Miracle-dz/UniFAR-FTUs huggingface dataset:
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment

Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing methods generate aligning entity representation by mining the relevance of triple elements via embedding-based methods, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA -- Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above issues considering ensemble triple specificity and entity role features. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic space and type space, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, our framework uses triple-ware entity enhancement to model the role diversity of triple elements. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework outperforms state-of-the-art methods.
Randomized Reversible Gate-Based Obfuscation for Secured Compilation of Quantum Circuit

The success of quantum circuits in providing reliable outcomes for a given problem depends on the gate count and depth in near-term noisy quantum computers. Quantum circuit compilers that decompose high-level gates to native gates of the hardware and optimize the circuit play a key role in quantum computing. However, the quality and time complexity of the optimization process can vary significantly especially for practically relevant large-scale quantum circuits. As a result, third-party (often less-trusted/untrusted) compilers have emerged, claiming to provide better and faster optimization of complex quantum circuits than so-called trusted compilers. However, untrusted compilers can pose severe security risks, such as the theft of sensitive intellectual property (IP) embedded within the quantum circuit. We propose an obfuscation technique for quantum circuits using randomized reversible gates to protect them from such attacks during compilation. The idea is to insert a small random circuit into the original circuit and send it to the untrusted compiler. Since the circuit function is corrupted, the adversary may get incorrect IP. However, the user may also get incorrect output post-compilation. To circumvent this issue, we concatenate the inverse of the random circuit in the compiled circuit to recover the original functionality. We demonstrate the practicality of our method by conducting exhaustive experiments on a set of benchmark circuits and measuring the quality of obfuscation by calculating the Total Variation Distance (TVD) metric. Our method achieves TVD of up to 1.92 and performs at least 2X better than a previously reported obfuscation method. We also propose a novel adversarial reverse engineering (RE) approach and show that the proposed obfuscation is resilient against RE attacks. The proposed technique introduces minimal degradation in fidelity (~1% to ~3% on average).
Spectral clustering in the Gaussian mixture block model

Gaussian mixture block models are distributions over graphs that strive to model modern networks: to generate a graph from such a model, we associate each vertex $i$ with a latent feature vector $u_i \in \mathbb{R}^d$ sampled from a mixture of Gaussians, and we add edge $(i,j)$ if and only if the feature vectors are sufficiently similar, in that $\langle u_i,u_j \rangle \ge \tau$ for a pre-specified threshold $\tau$. The different components of the Gaussian mixture represent the fact that there may be different types of nodes with different distributions over features -- for example, in a social network each component represents the different attributes of a distinct community. Natural algorithmic tasks associated with these networks are embedding (recovering the latent feature vectors) and clustering (grouping nodes by their mixture component). In this paper we initiate the study of clustering and embedding graphs sampled from high-dimensional Gaussian mixture block models, where the dimension of the latent feature vectors $d\to \infty$ as the size of the network $n \to \infty$. This high-dimensional setting is most appropriate in the context of modern networks, in which we think of the latent feature space as being high-dimensional. We analyze the performance of canonical spectral clustering and embedding algorithms for such graphs in the case of 2-component spherical Gaussian mixtures, and begin to sketch out the information-computation landscape for clustering and embedding in these models.
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System

Critical infrastructures like water treatment facilities and power plants depend on industrial control systems (ICS) for monitoring and control, making them vulnerable to cyber attacks and system malfunctions. Traditional ICS anomaly detection methods lack transparency and interpretability, which make it difficult for practitioners to understand and trust the results. This paper proposes a two-phase dual Copula-based Outlier Detection (COPOD) method that addresses these challenges. The first phase removes unwanted outliers using an empirical cumulative distribution algorithm, and the second phase develops two parallel COPOD models based on the output data of phase 1. The method is based on empirical distribution functions, parameter-free, and provides interpretability by quantifying each feature's contribution to an anomaly. The method is also computationally and memory-efficient, suitable for low- and high-dimensional datasets. Experimental results demonstrate superior performance in terms of F1-score and recall on three open-source ICS datasets, enabling real-time ICS anomaly detection.
Revised Properties and Dynamical History for the HD 17156 System

From the thousands of known exoplanets, those that transit bright host stars provide the greatest accessibility toward detailed system characterization. The first known such planets were generally discovered using the radial-velocity technique, then later found to transit. HD 17156b is particularly notable among these initial discoveries because it diverged from the typical hot-Jupiter population, occupying a 21.2 day eccentric (e = 0.68) orbit, offering preliminary insights into the evolution of planets in extreme orbits. Here we present new data for this system, including ground- and space-based photometry, radial velocities, and speckle imaging, that further constrain the system properties and stellar/planetary multiplicity. These data include photometry from the Transiting Exoplanet Survey Satellite that cover five transits of the known planet. We show that the system does not harbor any additional giant planets interior to 10 au. The lack of stellar companions and the age of the system indicate that the eccentricity of the known planet may have resulted from a previous planet–planet scattering event. We provide the results from dynamical simulations that suggest possible properties of an additional planet that culminated in ejection from the system, leaving a legacy of the observed high eccentricity for HD 17156b.
Attacker Profiling Through Analysis of Attack Patterns in Geographically Distributed Honeypots

