Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Data Recasting to Enhance Tabular Reasoning

Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness. In this research, we present a framework for semi-automatically recasting existing tabular data to make use of the benefits of both approaches. We utilize our framework to build tabular NLI instances from five datasets that were initially intended for tasks like table2text creation, tabular Q/A, and semantic parsing. We demonstrate that recasted data could be used as evaluation benchmarks as well as augmentation data to enhance performance on tabular NLI tasks. Furthermore, we investigate the effectiveness of models trained on recasted data in the zero-shot scenario, and analyse trends in performance across different recasted datasets types.
A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.
Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket

Binary neural networks are the extreme case of network quantization, which has long been thought of as a potential edge machine learning solution. However, the significant accuracy gap to the full-precision counterparts restricts their creative potential for mobile applications. In this work, we revisit the potential of binary neural networks and focus on a compelling but unanswered problem: how can a binary neural network achieve the crucial accuracy level (e.g., 80%) on ILSVRC-2012 ImageNet? We achieve this goal by enhancing the optimization process from three complementary perspectives: (1) We design a novel binary architecture BNext based on a comprehensive study of binary architectures and their optimization process. (2) We propose a novel knowledge-distillation technique to alleviate the counter-intuitive overfitting problem observed when attempting to train extremely accurate binary models. (3) We analyze the data augmentation pipeline for binary networks and modernize it with up-to-date techniques from full-precision models. The evaluation results on ImageNet show that BNext, for the first time, pushes the binary model accuracy boundary to 80.57% and significantly outperforms all the existing binary networks. Code and trained models are available at: https://github.com/hpi-xnor/BNext.git.
Representing Embeddability as Set Inclusion

A few steps are made towards a representation theory of embeddability among uncountable graphs. A class of graphs is defined by forbidding some countable configurations which are related to the graph's end‐structure. Using uncountable combinatorics, a representation theorem for embeddability in this class is proved, which asserts the existence of a surjective homomorphism from the relation of embeddability over isomorphism types of regular cardinality λ>ℵ1 onto set inclusion over all subsets of reals or cardinality λ or less. As corollaries we obtain: (1) the complexity of the class in every regular uncountable λ>ℵ1 is at least λ++sup{μℵ0:μ+
Quantum cohomology of the Grassmannian and unitary Dyson Brownian motion

We study a class of commuting Markov kernels whose simplest element describes the movement of $k$ particles on a discrete circle of size $n$ conditioned to not intersect each other. Such Markov kernels are related to the quantum cohomology ring of the Grassmannian, which is an algebraic object counting analytic maps from $\mathbb{P}^1(\mathbb{C})$ to the Grassmannian space of k-dimensional vector subspaces of $\mathbb{C}^n$ with prescribed constraints at some points of $\mathbb{P}^1(\mathbb{C})$. We obtain a Berry-Esseen theorem and a local limit theorem for an arbitrary product of approximately $n^2$ Markov kernels belonging to the above class, when k is fixed. As a byproduct of those results, we derive asymptotic formulas for the quantum cohomology ring of the Grassmannian in terms of the heat kernel on $SU (k)$.
Mixed Signals: Analyzing Software Attribution Challenges in the Android Ecosystem

The ability to identify the author responsible for a given software object is critical for many research studies and for enhancing software transparency and accountability. However, as opposed to other application markets like Apple's iOS App Store, attribution in the Android ecosystem is known to be hard. Prior research has leveraged market metadata and signing certificates to identify software authors without questioning the validity and accuracy of these attribution signals. However, Android application (app) authors can, either intentionally or by mistake, hide their true identity due to: (1) the lack of policy enforcement by markets to ensure the accuracy and correctness of the information disclosed by developers in their market profiles during the app release process, and (2) the use of self-signed certificates for signing apps instead of certificates issued by trusted CAs. In this paper, we perform the first empirical analysis of the availability, volatility and overall aptness of publicly available market and app metadata for author attribution in Android markets. To that end, we analyze a dataset of over 2.5 million market entries and apps extracted from five Android markets for over two years. Our results show that widely used attribution signals are often missing from market profiles and that they change over time. We also invalidate the general belief about the validity of signing certificates for author attribution. For instance, we find that apps from different authors share signing certificates due to the proliferation of app building frameworks and software factories. Finally, we introduce the concept of an attribution graph and we apply it to evaluate the validity of existing attribution signals on the Google Play Store. Our results confirm that the lack of control over publicly available signals can confuse automatic attribution processes.
Program Repair

