipeline named Explicit3D based on object geometry and semantics features. Taking the efficiency into consideration, we further define a relatedness score and design a novel dynamic pruning algorithm followed by a cluster sampling method for sparse scene graph generation and updating. Furthermore, our Explicit3D introduces homogeneous matrices and defines new relative loss and corner loss to model the spatial difference between target pairs explicitly. Instead of using ground-truth labels as direct supervision, our relative and corner loss are derived from the homogeneous transformation, which renders the model to learn the geometric consistency between objects. The experimental results on the SUN RGB-D dataset demonstrate that our Explicit3D achieves better performance balance than the-state-of-the-art.
E-unification for Second-Order Abstract Syntax

Higher-order unification (HOU) concerns unification of (extensions of) $\lambda$-calculus and can be seen as an instance of equational unification ($E$-unification) modulo $\beta\eta$-equivalence of $\lambda$-terms. We study equational unification of terms in languages with arbitrary variable binding constructions modulo arbitrary second-order equational theories. Abstract syntax with general variable binding and parametrised metavariables allows us to work with arbitrary binders without committing to $\lambda$-calculus or use inconvenient and error-prone term encodings, leading to a more flexible framework. In this paper, we introduce $E$-unification for second-order abstract syntax and describe a unification procedure for such problems, merging ideas from both full HOU and general $E$-unification. We prove that the procedure is sound and complete.
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching

Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predicts overlapping areas to promote effective and clean global context aggregation, with the key insight that humans focus on the overlapping areas instead of the entire images after multiple observations when matching keypoints in image pairs. Technically, we first perform global information integration across all keypoints to imitate the humans behavior of observing the entire images at the beginning of feature matching. Then, we propose Overlapping Areas Prediction Module (OAPM) to capture the keypoints in co-visible regions and conduct feature enhancement among them to simulate that humans transit the focus regions from the entire images to overlapping regions, hence realizeing effective information exchange without the interference coming from the keypoints in non overlapping areas. Besides, since humans tend to leverage probability to determine whether the match labels are correct or not, we propose a Match Labels Weight Strategy (MLWS) to generate the coefficients used to appraise the reliability of the ground-truth match labels, while alleviating the influence of measurement noise coming from the data. Moreover, we integrate depth-wise convolution into Tranformer encoder layers to ensure OAMatcher extracts local and global feature representation concurrently. Comprehensive experiments demonstrate that OAMatcher outperforms the state-of-the-art methods on several benchmarks, while exhibiting excellent robustness to extreme appearance variants. The source code is available at https://github.com/DK-HU/OAMatcher.
Escaping saddle points in zeroth-order optimization: the power of two-point estimators

Two-point zeroth order methods are important in many applications of zeroth-order optimization, such as robotics, wind farms, power systems, online optimization, and adversarial robustness to black-box attacks in deep neural networks, where the problem may be high-dimensional and/or time-varying. Most problems in these applications are nonconvex and contain saddle points. While existing works have shown that zeroth-order methods utilizing $\Omega(d)$ function valuations per iteration (with $d$ denoting the problem dimension) can escape saddle points efficiently, it remains an open question if zeroth-order methods based on two-point estimators can escape saddle points. In this paper, we show that by adding an appropriate isotropic perturbation at each iteration, a zeroth-order algorithm based on $2m$ (for any $1 \leq m \leq d$) function evaluations per iteration can not only find $\epsilon$-second order stationary points polynomially fast, but do so using only $\tilde{O}\left(\frac{d}{m\epsilon^{2}\bar{\psi}}\right)$ function evaluations, where $\bar{\psi} \geq \tilde{\Omega}\left(\sqrt{\epsilon}\right)$ is a parameter capturing the extent to which the function of interest exhibits the strict saddle property.
CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction

Multi-scale features are essential for dense prediction tasks, such as object detection, instance segmentation, and semantic segmentation. The prevailing methods usually utilize a classification backbone to extract multi-scale features and then fuse these features using a lightweight module (e.g., the fusion module in FPN and BiFPN, two typical object detection methods). However, as these methods allocate most computational resources to the classification backbone, the multi-scale feature fusion in these methods is delayed, which may lead to inadequate feature fusion. While some methods perform feature fusion from early stages, they either fail to fully leverage high-level features to guide low-level feature learning or have complex structures, resulting in sub-optimal performance. We propose a streamlined cascade encoder-decoder network, dubbed CEDNet, tailored for dense \mbox{prediction} tasks. All stages in CEDNet share the same encoder-decoder structure and perform multi-scale feature fusion within the decoder. A hallmark of CEDNet is its ability to incorporate high-level features from early stages to guide low-level feature learning in subsequent stages, thereby enhancing the effectiveness of multi-scale feature fusion. We explored three well-known encoder-decoder structures: Hourglass, UNet, and FPN. When integrated into CEDNet, they performed much better than traditional methods that use a pre-designed classification backbone combined with a lightweight fusion module. Extensive experiments on object detection, instance segmentation, and semantic segmentation demonstrated the effectiveness of our method. The code is available at https://github.com/zhanggang001/CEDNet.
PRAGTHOS: Practical Game Theoretically Secure Proof-of-Work Blockchain