Honeypots are a well-known and widely used technology in the cybersecurity community, where it is assumed that placing honeypots in different geographical locations provides better visibility and increases effectiveness. However, how geolocation affects the usefulness of honeypots is not well-studied, especially for threat intelligence as early warning systems. This paper examines attack patterns in a large public dataset of geographically distributed honeypots by answering methodological questions and creating behavioural profiles of attackers. Results show that the location of honeypots helps identify attack patterns and build profiles for the attackers. We conclude that not all the intelligence collected from geographically distributed honeypots is equally valuable and that a good early warning system against resourceful attackers may be built with only two distributed honeypots and a production server.
Random Function Descent

While gradient based methods are ubiquitous in machine learning, selecting the right step size often requires"hyperparameter tuning". This is because backtracking procedures like Armijo's rule depend on quality evaluations in every step, which are not available in a stochastic context. Since optimization schemes can be motivated using Taylor approximations, we replace the Taylor approximation with the conditional expectation (the best $L^2$ estimator) and propose"Random Function Descent"(RFD). Under light assumptions common in Bayesian optimization, we prove that RFD is identical to gradient descent, but with calculable step sizes, even in a stochastic context. We beat untuned Adam in synthetic benchmarks. To close the performance gap to tuned Adam, we propose a heuristic extension competitive with tuned Adam.
LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application

The development of Adaptive Cruise Control (ACC) systems aims to enhance the safety and comfort of vehicles by automatically regulating the speed of the vehicle to ensure a safe gap from the preceding vehicle. However, conventional ACC systems are unable to adapt themselves to changing driving conditions and drivers’ behavior. To address this limitation, we propose a Long Short-Term Memory (LSTM)based ACC system that can learn from past driving experiences and adapt and predict new situations in realtime. The model is constructed based on the real-world highD dataset, acquired from German highways with the assistance of camera-equipped drones. We evaluated the ACC system under aggressive lane changes when the side lane preceding vehicle cut off, forcing the targeted driver to reduce speed. To this end, the proposed system was assessed on a simulated driving environment and compared with a feedforward Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model. The results show that the LSTM-based system is 19.25 % more accurate than the ANN model and 5.9 % more accurate than the MPC model in terms of predicting future values of subject vehicle acceleration. The simulation is done in Matlab/Simulink environment.
Finding Neurons in a Haystack: Case Studies with Sparse Probing

Despite rapid adoption and deployment of large language models (LLMs), the internal computations of these models remain opaque and poorly understood. In this work, we seek to understand how high-level human-interpretable features are represented within the internal neuron activations of LLMs. We train $k$-sparse linear classifiers (probes) on these internal activations to predict the presence of features in the input; by varying the value of $k$ we study the sparsity of learned representations and how this varies with model scale. With $k=1$, we localize individual neurons which are highly relevant for a particular feature, and perform a number of case studies to illustrate general properties of LLMs. In particular, we show that early layers make use of sparse combinations of neurons to represent many features in superposition, that middle layers have seemingly dedicated neurons to represent higher-level contextual features, and that increasing scale causes representational sparsity to increase on average, but there are multiple types of scaling dynamics. In all, we probe for over 100 unique features comprising 10 different categories in 7 different models spanning 70 million to 6.9 billion parameters.
The minimum positive uniform Tur\'an density in uniformly dense $k$-uniform hypergraphs

