Miracle-dz/UniFAR-FTUs huggingface dataset:
GNEM: A Generic One-to-Set Neural Entity Matching Framework

Entity Matching is a classic research problem in any data analytics pipeline, aiming to identify records referring to the same real-world entity. It plays an important role in data cleansing and integration. Advanced entity matching techniques focus on extracting syntactic or semantic features from record pairs via complex neural architectures or pre-trained language models. However, the performances always suffer from noisy or missing attribute values in the records. We observe that comparing one record with several relevant records in a collective manner allows each pairwise matching decision to be made by borrowing valuable insights from other pairs, which is beneficial to the overall matching performance. In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. GNEM predicts matching labels between one record and a set of relevant records simultaneously. It constructs a record pair graph with weighted edges and adopts the graph neural network to propagate information among pairs. We further show that GNEM can be interpreted as an extension and generalization of the existing pairwise matching techniques. Extensive experiments on real-world data sets demonstrate that GNEM consistently outperforms the existing pairwise entity matching techniques and achieves up to 8.4% improvement on F1-Score compared with the state-of-the-art neural methods.
Response Selection for a Virtual Counsellor

Chatbots are increasingly used for delivering mental health assistance. As part of our effort to develop a chatbot on academic and social issues for Cantonese-speaking students, we have constructed a dataset of 1,028 post-reply pairs on test anxiety and loneliness. The posts, harvested from Cantonese social media, are manually classified to a symptom category drawn from counselling literature; the replies are human-crafted, offering brief advice for each post. For response selection, the chatbot predicts the quality of a candidate post-reply pair with a regression model. During training, the symptom categories were used as proxies of reply relevance. In experiments, this approach improved response selection accuracy over a binary classification model and a weakly supervised regression model. This result suggests that manual annotation of symptom category can help boost the performance of a counsellor chatbot.
PolyU-CBS at the FinSim-2 Task: Combining Distributional, String-Based and Transformers-Based Features for Hypernymy Detection in the Financial Domain

In this contribution, we describe the systems presented by the PolyU CBS Team at the second Shared Task on Learning Semantic Similarities for the Financial Domain (FinSim-2), where participating teams had to identify the right hypernyms for a list of target terms from the financial domain. For this task, we ran our classification experiments with several distributional, string-based, and Transformer features. Our results show that a simple logistic regression classifier, when trained on a combination of word embeddings, semantic and string similarity metrics and BERT-derived probabilities, achieves a strong performance (above 90%) in financial hypernymy detection.
Sinkhorn Collaborative Filtering

Recommender systems play a vital role in modern web services. In a typical recommender system, we are given a set of observed user-item interaction records and seek to uncover the hidden behavioral patterns of users from these historical interactions. By exploiting these hidden patterns, we aim to discover users’ personalized tastes and recommend them new items. Among various types of recommendation methods, the latent factor collaborative filtering models have dominated the field. In this paper, we develop a unified view for the existing latent factor models from a probabilistic perspective. The unified framework enables us to discern the underlying connections of different latent factor models and deepen our understandings of their advantages and limitations. In particular, we observe that the loss functions adopted by the existing models are oblivious to the geometry induced by the item-similarity. To address this, we propose a novel model—SinkhornCF—based on Sinkhorn divergence. To address the challenge of the expensive computational cost of Sinkhorn divergence, we also propose new techniques to enable the resulting model to be able to scale to large datasets. Its effectiveness is verified on two real-world recommendation datasets.
Tweet-aware News Summarization with Dual-Attention Mechanism

Neural models have been applied to many text summarization tasks recently. In general, a large number of high quality reference summaries are required to train well-performing neural models. The reference summaries, i.e., ground truth, are usually written by human, and are costly to obtain. Thus, in this paper, we focus on unsupervised summarization problem by exploring news and readers’ comments in linking tweets, i.e., tweets with URLs linking to the news. Our data analysis shows that the linking tweets, collectively highlight important information in news but may not fully cover all content in news. This inspires us to propose the dual-attention based model, named DAS, to address the observed issues above. The dual-attention mechanism extracts both important information highlighted by linking tweets and the salient content in news. Specifically, it consists of two similar structures of Transformer with multi-head attention. We propose position-dependent word salience, which reflects the effect of local context. The word salience is computed from dual-attention mechanism. Sentence salience is then estimated from the word salience. Experimental results on a benchmark dataset show that DAS outperforms state-of-the-art unsupervised models and achieves comparable results with state-of-the-art supervised models.
Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading

