Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmenting the User-Item Graph with Textual Similarity Models

This paper introduces a simple and effective form of data augmentation for recommender systems. A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that are added to the user-item graph. This increases the density of the graph without needing further labeled data. The data augmentation is evaluated on a variety of recommendation algorithms, using Euclidean, hyperbolic, and complex spaces, and over three categories of Amazon product reviews with differing characteristics. Results show that the data augmentation technique provides significant improvements to all types of models, with the most pronounced gains for knowledge graph-based recommenders, particularly in cold-start settings, leading to state-of-the-art performance.
Network Refinement: A unified framework for enhancing signal or removing noise of networks

Networks are widely used in many fields for their powerful ability to provide vivid representations of relationships between variables. However, many of them may be corrupted by experimental noise or inappropriate network inference methods that inherently hamper the efficacy of network-based downstream analysis. Consequently, it’s necessary to develop systematic methods for denoising networks, namely, improve the Signal-to-Noise Ratio (SNR) of noisy networks. In this paper, we have explored the properties of network signal and noise and proposed a novel network denoising framework called Network Refinement (NR) that adjusts the edge weights by applying a nonlinear graph operator based on a diffusion process defined by random walk on the graph. Specifically, this unified framework consists of two closely linked approaches named NR-F and NR-B, which improve the SNR of noisy input networks from two different perspectives: NR-F aims at enhancing signal strength, while NR-B aims at weakening noise strength. Users can choose from which angle to improve the SNR of the network according to the characteristics of the network itself. We show that NR can significantly refine the quality of many networks by several applications on simulated networks and typical real-world biological and social networks.
Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 misinfodemic, it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer who constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.
DEM Super-Resolution with EfficientNetV2

Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly. Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. In an effort to rectify this problem, we propose and assess an EfficientNetV2 based model. The proposed model increases the spatial resolution of DEMs up to 16times without additional information.
The Flattening Metallicity Gradient in the Milky Way’s Thin Disk

We calculate the ages, orbits and phase-space coordinates for a sample of ∼4 million LAMOST and Gaia stars. The ages are cross-matched and compared with values from two other popular age catalogs, which derive the ages using different methods. Of these ∼4 million stars, we select a sample of 1.3 million stars and investigate their radial metallicity gradients (as determined by orbital radii) as a function of their ages. This analysis is performed on various subsets of the data split by chemistry and orbital parameters. We find that commonly used selections for “thin disk” stars (such as low-α chemistry or vertically thin orbits) yield radial metallicity gradients that generally grow shallower for the oldest stars. We interpret this as a hallmark feature of radial migration (churning). Constraining our sample to very small orbital Z max (the maximal height of a star’s integrated orbit) makes this trend most pronounced. A chemistry-based “thin disk” selection of α-poor stars displays the same trend, but to a lesser extent. Intriguingly, we find that “thick disk” selections in chemistry and Z max reveal slightly positive radial metallicity gradients, which seem similar in magnitude at all ages. This may imply that the thick disk population is well mixed in age, but not in radius. This finding could help constrain conditions during the early epochs of Milky Way formation and shed light on processes such as the accretion and reaccretion of gases of different metallicities.
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition

EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience studies on inter-subject correlation, we proposed a Contrastive Learning method for Inter-Subject Alignment (CLISA) to tackle the cross-subject emotion recognition problem. Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signkal representations across subjects when they received the same emotional stimuli in contrast to different ones. Specifically, a convolutional neural network was applied to learn inter-subject aligned spatiotemporal representations from EEG time series in contrastive learning. The aligned representations were subsequently used to extract differential entropy features for emotion classification. CLISA achieved state-of-the-art cross-subject emotion recognition performance on our THU-EP dataset with 80 subjects and the publicly available SEED dataset with 15 subjects. It could generalize to unseen subjects or unseen emotional stimuli in testing. Furthermore, the spatiotemporal representations learned by CLISA could provide insights into the neural mechanisms of human emotion processing.
Determinantal structures for Bessel fields

A Bessel field B = {B(α, t), α ∈ N0, t ∈ R} is a two-variable random field such that for every (α, t), B(α, t) has the law of a Bessel point process with index α. The Bessel fields arise as hard edge scaling limits of the Laguerre field, a natural extension of the classical Laguerre unitary ensemble. It is recently proved in [LW21] that for fixed α, {B(α, t), t ∈ R} is a squared Bessel Gibbsian line ensemble. In this paper, we discover rich integrable structures for the Bessel fields: along a time-like or a space-like path, B is a determinantal point process with an explicit correlation kernel; for fixed t, {B(α, t), α ∈ N0} is an exponential Gibbsian line ensemble.
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments.

Artificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simulations with almost ab initio accuracy. However, the simultaneous training of NNs on energies and forces, which are a prerequisite for, e.g., molecular dynamics simulations, can be demanding. In this work, we present an improved NN architecture based on the previous GM-NN model [Zaverkin V. ; Kästner, J. J. Chem. Theory Comput. 2020, 16, 5410-5421], which shows an improved prediction accuracy and considerably reduced training times. Moreover, we extend the applicability of Gaussian moment-based interatomic potentials to periodic systems and demonstrate the overall excellent transferability and robustness of the respective models. The fast training by the improved methodology is a prerequisite for training-heavy workflows such as active learning or learning-on-the-fly.
JobBERT: Understanding Job Titles through Skills

Job titles form a cornerstone of today's human resources (HR) processes. Within online recruitment, they allow candidates to understand the contents of a vacancy at a glance, while internal HR departments use them to organize and structure many of their processes. As job titles are a compact, convenient, and readily available data source, modeling them with high accuracy can greatly benefit many HR tech applications. In this paper, we propose a neural representation model for job titles, by augmenting a pre-trained language model with co-occurrence information from skill labels extracted from vacancies. Our JobBERT method leads to considerable improvements compared to using generic sentence encoders, for the task of job title normalization, for which we release a new evaluation benchmark.
Interest-oriented Universal User Representation via Contrastive Learning

User representation is essential for providing high-quality commercial services in industry. Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application. In this paper, we attempt to improve universal user representation from two points of views. First, a contrastive self-supervised learning paradigm is presented to guide the representation model training. It provides a unified framework that allows for long-term or short-term interest representation learning in a data-driven manner. Moreover, a novel multi-interest extraction module is presented. The module introduces an interest dictionary to capture principal interests of the given user, and then generate his/her interest-oriented representations via behavior aggregation. Experimental results demonstrate the effectiveness and applicability of the learned user representations.
Robot's Inner Speech Effects on Trust and Anthropomorphic Cues in Human-Robot Cooperation

Inner Speech is an essential but also elusive human psychological process which refers to an everyday covert internal conversation with oneself. We argue that programming a robot with an overt self-talk system, which simulates human inner speech, might enhance human trust by improving robot transparency and anthropomorphism. For this reasons, this work aims to investigate if robot's inner speech, here intended as overt self-talk, affects human trust and anthropomorphism when human and robot cooperate. A group of participants was engaged in collaboration with the robot. During cooperation, the robot talks to itself. To evaluate if the robot's inner speech influences human trust, two questionnaires were administered to each participant before (pre-test) and after (post-test) the cooperative session with the robot. Preliminary results evidenced differences between the answers of participants in the pre-test and post-test assessment, suggesting that robot's inner speech influences human trust. Indeed, participant's levels of trust and perception of robot anthropomorphic features increase after the experimental interaction with the robot.
Manifold-preserved GANs

Generative Adversarial Networks (GANs) have been widely adopted in various fields. However, existing GANs generally are not able to preserve the manifold of data space, mainly due to the simple representation of discriminator for the real/generated data. To address such open challenges, this paper proposes Manifold-preserved GANs (MaF-GANs), which generalize Wasserstein GANs into high-dimensional form. Specifically, to improve the representation of data, the discriminator in MaF-GANs is designed to map data into a high-dimensional manifold. Furthermore, to stabilize the training of MaF-GANs, an operation with precise and universal solution for any K-Lipschitz continuity, called Topological Consistency is proposed. The effectiveness of the proposed method is justified by both theoretical analysis and empirical results. When adopting DCGAN as the backbone on CelebA (256*256), the proposed method achieved 12.43 FID, which outperforms the state-of-the-art model like Realness GAN (23.51 FID) by a large margin. Code will be made publicly available.
IEEE 802.1AS Clock Synchronization Performance Evaluation of an Integrated Wired–Wireless TSN Architecture

Industrial control systems present numerous challenges from the communication systems perspective: clock synchronization, deterministic behavior, low latency, high reliability, flexibility, and scalability. These challenges are mostly solved with standard technologies over Ethernet, e.g., time-sensitive networking (TSN). As a research trend, it is expected that TSN will converge with wireless, leading to the Wireless TSN paradigm. Also, Wireless TSN is expected to be integrated with Ethernet TSN to create large-scale wired–wireless (Hybrid) TSN networks. The first step toward Hybrid TSN is the distribution of the clock reference from the wired to the wireless domain. In this article, we leverage existing Ethernet TSN and wireless technologies implementations (Wi-Fi and w-SHARP) and present two hardware architectures specifically engineered to enable the clock synchronization distribution among the network domains. The hardware architectures have been implemented over a system-on-chip field-programmable gate array platform. We demonstrate through several experiments that the implementation is able to fulfill the synchronization performance required by TSN.
UTXO in Digital Currencies: Account-based or Token-based? Or Both?