A $k$-graph (or $k$-uniform hypergraph) $H$ is uniformly dense if the edge distribution of $H$ is uniformly dense with respect to every large collection of $k$-vertex cliques induced by sets of $(k-2)$-tuples. Reiher, R\"odl and Schacht [Int. Math. Res. Not. , 2018] proposed the study of the uniform Tur\'an density $\pi_{k-2}(F)$ for given $k$-graphs $F$ in uniformly dense $k$-graphs. Meanwhile, they [J. London Math. Soc. , 2018] characterized $k$-graphs $F$ satisfying $\pi_{k-2}(F)=0$ and showed that $\pi_{k-2}(\cdot)$ ``jumps"from 0 to at least $k^{-k}$. In particular, they asked whether there exist $3$-graphs $F$ with $\pi_{1}(F)$ equal or arbitrarily close to $1/27$. Recently, Garbe, Kr\'al' and Lamaison [arXiv:2105.09883] constructed some $3$-graphs with $\pi_{1}(F)=1/27$. In this paper, for any $k$-graph $F$, we give a lower bound of $\pi_{k-2}(F)$ based on a probabilistic framework, and provide a general theorem that reduces proving an upper bound on $\pi_{k-2}(F)$ to embedding $F$ in reduced $k$-graphs of the same density using the regularity method for $k$-graphs. By using this result and Ramsey theorem for multicolored hypergraphs, we extend the results of Garbe, Kr\'al' and Lamaison to $k\ge 3$. In other words, we give a sufficient condition for $k$-graphs $F$ satisfying $\pi_{k-2}(F)=k^{-k}$. Additionally, we also construct an infinite family of $k$-graphs with $\pi_{k-2}(F)=k^{-k}$.
Finiteness of the Tate-Shafarevich Group for some Elliptic Curves of Analytic Rank $>1$

We prove the finiteness of the Tate-Shafarevich group (i.e., the BSD conjecture) for a class of elliptic curves over function fields. This is an application of a more general theorem that the Tate conjecture in codimension $1$ is ''generically true'' for mod $p$ reductions of complex projective varieties with $h^{2, 0} = 1$, under a mild assumption on moduli. We also prove the Tate conjecture for a class of algebraic surfaces of general type with $p_g = 1$.
Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule

Previous results have shown that a two time-scale update rule (TTUR) using different learning rates, such as different constant rates or different decaying rates, is useful for training generative adversarial networks (GANs) in theory and in practice. Moreover, not only the learning rate but also the batch size is important for training GANs with TTURs and they both affect the number of steps needed for training. This paper studies the relationship between batch size and the number of steps needed for training GANs with TTURs based on constant learning rates. We theoretically show that, for a TTUR with constant learning rates, the number of steps needed to find stationary points of the loss functions of both the discriminator and generator decreases as the batch size increases and that there exists a critical batch size minimizing the stochastic first-order oracle (SFO) complexity. Then, we use the Fr'echet inception distance (FID) as the performance measure for training and provide numerical results indicating that the number of steps needed to achieve a low FID score decreases as the batch size increases and that the SFO complexity increases once the batch size exceeds the measured critical batch size. Moreover, we show that measured critical batch sizes are close to the sizes estimated from our theoretical results.
Robust reliable feedback controller design against actuator faults for linear parameter-varying systems in finite-frequency domain

This study addresses the finite-frequency robust feedback controller design problem against actuator faults for linear parameter-varying systems. First, a general model of actuator faults is presented. Then, sufficient conditions for the existence of the state-feedback controller are obtained by using generalised Kalman–Yakubovich–Popov lemma and projection lemma, which guarantee that the closed-loop system satisfies robustness performance in a finite-frequency domain and is stable for both faults free and actuator faults. In addition, by introducing a state feedback gain, the non-convexity conditions of the output-feedback gain are derived. An iterative linear matrix inequality algorithm is proposed in this study to get the solution. The performances of the proposed reliable controller schemes are illustrated by two examples.
Aurora A kinase inhibition compromises its antitumor efficacy by elevating PD-L1 expression

Aurora A plays a critical role in G2/M transition and mitosis, making it an attractive target for cancer treatment. Aurora A inhibitors showed remarkable antitumor effects in preclinical studies, but unsatisfactory outcomes in clinical trials have greatly limited their development. In this study, the Aurora A inhibitor alisertib upregulated programmed death ligand 1 (PD-L1) expression in a panel of tumor cells both in vitro and in vivo. Upregulation of the checkpoint protein PD-L1 reduced antitumor immunity in immune-competent mice, paradoxically inhibiting the antitumor effects of alisertib. Mechanistically, Aurora A directly bound to and phosphorylated cyclic GMP-AMP synthase (cGAS), suppressing PD-L1 expression in tumor cells. Aurora A inhibition by alisertib activated the cGAS/stimulator of IFN genes (STING)/NF-κB pathway and promoted PD-L1 expression. Combining alisertib with anti–PD-L1 antibody improved antitumor immunity and enhanced the antitumor effects of alisertib in immune-competent mice. Our results, which reveal the immunomodulatory functions of Aurora A inhibitors and provide a plausible explanation for the poor clinical outcomes with their use, offer a potential approach to improve the antitumor efficacy of these inhibitors.
Multiomics analysis of human peripheral blood reveals marked molecular profiling changes caused by one night of sleep deprivation

