Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of Direct LEO Satellite-to-Smartphone Communications by Distributed Beamforming

The low earth orbit (LEO) satellite network is undergoing rapid development with the maturing of satellite communications and rocket launch technologies, and the demand for a global coverage network. However, current satellite communication networks are constrained by limited transmitting signal power, resulting in the use of large-size and energy-consuming ground terminals to provide additional gain. This paper proposes a novel technology called distributed beamforming to address such challenges and support direct communications from LEO satellites to smartphones. The proposed distributed beamforming technique is based on the superposition of electromagnetic (EM) waves and aims to enhance the received signal strength. Furthermore, we utilize EM wave superposition to increase the link budget and provide the coverage pattern formed by the distributed antenna array, which will be affected by the array structure and the transmitter parameters. In addition, the impact of Doppler frequency shift and time misalignment on the performance of distributed beamforming is investigated. Numerical results show that the enhancement of the received power depends on the angle formed by those radiated beams and can be up to the square of the number of beams; namely, a maximum enhancement of 6 dB could be obtained by using two satellites and a maximum of 12 dB increase through four satellites, which provide a clear guideline for the design of distributed beamforming for future satellite communications.
CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks

Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning.
Approximation properties for dynamical W*-correspondences

Let $\mathbb{G}$ be a locally compact quantum group, and $A,B$ von Neumann algebras with an action by $\mathbb{G}$. We refer to these as $\mathbb{G}$-dynamical W$^*$-algebras. We make a study of $\mathbb{G}$-equivariant $A$-$B$-correspondences, that is, Hilbert spaces $\mathcal{H}$ with an $A$-$B$-bimodule structure by $*$-preserving normal maps, and equipped with a unitary representation of $\mathbb{G}$ which is equivariant with respect to the above bimodule structure. Such structures are a Hilbert space version of the theory of $\mathbb{G}$-equivariant Hilbert C$^*$-bimodules. We show that there is a well-defined Fell topology on equivariant correspondences, and use this to formulate approximation properties for them. Within this formalism, we then characterize amenability of the action of a locally compact group on a von Neumann algebra, using recent results due to Bearden and Crann. We further consider natural operations on equivariant correspondences such as taking opposites, composites and crossed products, and examine the continuity of these operations with respect to the Fell topology.
Pareto Invariant Representation Learning for Multimedia Recommendation

Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to reveal users' true preferences. Existing works attempt to alleviate this problem by learning invariant representations, but overlook the balance between independent and identically distributed (IID) and out-of-distribution (OOD) generalization. In this paper, we propose a framework called Pareto Invariant Representation Learning (PaInvRL) to mitigate the impact of spurious correlations from an IID-OOD multi-objective optimization perspective, by learning invariant representations (intrinsic factors that attract user attention) and variant representations (other factors) simultaneously. Specifically, PaInvRL includes three iteratively executed modules: (i) heterogeneous identification module, which identifies the heterogeneous environments to reflect distributional shifts for user-item interactions; (ii) invariant mask generation module, which learns invariant masks based on the Pareto-optimal solutions that minimize the adaptive weighted Invariant Risk Minimization (IRM) and Empirical Risk (ERM) losses; (iii) convert module, which generates both variant representations and item-invariant representations for training a multi-modal recommendation model that mitigates spurious correlations and balances the generalization performance within and cross the environmental distributions. We compare the proposed PaInvRL with state-of-the-art recommendation models on three public multimedia recommendation datasets (Movielens, Tiktok, and Kwai), and the experimental results validate the effectiveness of PaInvRL for both within-and cross-environmental learning.
A hyper-distance-based method for hypernetwork comparison

Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks, however, the comparison of the difference between two hypernetworks has been given less attention. This paper proposes a hyper-distance-based method (HD) for comparing hypernetworks. This method takes into account high-order information, such as the high-order distance between nodes. The experiments carried out on synthetic hypernetworks have shown that HD is capable of distinguishing between hypernetworks generated with different parameters, and it is successful in the classification of hypernetworks. Furthermore, HD outperforms current state-of-the-art baselines to distinguish empirical hypernetworks when hyperedges are disrupted.
Exploring lepton flavor violation phenomena of the 
Z
 and Higgs bosons at electron-proton colliders

