Miracle-dz/UniFAR-FTUs huggingface dataset:
Switch-based Hybrid Beamforming for Wideband Multi-carrier Communications

Switch-based hybrid beamforming (SW-HBF) architectures are promising for realizing massive multiple-input multiple-output (MIMO) communications systems because of their low cost and low power consumption. In this paper, we study the performance of SW-HBF in a wideband multi-carrier MIMO communication system considering the beam squint effect. We aim at designing the switch-based combiner that maximizes the system spectral efficiency (SE). However, the design problem is challenging because the analog combing matrix elements are binary variables. To overcome this, we propose tabu search-based (TS) SW-HBF schemes that can attain near-optimal performance with reasonable computational complexity. Furthermore, we compare the total power consumption and energy efficiency (EE) of the SW-HBF architecture to those of the phase-shifter-based hybrid beamforming (PS-HBF) architecture. Numerical simulations show that the proposed algorithms can efficiently find near-optimal solutions. Moreover, the SW-HBF scheme can significantly mitigate the beam squint effect and is less affected by the number of subcarriers than PS-HBF. It also provides improved SE and EE performance compared to PS-HBF schemes.
Third quantization for scalar and spinor wave functions of the Universe in an extended minisuperspace

We consider the third quantization in quantum cosmology of a minisuperspace extended by the Eisenhart–Duval lift. We study the third quantization based on both Klein–Gordon type and Dirac-type equations in the extended minisuperspace. Spontaneous creation of ‘Universes’ is investigated upon the quantization of a simple model. We find that the quantization of the Dirac-type wave function reveals that the number density of universes is expressed by the Fermi–Dirac distribution. We also calculate the entanglement entropy of the multi-universe system.
Global regularity of non-diffusive temperature fronts for the 2D viscous Boussinesq system

In this paper we address the temperature patch problem of the 2D viscous Boussinesq system without heat diﬀusion term. The temperature satisﬁes the transport equation and the initial data of temperature is given in the form of non-constant patch, usually called the temperature front initial data. Introducing a good unknown and applying the method of striated estimates, we prove that our partially viscous Boussinesq system admits a unique global regular solution and the initial C k,γ and W 2 , ∞ regularity of the temperature front boundary with k ∈ Z + = { 1 , 2 , · · · } and γ ∈ (0 , 1) will be preserved for all the time. In particular, this naturally extends the previous work by Danchin & Zhang (2017) and Gancedo & Garc´ıa-Ju´arez (2017). In the proof of the persistence result of higher boundary regularity, we introduce the striated type Besov space B s,(cid:96)p,r,W ( R d ) and establish a series of reﬁned striated estimates in such a function space, which may have its own interest.
Unsupervised Representation Learning for 3D Point Cloud Data

—Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervised learning of point cloud data has received much less attention to date. In this paper, we propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. They make up a pair. After going through a shared encoder and a shared head network, the consistency between the output representations are maximized with introducing two variants of contrastive losses to respectively facilitate downstream classiﬁcation and segmentation. To demonstrate the efﬁcacy of our method, we conduct experiments on three downstream tasks which are 3D object classiﬁcation (on ModelNet40 and ModelNet10), shape part segmentation (on ShapeNet Part dataset) as well as scene segmentation (on S3DIS). Comprehensive results show that our unsupervised contrastive representation learning enables impressive outcomes in object classiﬁcation and semantic segmentation. It generally outperforms current unsupervised methods, and even achieves comparable performance to supervised methods. Our source codes will be made publicly available.
SSSNET: Semi-Supervised Signed Network Clustering

Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited. In particular, most state-of-the-art methods generating node embeddings of signed networks focus on link sign prediction, and those that pertain to node clustering are usually not graph neural network (GNN) methods. Here, we introduce a novel probabilistic balanced normalized cut loss for training nodes in a GNN framework for semi-supervised signed network clustering, called SSSNET. The method is end-to-end in combining embedding generation and clustering without an intermediate step; it has node clustering as main focus, with an emphasis on polarization effects arising in networks. The main novelty of our approach is a new take on the role of social balance theory for signed network embeddings. The standard heuristic for justifying the criteria for the embeddings hinges on the assumption that"an enemy's enemy is a friend". Here, instead, a neutral stance is assumed on whether or not the enemy of an enemy is a friend. Experimental results on various data sets, including a synthetic signed stochastic block model, a polarized version of it, and real-world data at different scales, demonstrate that SSSNET can achieve comparable or better results than state-of-the-art spectral clustering methods, for a wide range of noise and sparsity levels. SSSNET complements existing methods through the possibility of including exogenous information, in the form of node-level features or labels.
A Framework for Verification of Wasserstein Adversarial Robustness

Machine learning image classifiers are susceptible to adversarial and corruption perturbations. Adding imperceptible noise to images can lead to severe misclassifications of the machine learning model. Using $L_p$-norms for measuring the size of the noise fails to capture human similarity perception, which is why optimal transport based distance measures like the Wasserstein metric are increasingly being used in the field of adversarial robustness. Verifying the robustness of classifiers using the Wasserstein metric can be achieved by proving the absence of adversarial examples (certification) or proving their presence (attack). In this work we present a framework based on the work by Levine and Feizi, which allows us to transfer existing certification methods for convex polytopes or $L_1$-balls to the Wasserstein threat model. The resulting certification can be complete or incomplete, depending on whether convex polytopes or $L_1$-balls were chosen. Additionally, we present a new Wasserstein adversarial attack that is projected gradient descent based and which has a significantly reduced computational burden compared to existing attack approaches.
Local controllability does imply global controllability

We say that a control system is locally controllable if the attainable set from any state $x$ contains an open neighborhood of $x$, while it is controllable if the attainable set from any state is the entire state manifold. We show in this note that a control system satisfying local controllability is controllable. Our self-contained proof is alternative to the combination of two previous results by Kevin Grasse.
A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling

The current state-of-the-art in user mobility research has extensively relied on open-source mobility traces captured from pedestrian and vehicular activity through a variety of communication technologies as users engage in a wide-range of applications, including connected healthcare, localization, social media, e-commerce, etc. Most of these traces are feature-rich and diverse, not only in the information they provide, but also in how they can be used and leveraged. This diversity poses two main challenges for researchers and practitioners who wish to make use of available mobility datasets. First, it is quite difficult to get a bird's eye view of the available traces without spending considerable time looking them up. Second, once they have found the traces, they still need to figure out whether the traces are adequate to their needs. The purpose of this survey is three-fold. It proposes a taxonomy to classify open-source mobility traces including their mobility mode, data source and collection technology. It then uses the proposed taxonomy to classify existing open-source mobility traces and finally, highlights three case studies using popular publicly available datasets to showcase how our taxonomy can tease out feature sets in traces to help determine their applicability to specific use-cases.
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expert data in a contextual MDP setup. We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup. We then discuss the problem of distribution shift between the expert data and the online environment when the data is only partially observable. We prove possibility and impossibility results for imitation learning under arbitrary distribution shift of the missing covariates. When additional external reward is provided, we propose a sampling procedure that addresses the unknown shift and prove convergence to an optimal solution. Finally, we validate our claims empirically on challenging assistive healthcare and recommender system simulation tasks.
Fine-Grained Style Control In Transformer-Based Text-To-Speech Synthesis

