Miracle-dz/UniFAR-FTUs huggingface dataset:
An In-Depth Survey on Virtualization Technologies in 6G Integrated Terrestrial and Non-Terrestrial Networks

6G networks are envisioned to deliver a large diversity of applications and meet stringent quality of service (QoS) requirements. Hence, integrated terrestrial and non-terrestrial networks (TN-NTNs) are anticipated to be key enabling technologies. However, the TN-NTNs integration faces a number of challenges that could be addressed through network virtualization technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and network slicing. In this survey, we provide a comprehensive review on the adaptation of these networking paradigms in 6G networks. We begin with a brief overview on NTNs and virtualization techniques. Then, we highlight the integral role of Artificial Intelligence in improving network virtualization by summarizing major research areas where AI models are applied. Building on this foundation, the survey identifies the main issues arising from the adaptation of SDN, NFV, and network slicing in integrated TN-NTNs, and proposes a taxonomy of integrated TN-NTNs virtualization offering a thorough review of relevant contributions. The taxonomy is built on a four-level classification indicating for each study the level of TN-NTNs integration, the used virtualization technology, the addressed problem, the type of the study and the proposed solution, which can be based on conventional or AI-enabled methods. Moreover, we present a summary on the simulation tools commonly used in the testing and validation of such networks. Finally, we discuss open issues and give insights on future research directions for the advancement of integrated TN-NTNs virtualization in the 6G era.
DPHMs: Diffusion Parametric Head Models for Depth-based Tracking

We introduce Diffusion Parametric Head Models (DPHMs), a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences. While recent volumetric head models, such as NPHMs, can now excel in representing high-fidelity head geometries, tracking and reconstruction heads from real-world single-view depth sequences remains very challenging, as the fitting to partial and noisy observations is underconstrained. To tackle these challenges, we propose a latent diffusion-based prior to regularize volumetric head reconstruction and tracking. This prior-based regularizer effectively constrains the identity and expression codes to lie on the underlying latent manifold which represents plausible head shapes. To evaluate the effectiveness of the diffusion-based prior, we collect a dataset of monocular Kinect sequences consisting of various complex facial expression motions and rapid transitions. We compare our method to state-of-the-art tracking methods, and demonstrate improved head identity reconstruction as well as robust expression tracking.
Stochastic Resource Allocation via Dual Tail Waterfilling

Optimal resource allocation in wireless systems still stands as a rather challenging task due to the inherent statistical characteristics of channel fading. On the one hand, minimax/outage-optimal policies are often overconservative and analytically intractable, despite advertising maximally reliable system performance. On the other hand, ergodic-optimal resource allocation policies are often susceptible to the statistical dispersion of heavy-tailed fading channels, leading to relatively frequent drastic performance drops. We investigate a new risk-aware formulation of the classical stochastic resource allocation problem for point-to-point power-constrained communication networks over fading channels with no cross-interference, by leveraging the Conditional Value-at-Risk (CV@R) as a coherent measure of risk. We rigorously derive closed-form expressions for the CV@R-optimal risk-aware resource allocation policy, as well as the optimal associated quantiles of the corresponding user rate functions by capitalizing on the underlying fading distribution, parameterized by dual variables. We then develop a purely dual tail waterfilling scheme, achieving significantly more rapid and assured convergence of dual variables, as compared with the primal-dual tail waterfilling algorithm, recently proposed in the literature. The effectiveness of the proposed scheme is also readily confirmed via detailed numerical simulations.
Integrality of genus zero Gopakumar-Vafa type invariants of semi-positive varieties

We give an alternate proof of the integrality conjecture of genus zero Gopakumar-Vafa type invariants on semi-positive varieties using algebraic geometry. The main technique is to relate Gopakumar-Vafa type invariants to quantum $K$-invariants and to utilize the integrality of the latter.
Model parameter optimisation with Bayesian statistics

The accuracy and precision of models are key issues for the design and development of new applications and experiments. We present a method of optimisation for a large variety of models. This approach is designed in order both to improve the accuracy of models through the modification of free parameters of these models, which results in a better reproduction of experimental data, and to estimate the uncertainties of these parameters and, by extension, their impacts on the model output. We discuss the method in detail and present a proof-of-concept for Monte Carlo models.
Mineralogical and Chemical Properties and the Origin of Two Types of Analcime in SW Ankara, Turkey