Abstract Sleep insufficiency is associated with various disorders; the molecular basis is unknown until now. Here, 14 males and 18 females were subjected to short‐term (24 h) sleep deprivation, and donated fasting blood samples prior to (day 1) and following (days 2 and 3) short‐term sleep deprivation. We used multiple omics techniques to examine changes in volunteers’ blood samples that were subjected to integrated, biochemical, transcriptomic, proteomic, and metabolomic analyses. Sleep deprivation caused marked molecular changes (46.4% transcript genes, 59.3% proteins, and 55.6% metabolites) that incompletely reversed by day 3. The immune system in particular neutrophil‐mediated processes associated with plasma superoxidase dismutase‐1 and S100A8 gene expression was markedly affected. Sleep deprivation decreased melatonin levels and increased immune cells, inflammatory factors and c‐reactive protein. By disease enrichment analysis, sleep deprivation induced signaling pathways for schizophrenia and neurodegenerative diseases enriched. In sum, this is the first multiomics approach to show that sleep deprivation causes prominent immune changes in humans, and clearly identified potential immune biomarkers associated with sleep deprivation. This study indicated that the blood profile following sleep disruption, such as may occur among shift workers, may induce immune and central nervous system dysfunction.
Airborne Tracking Sunphotometer

Abstract An airborne tracking sunphotometer, mounted on the outside top surface of an aircraft has been developed to provide unrestricted viewing of the Sun. This instrument will substantially increase the data that scientists can gather for atmospheric studies. The instrument has six wavelength channels and an automatic data gathering system. The instrument's optical features, tracking capability, mechanical features, and data gathering system are described.
γ′ fibrinogen levels as a biomarker of COVID-19 respiratory disease severity

Coronavirus disease 2019 (COVID-19) is characterized by a pro-inflammatory state associated with organ failure, thrombosis, and death. We investigated a novel inflammatory biomarker, γ′ fibrinogen (GPF), in 103 hospitalized patients with COVID-19 and 19 healthy controls. We found significant associations between GPF levels and the severity of COVID-19 as judged by blood oxygen saturation (SpO2). The mean level of GPF in the patients with COVID-19 was significantly higher than in controls (69.8 (95 % CI 64.8–74.8) mg/dL compared with 36.9 (95 % CI 31.4–42.4) mg/dL, p < 0.0001), whereas C-reactive protein (CRP), lactate dehydrogenase (LDH), and total fibrinogen levels were not significantly different between groups. Mean GPF levels were significantly highest in patients with severe COVID-19 (SpO2 ≤ 93 %, GPF 75.2 (95 % CI 68.7–81.8) mg/dL), compared to mild/moderate COVID-19 (SpO2 > 93 %, GPF 62.5 (95 % CI 55.0–70.0) mg/dL, p = 0.01, AUC of 0.68, 95 % CI 0.57–0.78; Youden's index cutpoint 62.9 mg/dL, sensitivity 0.64, specificity 0.63). In contrast, CRP, interleukin-6, ferritin, LDH, D-dimers, and total fibrinogen had weaker associations with COVID-19 disease severity (all ROC curves with lower AUCs). Thus, GPF may be a useful inflammatory marker of COVID-19 respiratory disease severity.
Macroeconomic Forecasting using Dynamic Factor Models: The Case of Morocco

This article discusses the use of dynamic factor models in macroeconomic forecasting, with a focus on the Factor-Augmented Error Correction Model (FECM). The FECM combines the advantages of cointegration and dynamic factor models, providing a flexible and reliable approach to macroeconomic forecasting, especially for non-stationary variables. We evaluate the forecasting performance of the FECM model on a large dataset of 117 Moroccan economic series with quarterly frequency. Our study shows that FECM outperforms traditional econometric models in terms of forecasting accuracy and robustness. The inclusion of long-term information and common factors in FECM enhances its ability to capture economic dynamics and leads to better forecasting performance than other competing models. Our results suggest that FECM can be a valuable tool for macroeconomic forecasting in Morocco and other similar economies.
Localized Multi-Site Knee Bioimpedance as a Predictor for Knee Osteoarthritis Associated Pain Within Older Adults During Free-Living

The drastic increase in the aging population has increased the prevalence of osteoarthritis in the United States. The ability to monitor symptoms of osteoarthritis (such as pain) within a free-living environment could improve understanding of each person's experiences with this disease and provide opportunities to personalize treatments specific to each person and their experience. In this work, localized knee tissue bioimpedance and self-reports of knee pain were collected from older adults (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$N=20$\end{document}) with and without knee osteoarthritis over 7 days of free-living to evaluate if knee tissue bioimpedance is associated with persons' knee pain experience. Within the group of persons' with knee osteoarthritis increases in 128 kHz per-length resistance and decreases in 40 kHz per-length reactance were associated with increased probability of persons having active knee pain (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p=0.038$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p=0.044$\end{document}).