There are different interpretations of the terms “tokens” and “token-based systems” in the literature around blockchain and digital currencies although the distinction between token-based and account-based systems is well entrenched in economics. Despite the wide use of the terminologies of tokens and tokenisation in the cryptocurrency community, the underlying concept sometimes does not square well with the economic notions, or is even contrary to them. The UTXO design of Bitcoin exhibits partially characteristics of a token-based system and partially characteristics of an account-based system. A discussion on the difficulty to implement the economic notion of tokens in the digital domain, along with an exposition of the design of a UTXO, is given in order to discuss why UTXO-based systems should be viewed as account-based according to the classical economic notion. Besides, a detailed comparison between UTXO-based systems and account-based systems is presented. Using the data structure of the global/system state representation as the defining feature to distinguish digital token-based and account-based systems is therefore suggested. This extended definition of token-based systems covers both physical and digital tokens while neatly distinguishing tokenbased and account-based systems.
Job Scheduling in High Performance Computing

The ever-growing processing power of supercomputers in recent decades enables us to explore increasing complex scientific problems. Effective scheduling these jobs is crucial for individual job performance and system efficiency. The traditional job schedulers in high performance computing (HPC) are simple and concentrate on improving CPU utilization. The emergence of new hardware resources and novel hardware structure impose severe challenges on traditional schedulers. The increasing diverse workloads, including compute-intensive and data-intensive applications, require more efficient schedulers. Even worse, the above two factors interplay with each other, which makes scheduling problem even more challenging. In recent years, many research has discussed new scheduling methods to combat the problems brought by rapid system changes. In this research study, we have investigated challenges faced by HPC scheduling and state-of-art scheduling methods to overcome these challenges. Furthermore, we propose an intelligent scheduling framework to alleviate the problems encountered in modern job scheduling.
Unsupervised Continual Learning in Streaming Environments

A deep clustering network (DCN) is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This article presents an unsupervised approach of DCN construction on the fly via simultaneous deep learning and clustering termed autonomous DCN (ADCN). It combines the feature extraction layer and autonomous fully connected layer in which both network width and depth are self-evolved from data streams based on the bias-variance decomposition of reconstruction loss. The self-clustering mechanism is performed in the deep embedding space of every fully connected layer, while the final output is inferred via the summation of cluster prediction score. Furthermore, a latent-based regularization is incorporated to resolve the catastrophic forgetting issue. A rigorous numerical study has shown that ADCN produces better performance compared with its counterparts while offering fully autonomous construction of ADCN structure in streaming environments in the absence of any labeled samples for model updates. To support the reproducible research initiative, codes, supplementary material, and raw results of ADCN are made available in https://github.com/andriash001/AutonomousDCN.git
Functoriality for supercuspidal L-packets

Kaletha constructs $L$-packets for supercuspidal $L$-parameters of tame $p$-adic groups. These $L$-packets consist entirely of supercuspidal representations, which are explicitly described. Using the explicit descriptions, we show that Kaletha's $L$-packets satisfy a fundamental functoriality property desired for the Local Langlands Correspondence.
Discrete and continuum models for the coevolutionary dynamics between CD8+ cytotoxic T lymphocytes and tumour cells.

We present an individual-based model for the coevolutionary dynamics between CD8+ cytotoxic T lymphocytes (CTLs) and tumour cells. In this model, every cell is viewed as an individual agent whose phenotypic state is modelled by a discrete variable. For tumour cells this variable represents a parameterisation of the antigen expression profiles, while for CTLs it represents a parameterisation of the target antigens of T-cell receptors (TCRs). We formally derive the deterministic continuum limit of this individual-based model, which comprises a non-local partial differential equation for the phenotype distribution of tumour cells coupled with an integro-differential equation for the phenotype distribution of CTLs. The biologically relevant homogeneous steady-state solutions of the continuum model equations are found. The linear-stability analysis of these steady-state solutions is then carried out in order to identify possible conditions on the model parameters that may lead to different outcomes of immune competition and to the emergence of patterns of phenotypic coevolution between tumour cells and CTLs. We report on computational results of the individual-based model, and show that there is a good agreement between them and analytical and numerical results of the continuum model. These results shed light on the way in which different parameters affect the coevolutionary dynamics between tumour cells and CTLs. Moreover, they support the idea that TCR-tumour antigen binding affinity may be a good intervention target for immunotherapy and offer a theoretical basis for the development of anti-cancer therapy aiming at engineering TCRs so as to shape their affinity for cancer targets.
Spin and quadrupole correlations by three-spin interaction in the frustrated pyrochlore magnet 
Tb2+xTi2−xO7+y

We have investigated the origin of the magnetic dipole correlations 〈σ Qσ −Q〉 characterized by the modulation wave vector k ∼ ( 1 2 , 1 2 , 1 2 ) observed in the frustrated pyrochlore magnet Tb2+xTi2−xO7+y. This magnetic short-range order cannot be accounted for by adding furtherneighbor exchange interactions to the nearest-neighbor pseudospin1 2 Hamiltonian for quantum pyrochlore magnets. Using classical Monte Carlo simulation and quantum simulation based on thermally pure quantum (TPQ) states we have shown that the spin correlations with k ∼ ( 1 2 , 1 2 , 1 2 ) are induced at low temperatures by a three-spin interaction of a form σ± r σ z r′σ z r′′ , which is a correction to the Hamiltonian due to the low crystal-field excitation. Simulations using TPQ states have shown that the spin correlations coexist with electric quadrupole correlations 〈σ Qσβ −Q〉 (α, β = x, y) with k ∼ 0. These results suggest that the putative quantum spin liquid state of Tb2+xTi2−xO7+y is located close to phase boundaries of the spin-ice, quadrupole-ordered, and magnetic-ordered states in the classical approximation, and that the three-spin interaction brings about a quantum disordered ground state with both spin and quadrupole correlations.