Authigenic analcimes were observed in different amounts in Miocene units in central Anatolia, Turkey. Two types of analcime occurrences were defined: (1) as continuous but inhomogeneous concentrations varying from 3 to 75 wt. % in lacustrine sedimentary rocks; and (2) as low concentrations (between 3 and 20%) and discontinuous components in the tuffs and claystones intercalated with tuff. The type 2 analcimes have been investigated by many researchers while the origin and properties of the sedimentary analcimes, which are widespread in different parts of Turkey, have not been clarified. The present study focused on the genesis and the mineralogical and geochemical properties of both types of analcime. The analcimes were investigated using X-ray diffraction, optical microscopy, scanning electron microscopy, and chemical analytical methods. In the first type, other than volcaniclastic material, analcime is the only zeolite mineral. The first type of analcime was associated mainly with montmorillonite, dolomite, and feldspar and sometimes with calcite, and rarely with illite and kaolinite. The second type of analcime was found as an accessory mineral accompanied by montmorillonite, feldspar, and heulandite/clinoptilolite, and more rarely by erionite, kaolinite, and mica. The pyroclastic rocks are chemically classified into two subgroups, dacitic and andesitic rocks, with an intermediate to high silica content and a high percentage of alkali cations. Analcime in the pyroclastics intercalated with clay layers commonly replaced early-formed zeolites, such as clinoptilolite or volcanic materials. The first type of analcime was not formed from precursor zeolites and had a different origin than the second type. Type 1 analcime contains larger amounts of Si (34.19 to 34.68 Si per unit cell) and less Al and Na than in theoretical analcime. The theoretical structural formula of analcime is Na16(Al16Si32O96)H2O. The strongly decomposing feldspar and clay minerals (in particular montmorillonite and partially illite) of the older formations and the dissolution of halite and also soda minerals, e.g. thenardite and glauberite, allow the authigenic formation of type 1 analcime, dolomite, K-feldspar, and montmorillonite in a saline and highly alkaline environment such as the marginal part of Lake Tuzgölü. Type 2 analcime may have been precipitated directly from solution, pyroclastic material, or precursor zeolite minerals in saline and alkaline lake water.
On a minimization problem of the maximum generalized eigenvalue: properties and algorithms

We study properties and algorithms of a minimization problem of the maximum generalized eigenvalue of symmetric-matrix-valued affine functions, which is nonsmooth and quasiconvex, and has application to eigenfrequency optimization of truss structures. We derive an explicit formula of the Clarke subdifferential of the maximum generalized eigenvalue and prove the maximum generalized eigenvalue is a pseudoconvex function, which is a subclass of a quasiconvex function, under suitable assumptions. Then, we consider smoothing methods to solve the problem. We introduce a smooth approximation of the maximum generalized eigenvalue and prove the convergence rate of the smoothing projected gradient method to a global optimal solution in the considered problem. Also, some heuristic techniques to reduce the computational costs, acceleration and inexact smoothing, are proposed and evaluated by numerical experiments.
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees

Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SpectRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided by learning neural network policies together with reach-avoid supermartingales (RASM) for the graph's sub-tasks and then composing them into a global policy. We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies. We implement a prototype of our approach and evaluate it on a Stochastic Nine Rooms environment.
Quick Back-Translation for Unsupervised Machine Translation

The field of unsupervised machine translation has seen significant advancement from the marriage of the Transformer and the back-translation algorithm. The Transformer is a powerful generative model, and back-translation leverages Transformer's high-quality translations for iterative self-improvement. However, the Transformer is encumbered by the run-time of autoregressive inference during back-translation, and back-translation is limited by a lack of synthetic data efficiency. We propose a two-for-one improvement to Transformer back-translation: Quick Back-Translation (QBT). QBT re-purposes the encoder as a generative model, and uses encoder-generated sequences to train the decoder in conjunction with the original autoregressive back-translation step, improving data throughput and utilization. Experiments on various WMT benchmarks demonstrate that a relatively small number of refining steps of QBT improve current unsupervised machine translation models, and that QBT dramatically outperforms standard back-translation only method in terms of training efficiency for comparable translation qualities.
DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text

Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignoring extreme video text challenges, i.e., dense and small text in various scenarios. In this paper, we establish a video text reading benchmark, named DSText V2, which focuses on Dense and Small text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotters to track and read. 2) High-proportioned small texts, coupled with the blurriness and distortion in the video, will bring further challenges. 3) Various new scenarios, e.g., Game, Sports, etc. The proposed DSText V2 includes 140 video clips from 7 open scenarios, supporting three tasks, i.e., video text detection (Task 1), video text tracking (Task 2), and end-to-end video text spotting (Task 3). In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols, and the results summaries. Most importantly, a thorough investigation and analysis targeting three unique challenges derived from our dataset are provided, aiming to provide new insights. Moreover, we hope the benchmark will promise video text research in the community. DSText v2 is built upon DSText v1, which was previously introduced to organize the ICDAR 2023 competition for dense and small video text.
Superconductivity in Ca-intercalated bilayer graphene: C$_{2}$CaC$_{2}$