In this paper, we present a novel architecture to realize fine-grained style control on the transformer-based text-to-speech synthesis (TransformerTTS). Specifically, we model the speaking style by extracting a time sequence of local style tokens (LST) from the reference speech. The existing content encoder in TransformerTTS is then replaced by our designed cross-attention blocks for fusion and alignment between content and style. As the fusion is performed along with the skip connection, our cross-attention block provides a good inductive bias to gradually infuse the phoneme representation with a given style. Additionally, we prevent the style embedding from encoding linguistic content by randomly truncating LST during training and using wav2vec 2.0 features. Experiments show that with fine-grained style control, our system performs better in terms of naturalness, intelligibility, and style transferability. Our code and samples are publicly available.1
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al. 2019, 2021). Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the graph updated by previously predicted high-confident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model (Yin et al. 2019, 2021) on the basis of final graph. Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model. Particularly, when equipped with BERT (Devlin et al. 2019) and FHDecoder (Yin et al. 2020), our model achieves state-of-the-art performance. Our code is available at https://github.com/DeepLearnXMU/IRSEG.
Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.
Fourier-domain transfer entropy spectrum

We propose the Fourier-domain transfer entropy spectrum, a novel generalization of transfer entropy, as a model-free metric of causality. For arbitrary systems, this approach systematically quantifies the causality among their different system components rather than merely analyze systems as entireties. The generated spectrum offers a rich-information representation of time-varying latent causal relations, efficiently dealing with non-stationary processes and high-dimensional conditions. We demonstrate its validity in the aspects of parameter dependence, statistic significance test, and sensibility. An open-source multi-platform implementation of this metric is developed and computationally applied on neuroscience data sets and diffusively coupled logistic oscillators.
Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue

We demonstrate that large language models are able to simulate Task Oriented Dialogues in novel domains, provided only with an API implementation and a list of goals. We show these simulations can formulate online, automatic metrics that correlate well with human evaluations. Furthermore, by checking for whether the User's goals are met, we can use simulation to repeatedly generate training data and improve the quality of simulations themselves. With no human intervention or domain-specific training data, our simulations bootstrap end-to-end models which achieve a 37\% error reduction in previously unseen domains. By including as few as 32 domain-specific conversations, bootstrapped models can match the performance of a fully-supervised model with $10\times$ more data. To our knowledge, this is the first time simulations have been shown to be effective at bootstrapping models without explicitly requiring any domain-specific training data, rule-engineering, or humans-in-the-loop.
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the area of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose SurveyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A real-time Reading Path Generation (RePaGer) system has been also implemented with our designed model. Our source code and SurveyBank dataset can be found here11https://github.com/JiayuanDing100/Reading-Path-Generation.
Galaxy cluster strong lensing cosmography. Cosmological constraints from a sample of regular galaxy clusters

Cluster strong lensing cosmography is a promising probe of the background geometry of the Universe and several studies have emerged, thanks to the increased quality of observations using space and ground-based telescopes. For the first time, we use a sample of five cluster strong lenses to measure the values of cosmological parameters and combine them with those from classical probes. In order to assess the degeneracies and the effectiveness of strong-lensing cosmography in constraining the background geometry of the Universe, we adopt four cosmological scenarios. We find good constraining power on the total matter density of the Universe ($\Omega_{\rm m}$) and the equation of state of the dark energy parameter $w$. For a flat $w$CDM cosmology, we find $\Omega_{\rm m} = 0. 30_{-0.11}^{+0.09}$ and $w=-1. 12_{-0.32}^{+0.17}$ from strong lensing only. Interestingly, we show that the constraints from the Cosmic Microwave Background (CMB) are improved by factors of 2.5 and 4.0 on $\Omega_{\rm m}$ and $w$, respectively, when combined with our posterior distributions in this cosmological model. In a scenario where the equation of state of dark energy evolves with redshift, the strong lensing constraints are compatible with a cosmological constant (i.e. $w=-1$). In a curved cosmology, our strong lensing analyses can accommodate a large range of values for the curvature of the Universe of $\Omega_{\rm k}=0. 28_{-0.21}^{+0.16}$. In all cosmological scenarios, we show that our strong lensing constraints are complementary and in good agreement with measurements from the CMB, baryon acoustic oscillations and Type Ia supernovae. Our results show that cluster strong lensing cosmography is a potentially powerful probe to be included in the cosmological analyses of future surveys.
Incremental Community Detection in Distributed Dynamic Graph