Security analysis of blockchain technology is an active domain of research. There has been both cryptographic and game-theoretic security analysis of Proof-of-Work (PoW) blockchains. Prominent work includes the cryptographic security analysis under the Universal Composable framework and Game-theoretic security analysis using Rational Protocol Design. These security analysis models rely on stricter assumptions that might not hold. In this paper, we analyze the security of PoW blockchain protocols. We first show how assumptions made by previous models need not be valid in reality, which attackers can exploit to launch attacks that these models fail to capture. These include Difficulty Alternating Attack, under which forking is possible for an adversary with less than 0.5 mining power, Quick-Fork Attack, a general bound on selfish mining attack and transaction withholding attack. Following this, we argue why previous models for security analysis fail to capture these attacks and propose a more practical framework for security analysis pRPD. We then propose a framework to build PoW blockchains PRAGTHOS, which is secure from the attacks mentioned above. Finally, we argue that PoW blockchains complying with the PRAGTHOS framework are secure against a computationally bounded adversary under certain conditions on the reward scheme.
De-confounding causal inference using latent multiple-mediator pathways

Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors to improve the goodness-of-fit of the model to observed data, and deconfound the mediators and outcome simultaneously. One major advantage of the proposed framework is that it utilizes the causal pathway structure from cause to outcome via multiple mediators to debias the causal effect without requiring external information on latent confounders. In addition, the proposed framework is flexible in terms of integrating powerful nonparametric prediction algorithms while retaining interpretable mediation effects. In theory, we establish the identification of both causal and mediation effects based on the proposed deconfounding method. Numerical experiments on both simulation settings and a normative aging study indicate that the proposed approach reduces the estimation bias of both causal and mediation effects.
The Synchronization Power (Consensus Number) of Access-Control Objects: The Case of AllowList and DenyList

This article studies the synchronization power of AllowList and DenyList objects under the lens provided by Herlihy's consensus hierarchy. It specifies AllowList and DenyList as distributed objects and shows that while they can both be seen as specializations of a more general object type, they inherently have different synchronization properties. While the AllowList object does not require synchronization between participating processes, a DenyList object requires processes to reach consensus on a specific set of processes. These results are then applied to the analysis of anonymity-preserving systems that use AllowList and DenyList objects. First, a blind-signature-based e-voting is presented. Then DenyList and AllowList objects are used to determine the consensus number of a specific decentralized key management system. Finally, an anonymous money transfer protocol using the association of AllowList and DenyList objects is studied.
Hysteresis phenomena and multipulse formation of a dissipative system in a passively mode-locked fiber laser

A model describing the dissipative soliton evolution in a passively mode-locked fiber laser is proposed by using the nonlinear polarization rotation technique and the spectral filtering effect. It is numerically found that the laser alternately evolves on the stable and unstable mode-locking states as a function of the pump strength. Numerical simulations show that the passively mode-locked fiber lasers with large net normal dispersion can operate on multiple pulse behavior and hysteresis phenomena. The experimental observations confirm the theoretical predictions. The theoretical and experimental results achieved are qualitatively distinct from those observed in net-anomalous-dispersion conventional-soliton fiber lasers.
Tunable Electron Transport in Defect-Engineered PdSe2

Tuning the ambipolar behavior in charge carrier transport via defect-engineering is crucial for achieving high mobility transistors for nonlinear logic circuits. Here, we present the electric-field tunable electron and hole transport in a microchannel device consisting of highly air-stable van der Waals (vdW) noble metal dichalcogenide (NMDC), PdSe$_\mathrm{2}$, as an active layer. Pristine bulk PdSe$_\mathrm{2}$ constitutes Se surface vacancy defects created during the growth or exfoliation process and offers an ambipolar transfer characteristics with a slight electron dominance recorded in field-effect transistor (FET) characteristics showing an ON/OFF ratio<10 and electron mobility ~ 21 cm$^2$/V.s. However, transfer characteristics of PdSe$_\mathrm{2}$ can be tuned to a hole-dominated transport while using hydrochloric acid (HCl) as a $p$-type dopant. On the other hand, the chelating agent EDTA, being a strong electron donor, enhances the electron-dominance in PdSe$_\mathrm{2}$ channel. In addition, $p$-type behavior with a 100 times higher ON/OFF ratio is obtained while cooling the sample down to 10 K. Low-temperature angle-resolved photoemission spectroscopy resembles the $p$-type band structure of PdSe$_\mathrm{2}$ single crystal. Also, first principle density functional theory calculations justify the tunability observed in PdSe$_\mathrm{2}$ as a result of defect-engineering. Such a defect-sensitive ambipolar vdW architecture may open up new possibilities towards future CMOS (Complementary Metal-Oxide-Semiconductor) device fabrications and high performance integrated circuits.