The deposition and intercalation of metal atoms can induce superconductivity in monolayer and bilayer graphenes. For example, it has been experimentally proved that Li-deposited graphene is a superconductor with critical temperature $T_{c}$ of 5.9 K, Ca-intercalated bilayer graphene C$_{6}$CaC$_{6}$ and K-intercalated epitaxial bilayer graphene C$_{8}$KC$_{8}$ are superconductors with $T_{c}$ of 2-4 K and 3.6 K, respectively. However, the $T_{c}$ of them are relatively low. To obtain higher $T_{c}$ in graphene-based superconductors, here we predict a new Ca-intercalated bilayer graphene C$_{2}$CaC$_{2}$, which shows higher Ca concentration than the C$_{6}$CaC$_{6}$. It is proved to be thermodynamically and dynamically stable. The electronic structure, electron-phonon coupling (EPC) and superconductivity of C$_{2}$CaC$_{2}$ are investigated based on first-principles calculations. The EPC of C$_{2}$CaC$_{2}$ mainly comes from the coupling between the electrons of C-$p_{z}$ orbital and the high- and low-frequency vibration modes of C atoms. The calculated EPC constant $\lambda$ of C$_{2}$CaC$_{2}$ is 0.75, and the superconducting $T_{c}$ is 18.9 K, which is much higher than other metal-intercalated bilayer graphenes. By further applying -4\% biaxial compressive strain to C$_{2}$CaC$_{2}$, the $T_{c}$ can be boosted to 26.6 K. Thus, the predicted C$_{2}$CaC$_{2}$ provides a new platform for realizing superconductivity with the highest $T_{c}$ in bilayer graphenes.
Doubly 1-Bit Quantized Massive MIMO

Enabling communications in the (sub-)THz band will call for massive multiple-input multiple-output (MIMO) arrays at either the transmit- or receive-side, or at both. To scale down the complexity and power consumption when operating across massive frequency and antenna dimensions, a sacrifice in the resolution of the digital-to-analog/analog-to-digital converters (DACs/ADCs) will be inevitable. In this paper, we analyze the extreme scenario where both the transmit- and receive-side are equipped with fully digital massive MIMO arrays and 1-bit DACs/ADCs, which leads to a system with minimum radio-frequency complexity, cost, and power consumption. Building upon the Bussgang decomposition, we derive a tractable approximation of the mean squared error (MSE) between the transmitted data symbols and their soft estimates. Numerical results show that, despite its simplicity, a doubly 1-bit quantized massive MIMO system with very large antenna arrays can deliver an impressive performance in terms of MSE and symbol error rate.
Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach

Anomaly detection is defined as the problem of finding data points that do not follow the patterns of the majority. Among the various proposed methods for solving this problem, classification-based methods, including one-class Support Vector Machines (SVM) are considered effective and state-of-the-art. The one-class SVM method aims to find a decision boundary to distinguish between normal data points and anomalies using only the normal data. On the other hand, most real-world problems involve some degree of uncertainty, where the true probability distribution of each data point is unknown, and estimating it is often difficult and costly. Assuming partial distribution information such as the first and second-order moments is known, a distributionally robust chance-constrained model is proposed in which the probability of misclassification is low. By utilizing a mapping function to a higher dimensional space, the proposed model will be capable of classifying origin-inseparable datasets. Also, by adopting the kernel idea, the need for explicitly knowing the mapping is eliminated, computations can be performed in the input space, and computational complexity is reduced. Computational results validate the robustness of the proposed model under different probability distributions and also the superiority of the proposed model compared to the standard one-class SVM in terms of various evaluation metrics.
Trisections of PL 4-manifolds associated to colored triangulations

The purpose of the present paper is twofold: firstly to extend to non-orientable compact 4-manifolds, both in the closed and the boundary case, the notion of {\it gem-induced trisection}, which arises from colored triangulations; secondly to prove that, if the boundary is homeomorphic to a connected sum of sphere bundles over $\mathbb S^1$, gem-induced trisections naturally give rise to trisections of the corresponding closed 4-manifold. As a consequence, an estimation of the trisection genus of any closed orientable 4-manifold in terms of surgery description is obtained via trisections associated to colored triangulations. Moreover, trisections arising from gems turn out to realize the trisection genus for a wide class of (orientable and non-orientable) 4-manifolds.
The largely linear response of earth's ice volume to orbital forcing