We comprehensively study the potential for discovering lepton flavor violation (LFV) phenomena associated with the $Z$ and Higgs bosons at the LHeC and FCC-he. Our meticulous investigation reveals the remarkable suitability of electron-proton colliders for probing these rare new physics signals. This is due to the distinct advantages they offer, including negligible pileups, minimal QCD backgrounds, electron-beam polarization $P_e$, and the capability of distinguishing the charged-current from neutral-current processes. In our pursuit of LFV of the $Z$ boson, we employ an innovative indirect probe, utilizing the $t$-channel mediation of the $Z$ boson in the process $p e^- \to j \tau^-$. For LFV in the Higgs sector, we scrutinize direct observations of the on-shell decays of $H\to e^+\tau^-$ and $H\to \mu^\pm\tau^\mp$ through the charged-current production of $H$. Focusing on $H\to e^+\tau^-$ proves highly efficient due to the absence of positron-related backgrounds in the charged-current modes at electron-proton colliders. Through a dedicated signal-to-background analysis with the boosted decision tree algorithm, we demonstrate that the LHeC with the total integrated luminosity of $1{\,{\rm ab}^{-1}}$ can put significantly lower $2\sigma$ bounds than the HL-LHC with $3{\,{\rm ab}^{-1}}$. Specifically, we find ${\rm{Br}}(Z\to e\tau)<2.2 \times 10^{-7}$, ${\rm{Br}}(H\to e\tau)<1.7 \times 10^{-4} $, and ${\rm{Br}}(H\to \mu\tau)<1.0 \times 10^{-4}$. Furthermore, our study uncovers the exceptional precision of the FCC-he in measuring the LFV signatures of the $Z$ and Higgs bosons, which indicates the potential for future discoveries in this captivating field.
Conversion possibilities of oak (Quercus sp. L.) coppices into high forests in Bartin, Turkey.

In this study the possibilities of converting Bartin-region oak coppices (Quercus sp.L) into high forests. The study has been performed in 50 sample plots and in addition to the data obtained from sample plots stem analysis values of 30 oaks have been inspected. The development of volume and volume components of oak in single tree has been determined by using stem analysis values of sample trees. The data obtained from sample plots have been utilized to examine the development of stand volume and volume components with respect to time. By the help of the amount of product types, product amounts from stand have been calculated according to various rotation length alternatives. Present Net Values have been calculated by using income and expenditure data. High forest and coppice investment alternatives have been compared in the aspect of wood production abilities and discounted net values they provide and their effectiveness have been stated.
A tensor density measure of topological charge in three dimensional nematic phases

A path independent measure in order parameter space is introduced such that, when integrated along any closed contour in a three dimensional nematic phase, it yields the topological charge of any line defects encircled by the contour. A related measure, when integrated over either closed or open surfaces, reduces to known results for the charge associated with point defects (hedgehogs) or Skyrmions. We further define a tensor density, the disclination density tensor $\mathbf{D}$, from which the location of a disclination line can be determined. This tensor density has a dyadic decomposition near the line into its tangent and its rotation vector, allowing a convenient determination of both. The tensor $\mathbf{D}$ may be nonzero in special configurations in which there are no defects (double-splay or double-twist configurations), and its behavior there is provided. The special cases of Skyrmions and hedgehog defects are also examined, including the computation of their topological charge from $ \mathbf{D}$.
Risk-Based Security Measure Allocation Against Actuator Attacks

This article considers the problem of risk-optimal allocation of security measures when the actuators of an uncertain control system are under attack. We consider an adversary injecting false data into the actuator channels. The attack impact is characterized by the maximum performance loss caused by a stealthy adversary with bounded energy. Since the impact is a random variable, due to system uncertainty, we use Conditional Value-at-Risk (CVaR) to characterize the risk associated with the attack. We then consider the problem of allocating security measures to the set of actuators to minimize the risk. We assume that there are only a limited number of security measures available. Under this constraint, we observe that the allocation problem is a mixed-integer optimization problem. Thus we use relaxation techniques to approximate the security allocation problem into a Semi-Definite Program (SDP). We also compare our allocation method <inline-formula><tex-math notation="LaTeX">$(i)$</tex-math></inline-formula> across different risk measures: the worst-case measure, the average (nominal) measure, and <inline-formula><tex-math notation="LaTeX">$(ii)$</tex-math></inline-formula> across different search algorithms: the exhaustive and the greedy search algorithms. We depict the efficacy of our approach through numerical examples.
Measurement of the J/ψ Polarization with Respect to the Event Plane in Pb-Pb Collisions at the LHC.

