Miracle-dz/UniFAR-FTUs huggingface dataset:
A Platform-Free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.
Distributed Scaled Proximal ADMM Algorithms for Cooperative Localization in WSNs

Distributed cooperative localization in wireless networks is a challenging problem since it typically requires solving a large-scale nonconvex and nonsmooth optimization problem. In this article, we reformulate the classic cooperative localization problem as a smooth and constrained nonconvex minimization problem while its loss function is separable over nodes. By utilizing the structure of the reformulation, we propose two novel scaled proximal alternating direction method of multipliers (SP-ADMM) algorithms, which can be implemented in a distributed manner. Compared with the classic semi-definite programming relaxation techniques, the proposed algorithms can provide more accurate position estimates with significantly lower computation complexity. The associated theoretical analysis shows that our algorithms globally converge to a KKT point of the reformulated problem and a critical point of the original problem, with a favorable sublinear $\boldsymbol{\mathcal{O}}\left(\mathbf{1}/\boldsymbol{T}\right)$ convergence rate, where $\boldsymbol{T}$ is the iteration counter. Numerical experiments have consistently shown that the proposed SP-ADMM algorithms are superior to state-of-the-art methods in terms of localization accuracy and computational complexity across all tested scenarios, varying network size, number of anchors, average number of neighbors, and noise variance levels.
Wasserstein Task Embedding for Measuring Task Similarities

Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most current approaches to measuring task similarities are architecture-dependent: 1) relying on pre-trained models, or 2) training networks on tasks and using forward transfer as a proxy for task similarity. In this paper, we leverage the optimal transport theory and define a novel task embedding for supervised classification that is model-agnostic, training-free, and capable of handling (partially) disjoint label sets. In short, given a dataset with ground-truth labels, we perform a label embedding through multi-dimensional scaling and concatenate dataset samples with their corresponding label embeddings. Then, we define the distance between two datasets as the 2-Wasserstein distance between their updated samples. Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks. We show that the proposed embedding leads to a significantly faster comparison of tasks compared to related approaches like the Optimal Transport Dataset Distance (OTDD). Furthermore, we demonstrate the effectiveness of our proposed embedding through various numerical experiments and show statistically significant correlations between our proposed distance and the forward and backward transfer between tasks.
Consistency and interpretation of the LHC dijet excesses

ATLAS observed a limit for {the cross section of di-jets resonances, which is weaker than expected for a} mass slightly below $\approx$1\TeV. In addition, CMS reported hints for the (non-resonant) pair production of di-jet resonances $X$ via a particle $Y$ at a very similar mass range with a local (global) significance of 3.6\,$\sigma$ (2.5\,$\sigma$) at $m_X\approx950\,$GeV. In this article we show that using the preferred range for $m_X$ from the ATLAS analysis, one can reinterpret the CMS analysis of di-di-jets in terms of a resonant search with $Y\to XX$, with a significantly reduced look-elsewhere effect, finding an excess for $m_Y\! \approx\! 3.6$\TeV with a significance of $4.0\,\sigma$ ($3.2\,\sigma$) locally (globally). We present two possible UV completions capable of explaining the (di-)di-jet excesses, one containing two scalar di-quarks, the other one involving heavy gluons based on an $SU(3)_1\! \times\! SU(3)_2\! \times\! SU(3)_3$ gauge symmetry, spontaneously broken to $SU(3)$ color. In the latter case, non-perturbative couplings are required, pointing towards a composite or extra-dimensional framework. In fact, using 5D-AdS space-time, one obtains the correct mass ratio for $m_X/m_Y$, assuming the $X$ is the lowest lying resonance, and predicts a third (di-)di-jet resonance with a mass around $\approx2.2$\TeV.
Hiding canonicalisation in tensor computer algebra

Simplification of expressions in computer algebra systems often involves a step known as"canonicalisation", which reduces equivalent expressions to the same form. However, such forms may not be natural from the perspective of a pen-and-paper computation, or may be unwieldy, or both. This is, for example, the case for expressions involving tensor multi-term symmetries. We propose an alternative strategy to handle such tensor expressions, which hides canonical forms from the user entirely, and present an implementation of this idea in the Cadabra computer algebra system.
On the prompt contribution to the atmospheric neutrino flux