Orbital forcing plays a key role in pacing the glacial-interglacial cycles. However, the mechanistic linkages between the orbital parameters - eccentricity, obliquity, and precession - and global ice volume remain unclear. Here, we investigate the effect of Earth's orbitally governed incoming solar radiation (that is, insolation) on global ice volume over the past 800,000 years. We consider a simple linear model of ice volume that imposes minimal assumptions about its dynamics. We find that this model can adequately reproduce the observed ice volume variations for most of the past 800,000 years, with the notable exception of Marine Isotope Stage 11. This suggests that, aside from a few extrema, the ice volume dynamics primarily result from an approximately linear response to orbital forcing. We substantiate this finding by addressing some of the key criticisms of the orbitally forced hypothesis. In particular, we show that eccentricity can significantly vary the ocean temperature without the need for amplification on Earth. We also present a feasible mechanism to explain the absence of eccentricity's 400,000 year period in the ice volume data. This requires part of the forcing from eccentricity to be lagged via a slow-responding mechanism, resulting in a signal that closer approximates the change in eccentricity. A physical interpretation of our model is proposed, using bulk ocean and surface temperatures as intermediate mechanisms through which the orbital parameters affect ice volume. These show reasonable alignment with their relevant proxy data, though we acknowledge that these variables likely represent a combination of mechanisms.
Effects of linking topology on the shear response of connected ring polymers: Catenanes and bonded rings flow differently

We perform computer simulations of mechanically linked (poly[2]catenanes, PC) and chemically bonded (bonded rings, BR) pairs of self-avoiding ring polymers in steady shear. We find that BR's develop a novel motif, termed gradient tumbling, rotating around the gradient axis. For the PC's the rings are stretched and display another new pattern, termed slip-tumbling. The dynamics of BR's is continuous and oscillatory, whereas that of PC's is intermittent between slip-tumbling attempts. Our findings demonstrate the interplay between topology and hydrodynamics in dilute solutions of connected polymers.
Universal Deoxidation of Semiconductor Substrates Assisted by Machine-Learning and Real-Time-Feedback-Control

Thin film deposition is an essential step in the semiconductor process. During preparation or loading, the substrate is exposed to the air unavoidably, which has motivated studies of the process control to remove the surface oxide before thin film deposition. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for a random substrate is a multidimensional challenge and sometimes controversial. Due to variations in semiconductor materials and growth processes, the determination of substrate deoxidation temperature is highly dependent on the grower's expertise; the same substrate may yield inconsistent results when evaluated by different growers. Here, we employ a machine learning (ML) hybrid convolution and vision transformer (CNN-ViT) model. This model utilizes reflection high-energy electron diffraction (RHEED) video as input to determine the deoxidation status of the substrate as output, enabling automated substrate deoxidation under a controlled architecture. This also extends to the successful application of deoxidation processes on other substrates. Furthermore, we showcase the potential of models trained on data from a single MBE equipment to achieve high-accuracy deployment on other equipment. In contrast to traditional methods, our approach holds exceptional practical value. It standardizes deoxidation temperatures across various equipment and substrate materials, advancing the standardization research process in semiconductor preparation, a significant milestone in thin film growth technology. The concepts and methods demonstrated in this work are anticipated to revolutionize semiconductor manufacturing in optoelectronics and microelectronics industries by applying them to diverse material growth processes.
Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies

The benefits and capabilities of pre-trained language models (LLMs) in current and future innovations are vital to any society. However, introducing and using LLMs comes with biases and discrimination, resulting in concerns about equality, diversity and fairness, and must be addressed. While understanding and acknowledging bias in LLMs and developing mitigation strategies are crucial, the generalised assumptions towards societal needs can result in disadvantages towards under-represented societies and indigenous populations. Furthermore, the ongoing changes to actual and proposed amendments to regulations and laws worldwide also impact research capabilities in tackling the bias problem. This research presents a comprehensive survey synthesising the current trends and limitations in techniques used for identifying and mitigating bias in LLMs, where the overview of methods for tackling bias are grouped into metrics, benchmark datasets, and mitigation strategies. The importance and novelty of this survey are that it explores the perspective of under-represented societies. We argue that current practices tackling the bias problem cannot simply be 'plugged in' to address the needs of under-represented societies. We use examples from New Zealand to present requirements for adopting existing techniques to under-represented societies.