We study the polarization of inclusive J/ψ produced in Pb-Pb collisions at sqrt[s_{NN}]=5.02 TeV at the LHC in the dimuon channel, via the measurement of the angular distribution of its decay products. We perform the study in the rapidity region 2.5<y<4, for three transverse momentum intervals (2<p_{T}<4, 4<p_{T}<6, 6<p_{T}<10 GeV/c) and as a function of the centrality of the collision for 2<p_{T}<6 GeV/c. For the first time, the polarization is measured with respect to the event plane of the collision, by considering the angle between the positive-charge decay muon in the J/ψ rest frame and the axis perpendicular to the event-plane vector in the laboratory system. A small transverse polarization is measured, with a significance reaching 3. 9σ at low p_{T} and for intermediate centrality values. The polarization could be connected with the behavior of the quark-gluon plasma, formed in Pb-Pb collisions, as a rotating fluid with large vorticity, as well as with the existence of a strong magnetic field in the early stage of its formation.
PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous learning-based works commonly focus on supervised registration, which have limitations in practice. Recently, with the advance of inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to achieve unsupervised registration. However, most of existing unsupervised methods follow a cascaded design or fuse RGB-D data in a unidirectional manner, which do not fully exploit the complementary information in the RGB-D data. To leverage the complementary information more effectively, we propose a network implementing multi-scale bidirectional fusion between RGB images and point clouds generated from depth images. By bidirectionally fusing visual and geometric features in multi-scales, more distinctive deep features for correspondence estimation can be obtained, making our registration more accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our method achieves new state-of-the-art performance. Code will be released at https://github.com/phdymz/PointMBF.
Deterministic k-Vertex Connectivity in k2 Max-flows

An $n$-vertex $m$-edge graph is \emph{$k$-vertex connected} if it cannot be disconnected by deleting less than $k$ vertices. After more than half a century of intensive research, the result by [Li et al. STOC'21] finally gave a \emph{randomized} algorithm for checking $k$-connectivity in near-optimal $\widehat{O}(m)$ time. (We use $\widehat{O}(\cdot)$ to hide an $n^{o(1)}$ factor. ) Deterministic algorithms, unfortunately, have remained much slower even if we assume a linear-time max-flow algorithm: they either require at least $\Omega(mn)$ time [Even'75; Henzinger Rao and Gabow, FOCS'96; Gabow, FOCS'00] or assume that $k=o(\sqrt{\log n})$ [Saranurak and Yingchareonthawornchai, FOCS'22]. We show a \emph{deterministic} algorithm for checking $k$-vertex connectivity in time proportional to making $\widehat{O}(k^{2})$ max-flow calls, and, hence, in $\widehat{O}(mk^{2})$ time using the deterministic max-flow algorithm by [Brand et al. FOCS'23]. Our algorithm gives the first almost-linear-time bound for all $k$ where $\sqrt{\log n}\le k\le n^{o(1)}$ and subsumes up to a sub polynomial factor the long-standing state-of-the-art algorithm by [Even'75] which requires $O(n+k^{2})$ max-flow calls. Our key technique is a deterministic algorithm for terminal reduction for vertex connectivity: given a terminal set separated by a vertex mincut, output either a vertex mincut or a smaller terminal set that remains separated by a vertex mincut. We also show a deterministic $(1+\epsilon)$-approximation algorithm for vertex connectivity that makes $O(n/\epsilon^2)$ max-flow calls, improving the bound of $O(n^{1.5})$ max-flow calls in the exact algorithm of [Gabow, FOCS'00]. The technique is based on Ramanujan graphs.
Resilience in a time of crisis: how COVID-19 pandemic insights are supporting a vibrant longevity risk transfer market

Abstract Pension risk transfer and longevity risk transfer are now growing secular trends. From North America to Europe, companies are de-risking pension plans in near-record volumes and have continued to boldly do so throughout the pandemic—at or near the most favorable pricing experienced in years. The arrival of funded reinsurance on both sides of the Atlantic is bringing reinsurer capital and private assets to support the steady growth in the pension risk transfer market. Additionally, we have observed that the enduring low-rate environment and quest for uncorrelated risk has the world's largest investors directing billions into life reinsurance sidecars. How have these markets thrived during the worst global outbreak in a century? Key research on the pandemic's impact on pensioner life expectancy allowed prices to be set and transactions to proceed through a time of significant uncertainty.
Nakula: Coercion Resistant Data Storage against Time-Limited Adversary