The prompt contribution to the atmospheric neutrino ﬂux is analyzed. It is demonstrated that the corresponding theoretical uncertainties related to perturbative treatment of charm production, notably, the ones stemming from the low and high x behavior of parton distribution functions, can be conveniently studied at the level of charm quark production. Additionally, we discuss the non-perturbative contribution to the prompt neutrino ﬂux, related to the intrinsic charm content of the proton, and analyze its main features.
Dissipative phase transitions in optomechanical systems

We show that optomechanical quantum systems can undergo dissipative phase transitions within the limit of small nonlinear interaction and strong external drive. In such a defined thermodynamical limit, the nonlinear interaction stabilizes optomechanical dynamics in strong and ultrastrong coupling regimes. As a consequence optomechanical systems possess a rich phase diagram consisting of periodic orbits, discontinuous, and continuous dissipative phase transitions with and without bifurcation. We also find a critical point where continuous and discontinuous dissipative phase transition lines meet. Our analysis demonstrates that optomechanical systems are valuable for understanding the rich physics of dissipative phase transitions and ultrastrong coupling regimes.
Revisit on thermodynamics of BTZ black hole with variable Newton constant

The thermodynamics of the BTZ black holes are revisited with variable Newton constant. A new pair of conjugated variables, the central charge C and the chemical potential µ , is introduced as thermodynamic variables. The ﬁrst law of thermodynamics and the Euler relation, instead of the Smarr relation in the extended phase space formalism, are matched perfectly in this formalism. Compatible with standard extensive thermodynamics, the black hole mass is veriﬁed to be a ﬁrst order homogeneous function of the related extensive variables, and restores the role of internal energy. In addition, the heat capacity has also resulted in a ﬁrst order homogeneous function in this formalism as we expected, and an asymptotic behavior in high temperature limit is shown intriguingly. The non-negatively of heat capacity indicates that the rotating and charged BTZ black holes are thermodynamically stable.
Combating Mode Collapse in GANs via Manifold Entropy Estimation

Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years. However, mode collapse remains a critical problem in GANs. In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs. Different from existing methods, we propose to generalize the discriminator as feature embedding and maximize the entropy of distributions in the embedding space learned by the discriminator. Specifically, two regularization terms, i.e., Deep Local Linear Embedding (DLLE) and Deep Isometric feature Mapping (DIsoMap), are designed to encourage the discriminator to learn the structural information embedded in the data, such that the embedding space learned by the discriminator can be well-formed. Based on the well-learned embedding space supported by the discriminator, a non-parametric entropy estimator is designed to efficiently maximize the entropy of embedding vectors, playing as an approximation of maximizing the entropy of the generated distribution. By improving the discriminator and maximizing the distance of the most similar samples in the embedding space, our pipeline effectively reduces the mode collapse without sacrificing the quality of generated samples. Extensive experimental results show the effectiveness of our method, which outperforms the GAN baseline, MaF-GAN on CelebA (9.13 vs. 12.43 in FID) and surpasses the recent state-of-the-art energy-based model on the ANIME-FACE dataset (2.80 vs. 2.26 in Inception score). The code is available at https://github.com/HaozheLiu-ST/MEE
Quantum Gravitational Decoherence in the 3 Neutrino Flavor Scheme

In many theories of quantum gravity quantum fluctuations of spacetime may serve as an environment for decoherence. Here we study quantum-gravitational decoherence of high energy astrophysical neutrinos in the presence of fermionic dark sectors and for a realistic three neutrino scenario. We show how violation of global symmetries expected to arise in quantum gravitational interactions provides a possibility to pin down the number of dark matter fermions in the universe. Furthermore, we predict the expected total neutrino flux and flavor ratios at experiments depending on the flavor composition at the source.
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning

In our recent dietary assessment field studies on passive dietary monitoring in Ghana, we have collected over 250k in-the-wild images. The dataset is an ongoing effort to facilitate accurate measurement of individual food and nutrient intake in low and middle income countries with passive monitoring camera technologies. The current dataset involves 20 households (74 subjects) from both the rural and urban regions of Ghana, and two different types of wearable cameras were used in the studies. Once initiated, wearable cameras continuously capture subjects' activities, which yield massive amounts of data to be cleaned and annotated before analysis is conducted. To ease the data post-processing and annotation tasks, we propose a novel self-supervised learning framework to cluster the large volume of egocentric images into separate events. Each event consists of a sequence of temporally continuous and contextually similar images. By clustering images into separate events, annotators and dietitians can examine and analyze the data more efficiently and facilitate the subsequent dietary assessment processes. Validated on a held-out test set with ground truth labels, the proposed framework outperforms baselines in terms of clustering quality and classification accuracy.
Influence of Stochastic Dependence on Network Constraints Screening for Unit Commitment

The deepening penetration of renewable energy is challenging how power system operators cope with the associated variability and uncertainty in the unit commitment problem. Given its computational complexity, several optimization-based methods have been proposed to lighten the full unit commitment formulation by removing redundant line flow constraints. These approaches often ignore the spatial couplings of multi-side renewable generation and demand. To address this pitfall, we rule out redundant constraints over a tightened linear programming relaxation of the original unit commitment feasibility region by adding a constraint that efficiently models the correlation of residual demand variations. We set forth a novel, tractable and robust polyhedral uncertainty envelope induced by a given set of scenarios to characterize the tightening constraint. We propose a data-driven umbrella constraint discovery problem formulation that substantially increase the network constraints filtration in unit commitment. Numerical tests are performed on standard IEEE test networks to substantiate the effectiveness of the approach.
Everything You Always Wanted to Know About Quantum Circuits

The development of circuits for quantum computing has been motivated by the proliferation of quantum algorithms which promise up to a superpolynomial factor speedup over classical equivalents. The quantum algorithms developed have the potential to impact fields such as number theory, encryption, scientific computation [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]. The design of quantum algorithms remains an active area of research and new algorithms continue to appear in the literature (see [17] for a representative listing of quantum algorithms). In order to realize the potential performance gains of these proposed quantum algorithms, they must be implemented on quantum hardware. The quantum computers developed by entities such as IBM or Honeywell are examples of quantum hardware platforms which could be used to implement quantum algorithms [18] [19] [20] [21] [22]. To implement quantum algorithms on these hardware platforms, we require quantum datapath systems which are composed of quantum circuits. In this work we shall introduce the design and resource cost assessment of quantum circuits. These quantum circuits are composed of quantum gate networks. Quantum machines developed from entities such as IBM and Honeywell support gate based quantum computation. Gate based quantum circuit design has applications in fault tolerant quantum computation and in quantum circuit design automation [23] [24] [25] [26] [27] [28] [16] [29]. Each quantum gate represents a quantum mechanical operation. As a result, the designer working with quantum circuits shall have to contend with novel properties and challenges. For instance, quantum circuits are one-to-one and all information is preserved. The design of quantum circuits for the implementation of quantum algorithms has caught the attention of researchers. Circuits for elementary functions such as basic arithmetic functions (such as addition or division) have been proposed such as [30] [31][32] [33] [34] [35] [36] [37]. These basic circuits are used as building blocks for more complex datapath systems such as higher level mathematical functions for applications in scientific computing, image processing or machine learning [38] [39] [40] [12] [41] [42] [43].
Quo Vadis: Hybrid Machine Learning Meta-Model Based on Contextual and Behavioral Malware Representations