Community detection is an important research topic in graph analytics that has a wide range of applications. A variety of static community detection algorithms and quality metrics were developed in the past few years. However, most real-world graphs are not static and often change over time. In the case of streaming data, communities in the associated graph need to be updated either continuously or whenever new data streams are added to the graph, which poses a much greater challenge in devising good community detection algorithms for maintaining dynamic graphs over streaming data. In this paper, we propose an incremental community detection algorithm for maintaining a dynamic graph over streaming data. The contributions of this study include (a) the implementation of a Distributed Weighted Community Clustering (DWCC) algorithm, (b) the design and implementation of a novel Incremental Distributed Weighted Community Clustering (IDWCC) algorithm, and (c) an experimental study to compare the performance of our IDWCC algorithm with the DWCC algorithm. We validate the functionality and efficiency of our framework in processing streaming data and performing large in-memory distributed dynamic graph analytics. The results demonstrate that our IDWCC algorithm performs up to three times faster than the DWCC algorithm for a similar accuracy.
Detection Software Content Failures using Dynamic Execution Information

Modern software systems become more and more complex, which makes them difficult to test and validate. Detecting software partial failures in complex systems at runtime assist to handle software unintended behaviors, avoiding catastrophic software failures and improving software runtime availability. These detection techniques aim to find the manifestation of faults before they finally lead to unavoidable failures, thus supporting following runtime fault-tolerant techniques. We review the state-of-the-art articles and find that the content failures account for the majority of all kinds of software failures, but its detection methods are rarely studied. In this work, we propose a novel failure detection indicator based on the software runtime dynamic execution information for software content failures. The runtime information is recorded during software execution, then transformed to a measure named runtime entropy and finally fed into decision tree models. The machine-learning models are built to classify the intended and unintended behaviors of the objected software systems. A series of controlled experiments on several open-source projects are conducted to prove the feasibility of the method. We also evaluate the accuracy of machine-learning models built in this work.
Durable superoleophobic–superhydrophilic fabrics with high anti-oil-fouling property

Although a number of methods have been reported for the preparation of superoleophobic–superhydrophobic surfaces, a challenge still remains in preparing a surface showing simultaneous superoleophobicity and superhydrophilicity. Herein, we demonstrate a novel strategy for preparing a simultaneously superhydrophilic–superoleophobic surface on cotton fabrics. A wet chemical coating method was employed to apply an oligomer which consists of a fluorinated alkyl and a PEG-phosphate hydrophilic moiety, silica nanoparticles and fluoroalkyl silane, onto fabric substrates. The treated fabrics exhibited both superoleophobicity and superhydrophilicity with a contact angle over 150° for oil fluids (surface tension > 27 mN m−1) but 0° for water. Water can spread into the fabric matrix within 2 seconds. The superhydrophilic–superoleophobic fabric had excellent superoleophobicity no matter whether it was at the dry state in air, pre-wetted with water, or in underwater conditions. When being contaminated forcedly with oil or oil-free dirt, the fabric can be easily cleaned up with water without using any detergent and chemical agent. Superoleophobic–superhydrophilic surfaces may provide an alternative and feasible way for anti-fouling applications.
Path-Free Decomposition for Direct, Indirect and Interaction Effects in Mediation Analysis

Given a binary treatment and a binary mediator, mediation analysis decomposes the total effect of the treatment on an outcome variable into direct and indirect effects. However, the existing decompositions are “path-dependent”, and consequently, there appeared different versions of direct and indirect effects. Differently from these, this paper proposes a “path-free” decomposition of the total effect into three sub-effects: direct, indirect, and treatment-mediator interaction effects. Whereas the interaction effect has been part of the indirect effect in the existing two-effect decompositions, it is separately identified in our three-effect decomposition. All effects are found using conditional means, but not conditional densities, and are estimated with ordinary least squares estimators. Simulation and empirical analyses are provided as well.