Both private citizens and professionals including journalists and whistleblowers can find themselves in a situation where they need to physically carry confidential data on a mobile device, through a situation where they might have their device seized and be subject to interrogation. In that case the user may be required to hand over the data by providing the password to unlock the device, violating confidentiality. Many existing proposals to address this issue involve the user lying to the interrogator to convince them that there is no data present, or that they forgot the password, or provide them with a second password that reveal different information. Although data hiding or alternative passwords can be useful solutions, we want to avoid this and instead focus on a scheme where the user can show that they cannot possibly access the data. In this paper we propose Nakula, a mechanism that enables a user to lock down data with a single click (or voice command, gesture, etc.), enabling secure data transport. The information remains confidential against a very strong adversary who has full control over both the network and the device; and has the ability to force the user to cooperate through coercion. Nakula is designed so that the user does not have to lie or provide any misleading information at all. To achieve this, the user temporarily loses the ability to access the data and will need a trusted third party to recover it. We present a detailed design and security analysis of Nakula, and a proof-of-concept implementation that demonstrates the feasibility of using standard mobile phones to carry data. Finally we discuss several context-specific authentication methods that can be used with the scheme to enable data recovery in a variety of situations.
Interpretations of the OSCAR data for reactive-gas scavenging

A description is given of the application of a reactive scavenging model for the interpretation of data from the Oxidation and Scavenging Characteristics of April Rains (OSCAR) field study to evaluate scavenging mechanisms. The OSCAR experiment, conducted during April 1982, was a cooperative field investigation of wet removal by cyclonic storms. A part of the experiment involved intensive measurements at a site in NE Indiana and was designed to provide needed inputs for diagnostic scavenging models. Sequential precipitation chemistry, surface and airborne air chemistry, cloud physics, and meteorological measurements were performed. The model application reported here involves a single storm event at the Indiana site. Although the work presented involves the analysis of only a single precipitation event over a limited geographical area (10/sup 4/ km/sup 2/), the data utilized have considerable uncertainties, and the model contains numerous approximations, it is nevertheless concluded that the ability of the model to reproduce much of the observed precipitation chemistry behavior for the event is quite encouraging.
Effectiveness of oestrogen pretreatment in patients with expected poor ovarian response (POSEIDON groups 3 and 4) undergoing GnRH antagonist protocol: study protocol for a randomised controlled trial

Introduction Women characterised by diminished ovarian reserve are considered to have poor ovarian response (POR) according to Patient-Oriented Strategies Encompassing IndividualizeD Oocyte Number (POSEIDON) criteria. Patients in this population often have a poor prognosis for treatment with assisted reproductive technology. In previous studies, oestrogen pretreatment before ovarian stimulation has been shown to have a beneficial effect. However, recent studies presented conflicting conclusions. This study aims to evaluate the effectiveness of oestrogen pretreatment in patients with expected POR (POSEIDON groups 3 and 4) undergoing gonadotrophin releasing hormone antagonist (GnRH-ant) protocol. Methods and analysis A prospective superiority randomised parallel controlled trial will be conducted at a tertiary university-affiliated hospital. A total of 316 patients will be randomly divided into two groups at a ratio of 1:1. In the intervention group, oral oestrogen pretreatment will be administered from day 7 after ovulation until day 2 of the next menstrual cycle. Afterwards, a flexible GnRH-ant protocol will be initiated. The control group will receive no additional intervention beyond routine ovarian stimulation. The primary outcome is the number of oocytes retrieved. Secondary outcomes include the total number of retrieved metaphase II oocytes, average daily dose of gonadotropin, total gonadotropin dose and duration of ovarian stimulation, cycle cancellation rate, top quality embryos rate, blastocyst formation rate, embryo implantation rate, clinical pregnancy rate, early miscarriage rate and endometrial thickness on trigger day. All data will be analysed according to the intention-to-treat and per-protocol principles. Ethics and dissemination The ethical approval has been confirmed by the reproductive ethics committee of the affiliated hospital of Shandong University of Traditional Chinese Medicine (SDUTCM/2022.9.20). In addition, written informed consent will be obtained from all the participants before the study. The results will be disseminated via publications. Trial registration number ChiCTR2200064812.