We propose a hybrid machine learning architecture that simultaneously employs multiple deep learning models analyzing contextual and behavioral characteristics of Windows portable executable, producing a final prediction based on a decision from the meta-model. The detection heuristic in contemporary machine learning Windows malware classifiers is typically based on the static properties of the sample since dynamic analysis through virtualization is challenging for vast quantities of samples. To surpass this limitation, we employ a Windows kernel emulation that allows the acquisition of behavioral patterns across large corpora with minimal temporal and computational costs. We partner with a security vendor for a collection of more than 100k int-the-wild samples that resemble the contemporary threat landscape, containing raw PE files and filepaths of applications at the moment of execution. The acquired dataset is at least ten folds larger than reported in related works on behavioral malware analysis. Files in the training dataset are labeled by a professional threat intelligence team, utilizing manual and automated reverse engineering tools. We estimate the hybrid classifier's operational utility by collecting an out-of-sample test set three months later from the acquisition of the training set. We report an improved detection rate, above the capabilities of the current state-of-the-art model, especially under low false-positive requirements. Additionally, we uncover a meta-model's ability to identify malicious activity in both validation and test sets even if none of the individual models express enough confidence to mark the sample as malevolent. We conclude that the meta-model can learn patterns typical to malicious samples out of representation combinations produced by different analysis techniques. Furthermore, we publicly release pre-trained models and anonymized dataset of emulation reports.
Seamless Tracking of Group Targets and Ungrouped Targets Using Belief Propagation

This paper considers the problem of tracking a large-scale number of group targets. Usually, multi-target in most tracking scenarios are assumed to have independent motion and are well-separated. However, for group target tracking (GTT), the targets within groups are closely spaced and move in a coordinated manner, the groups can split or merge, and the numbers of targets in groups may be large, which lead to more challenging data association, filtering and computation problems. Within the belief propagation (BP) framework, we propose a scalable group target belief propagation (GTBP) method by jointly inferring target existence variables, group structure, data association and target states. The method can efficiently calculate the approximations of the marginal posterior distributions of these variables by performing belief propagation on the devised factor graph. As a consequence, GTBP is capable of capturing the changes in group structure, e.g., group splitting and merging. Furthermore, we model the evolution of targets as the co-action of the group or single-target motions specified by the possible group structures and corresponding probabilities. This flexible modeling enables seamless and simultaneous tracking of multiple group targets and ungrouped targets. Particularly, GTBP has excellent scalability and low computational complexity. It not only maintains the same scalability as BP, i.e., scaling linearly in the number of sensor measurements and quadratically in the number of targets, but also only scales linearly in the number of preserved group partitions. Finally, numerical experiments are presented to demonstrate the effectiveness and scalability of the proposed GTBP method.
Finding out Reasons for Low Completion in MOOC Environment: An Explicable Approach Using Hybrid Data Mining Methods

Because of the worldwide rapid development of MOOC, academic researches and industrial applications of MOOC have become a branch of the major concerns in modern education and information technology fields. This paper focuses on low completion phenomenon in MOOC environment and proposes an explicable approach to find out hidden reasons convincingly. Different from existing works, this approach utilizes data mining methods to make quantitative analysis. It employs learners clustering basing on their study features at first, aiming at discovering inactive learners automatically. These learners are representative of low completion in course study on MOOC platform. Their study behaviors and interactions with website are analyzed with association rules mining in order to explore potential patterns and rules. The extracted rules are used to find out and explain the reasons for low completion in MOOC environment. The experimental result on practical XuetangX platform reveals several strong rules with high support, confidence and lift, which can be regarded as evidence and reference for further explanations of reasons for low completion in MOOC environment.
Ab initio
 construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials

While several studies confirmed that machine-learned potentials (MLPs) can provide accurate free energies for determining phase stabilities, the abilities of MLPs for efficiently constructing a full phase diagram of multi-component systems are yet to be established. In this work, by employing neural network interatomic potentials (NNPs), we demonstrate construction of the MgO-CaO eutectic phase diagram with temperatures up to 3400 K, which includes liquid phases. The NNP is trained over trajectories of various solid and liquid phases at several compositions that are calculated within the density functional theory (DFT). For the exchange-correlation energy among electrons, we compare the PBE and SCAN functionals. The phase boundaries such as solidus, solvus, and liquidus are determined by free-energy calculations based on the thermodynamic integration or semigrand ensemble methods, and salient features in the phase diagram such as solubility limit and eutectic points are well reproduced. In particular, the phase diagram produced by the SCAN-NNP closely follows the experimental data, exhibiting both eutectic composition and temperature within the measurements. On a rough estimate, the whole procedure is more than 1,000 times faster than pure-DFT based approaches. We believe that this work paves the way to fully $\textit{ab initio}$ calculation of phase diagrams.