ed transformation rules. Each transformation (either concrete transformation or abstracted transformation rule) is denoted with pair of red and green boxes. Among them, the root of the tree rule-3 is the most generalized transformation, while the other rules (rule-1, rule-2) represent different levels of abstractions that are generated by generalizing different concrete transformations. For instance, the rule-1 shows an generalization that abstract the method names (remove of ct-1 and render of ct-2) to h1. The rule-2 further generalizes variable c and t of rule-1 and ct-3, respectively. Keeping intermediate rules, instead of producing the most generalized rule by generalizing all concrete transformations, enables us to apply the most suitable rule to transform a given code. Given a set of concrete transformations, a novel clustering algorithm has been proposed by GetaFix [5] to generate a tree of transformation rules. This algorithm maintains a working set S of transformation rules, which is initialized with all the given concrete transformations. Then, the following steps are repeated until the size of S is reduced to one: (1) Choose two transformation rules e1, e2 from S . (2) Generalize e1, e2 using synthesizer and yields e3 = Synthesize(e1, e2). (3) Add e3 to S to S , and remove e1 and e2 from S . (4) Set e3 as the parent of e1 and e2 in the tree and continue the loop. How to pick the pair e1, e2 from the working set to generalize is crucial for the clustering algorithm. To minimize the loss of concrete information at each step, the pairs that are similar to each other are always given high priority to be selected. For instance, for the example shown in Figure 9, the transformations that are applied to c.remove() and c.render() are first picked up because they are similar and share one same identifier (c). Given a tree of learned rules and a buggy source code C need to be transformed, the last step is to find a proper rule to apply and produce a fixed version ofC . In case the over-generalized learned rule produces incorrect results, the most specific rules that can be applied to C are preferred. For instance, the left part of Figure 10 shows an example source code that needs to be patched by the learned transformation rules in Figure 9. All the rule-3, rule-2 and rule-1 are applicable on C . ACM Trans. Softw. Eng. Methodol. , Vol. 1, No. 1, Article . Publication date: November 2022.
Fine-structure transitions of the carbon isoelectronic sequence C, N+, and O2 + induced by collisions with atomic hydrogen

Fine-structure transitions can be involved in various processes including photon absorption, charge transfer, and inelastic collisions between ions, electrons, and neutral atoms. We present fine-structure excitation and relaxation cross-sections for the collisions of the first few members of the carbon isoelectronic sequence (C, N+ and O2 +) with atomic hydrogen calculated using quantum-mechanical methods. For C, the scattering theory and computational approach is verified by comparison with previous calculations. The rate coefficients for the collisional processes are obtained. For N+ and O2 +, the transitions correspond to the lines [O iii] 52 μm, [O iii] 88 μm, [N ii] 122 μm, and [N ii] 205 μm, observed in the far-infrared in the local universe and more recently in high-redshift galaxies using radio interferometry. The influence of different potentials on the cross-sections and rate coefficients are demonstrated.
Gravitational radiation from binary systems in f(R) gravity: A semi-classical approach

The rate of energy loss and orbital period decay of quasi- stable compact binary systems are derived in f(R) theory of gravity using the method of a single vertex graviton emission process from a classical source. After linearising the f(R) action written in an equivalent scalar-tensor format in the Einstein frame, we identify the appropriate interaction terms between the massless spin-2 tensor mode, massive scalar mode, and the energy momentum tensor. The definition of the scalar field is related to the f(R) models. Then using the interaction vertex we compute the rate of energy loss due to spin-2 quadrupole radiation, which comes out to be the same as the Peter-Mathews formula with a multiplication factor, and also the energy loss due to the scalar dipole radiation. The total energy loss is the sum of these two contributions. Our derivation is most general as it is applicable for both arbitrary eccentricity of the binary orbits and arbitrary mass of the scalar field. Using the derived theoretical formula for the period decay of the binary systems, we compare the predictions of f(R) gravity and general relativity for the observations of four binary systems, i.e. Hulse-Taylor Binary, PSR J1141-6545, PSR J1738+0333, and PSR J0348+0432. Thus we put bound on three well-known f(R) dark energy models, namely the Hu-Sawicki, the Starobinsky, and the Tsujikawa model. We get the best constraint on f'(R 0)-1 (where R 0 is the scalar curvature of the Universe at the present epoch) from the Tsujikawa model, i.e |f'(R 0)-1| < 2.09 × 10-4. This bound is stronger than those from most of the astrophysical observations and even some cosmological observations.
SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems

Gradient Boosted Decision Tree (GBDT) is a widely-used machine learning algorithm that has been shown to achieve state-of-the-art results on many standard data science problems. We are interested in its application to multioutput problems when the output is highly multidimensional. Although there are highly effective GBDT implementations, their scalability to such problems is still unsatisfactory. In this paper, we propose novel methods aiming to accelerate the training process of GBDT in the multioutput scenario. The idea behind these methods lies in the approximate computation of a scoring function used to find the best split of decision trees. These methods are implemented in SketchBoost, which itself is integrated into our easily customizable Python-based GPU implementation of GBDT called Py-Boost. Our numerical study demonstrates that SketchBoost speeds up the training process of GBDT by up to over 40 times while achieving comparable or even better performance.
GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents performance from being further improved. Introducing self-attention into convolution can capture global information well, but it will largely encumber the actual speed. In this paper, we propose a hardware-friendly attention mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications. The proposed DFC attention is constructed based on fully-connected layers, which can not only execute fast on common hardware but also capture the dependence between long-range pixels. We further revisit the expressiveness bottleneck in previous GhostNet and propose to enhance expanded features produced by cheap operations with DFC attention, so that a GhostNetV2 block can aggregate local and long-range information simultaneously. Extensive experiments demonstrate the superiority of GhostNetV2 over existing architectures. For example, it achieves 75.3% top-1 accuracy on ImageNet with 167M FLOPs, significantly suppressing GhostNetV1 (74.5%) with a similar computational cost. The source code will be available at https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv2_pytorch and https://gitee.com/mindspore/models/tree/master/research/cv/ghostnetv2.
Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms.
EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data

Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Previous works often use a unified solution like relative positional encoding. However, there exists different kinds of spatial relations, including short-range, medium-range and long-range relations, and modeling them separately can better capture the focus of different tasks on the multi-range relations (e.g., short-range relations can be important in instance segmentation, while long-range relations should be upweighted for semantic segmentation). In this work, we introduce the EurNet for Efficient multi-range relational modeling. EurNet constructs the multi-relational graph, where each type of edge corresponds to short-, medium- or long-range spatial interactions. In the constructed graph, EurNet adopts a novel modeling layer, called gated relational message passing (GRMP), to propagate multi-relational information across the data. GRMP captures multiple relations within the data with little extra computational cost. We study EurNets in two important domains for image and protein structure modeling. Extensive experiments on ImageNet classification, COCO object detection and ADE20K semantic segmentation verify the gains of EurNet over the previous SoTA FocalNet. On the EC and GO protein function prediction benchmarks, EurNet consistently surpasses the previous SoTA GearNet. Our results demonstrate the strength of EurNets on modeling spatial multi-relational data from various domains. The implementations of EurNet for image modeling are available at https://github.com/hirl-team/EurNet-Image . The implementations for other applied domains/tasks will be released soon.
SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Recent years have witnessed remarkable advances in spatiotemporal predictive learning, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. Although impressive, the system complexity of mainstream methods is increasing as well, which may hinder the convenient applications. This paper proposes SimVP, a simple spatiotemporal predictive baseline model that is completely built upon convolutional networks without recurrent architectures and trained by common mean squared error loss in an end-to-end fashion. Without introducing any extra tricks and strategies, SimVP can achieve superior performance on various benchmark datasets. To further improve the performance, we derive variants with the gated spatiotemporal attention translator from SimVP that can achieve better performance. We demonstrate that SimVP has strong generalization and extensibility on real-world datasets through extensive experiments. The significant reduction in training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to benefit the spatiotemporal predictive learning community.
Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets

Satellite altimetry is a unique way for direct observations of sea surface dynamics. This is however limited to the surface-constrained geostrophic component of sea surface velocities. Ageostrophic dynamics are however expected to be significant for horizontal scales below 100~km and time scale below 10~days. The assimilation of ocean general circulation models likely reveals only a fraction of this ageostrophic component. Here, we explore a learning-based scheme to better exploit the synergies between the observed sea surface tracers, especially sea surface height (SSH) and sea surface temperature (SST), to better inform sea surface currents. More specifically, we develop a 4DVarNet scheme which exploits a variational data assimilation formulation with trainable observations and {\em a priori} terms. An Observing System Simulation Experiment (OSSE) in a region of the Gulf Stream suggests that SST-SSH synergies could reveal sea surface velocities for time scales of 2.5-3.0 days and horizontal scales of 0.5$^\circ$-0.7$^\circ$, including a significant fraction of the ageostrophic dynamics ($\approx$ 47\%). The analysis of the contribution of different observation data, namely nadir along-track altimetry, wide-swath SWOT altimetry and SST data, emphasizes the role of SST features for the reconstruction at horizontal spatial scales ranging from \nicefrac{1}{20}$^\circ$ to \nicefrac{1}{4}$^\circ$.
Revenue Comparisons of Auctions with Ambiguity Averse Sellers

We study the revenue comparison problem of auctions when the seller has a maxmin expected utility preference. The seller holds a set of priors around some reference belief, interpreted as an approximating model of the true probability law or the focal point distribution. We develop a methodology for comparing the revenue performances of auctions: the seller prefers auction X to auction Y if their transfer functions satisfy a weak form of the single-crossing condition. Intuitively, this condition means that a bidder's payment is more negatively associated with the competitor's type in X than in Y. Applying this methodology, we show that when the reference belief is independent and identically distributed (IID) and the bidders are ambiguity neutral, (i) the first-price auction outperforms the second-price and all-pay auctions, and (ii) the second-price and all-pay auctions outperform the war of attrition. Our methodology yields results opposite to those of the Linkage Principle.
Extension of a Method of Gilmer

It is a well-known conjecture, sometimes attributed to Frankl, that for any family of sets which is closed under the union operation, there is some element which is contained in at least half of the sets. Gilmer [1] was the ﬁrst to prove a constant bound, showing that there is some element contained in at least 1% of the sets. They state in their paper that the best possible bound achievable by the same method is 3 −√ 5 2 ≈ 38 . 1%. This note achieves that bound by ﬁnding the optimum value, given a binary variable X potentially depending on some other variable S with a given expected value E ( X ) and conditional entropy H ( X | S ) of the conditional entropy of H ( X 1 ∪ X 2 | S 1 , S 2 ) for independent readings X 1 , S 1 and X 2 , S 2 .