Quantitative trading and investment decision making are intricate financial tasks in the ever-increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a limitation of most existing neural methods is that they treat stocks independent of each other, ignoring the valuable rich signals between related stocks’ movements. Motivated by financial literature that shows stock markets and inter-stock correlations show scale-free network characteristics, we leverage domain knowledge on the Web to model inter-stock relations as a graph in four major global stock markets and formulate stock selection as a scale-free graph-based learning to rank problem. To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. Our work’s key novelty is the proposal of modeling the complex, scale-free nature of inter-stock relations through temporal hyperbolic graph learning on Riemannian manifolds that can represent the spatial correlations between stocks more accurately. Through extensive experiments on long-term real-world data spanning over six years on four of the world’s biggest markets: NASDAQ, NYSE, TSE, and China exchanges, we show that HyperStockGAT significantly outperforms state-of-the-art stock forecasting methods in terms of profitability by over 12%, and risk-adjusted Sharpe Ratio by over 4%. We analyze HyperStockGAT’s components’ contributions through a series of exploratory and ablative experiments to demonstrate its practical applicability to real-world trading. Furthermore, we propose a novel hyperbolic architecture that can be applied across various spatiotemporal problems on the Web’s commonly occurring scale-free networks.
A Novel Macro-Micro Fusion Network for User Representation Learning on Mobile Apps

The evolution of mobile apps has greatly changed the way that we live. It becomes increasingly important to understand and model the users on mobile apps. Instead of focusing on some specific app alone, it has become a popular paradigm to study the user behavior on various mobile apps in a symbiotic environment. In this paper, we study the task of user representation learning with both macro and micro interaction data on mobile apps. Specifically, macro and micro interaction refer to user-app interaction or user-item interaction on some specific app, respectively. By combining the two kinds of user data, it is expected to derive a more comprehensive, robust user representation model on mobile apps. In order to effectively fuse the information across the macro and micro views, we propose a novel macro-micro fusion network for user representation learning on mobile apps. With a Transformer architecture as the base model, we design a representation fusion component that is able to capture the category-based semantic alignment at the user level. After such semantic alignment, the information across the two views can be adaptively fused in our approach. Furthermore, we adopt mutual information maximization to derive a self-supervised loss to enhance the learning of our fusion network. Extensive experiments with three downstream tasks on two real-world datasets have demonstrated the effectiveness of our approach.
Surrounded by the Clouds: A Comprehensive Cloud Reachability Study

In the early days of cloud computing, datacenters were sparsely deployed at distant locations far from end-users with high end-to-end communication latency. However, today’s cloud datacenters have become more geographically spread, the bandwidth of the networks keeps increasing, pushing the end-users latency down. In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. We leverage the well-known measurement platform RIPE Atlas, involving up to 8500 probes deployed in heterogeneous environments, e.g., home and offices. Our goal is to evaluate the suitability of modern cloud environments for various current and predicted applications. We achieve this by comparing our latency measurements against known human perception thresholds and are able to draw inferences on the suitability of current clouds for novel applications, such as augmented reality. Our results indicate that the current cloud coverage can easily support several latency-critical applications, like cloud gaming, for the majority of the world’s population.
The burden of excessive saturated fatty acid intake attributed to ultra-processed food consumption: a study conducted with nationally representative cross-sectional studies from eight countries

Abstract Cross-sectional nutritional survey data collected in eight countries were used to estimate saturated fatty acid intakes. Our objective was to estimate the proportion of excessive saturated fatty acid intakes (>10 % of total energy intake) that could be avoided if ultra-processed food consumption was reduced to levels observed in the first quintile of each country. Secondary analysis was performed of 24 h dietary recall or food diary/record data collected by the most recently available nationally representative cross-sectional surveys carried out in Brazil (2008–9), Chile (2010), Colombia (2005), Mexico (2012), Australia (2011–12), the UK (2008–16), Canada (2015) and the US (2015–16). Population attributable fractions estimated the impact of reducing ultra-processed food consumption on excessive saturated fatty acid intakes (above 10 % of total energy intake) in each country. Significant relative reductions in the percentage of excessive saturated fatty acid intakes would be observed in all countries if ultra-processed food consumption was reduced to levels observed in the first quintile's consumption. The reductions in excessive intakes ranged from 10⋅0 % (95 % CI 6⋅2–13⋅6 %) in Canada to 35⋅0 % (95 % CI 28⋅7–48⋅0 %) in Mexico. In all eight studied countries, all presenting more than 30 % of intakes with excessive saturated fatty acids, lowering the dietary contribution of ultra-processed foods to attainable, context-specific levels was shown to be a potentially effective way to reduce the percentage of intakes with excessive saturated fatty acids, which may play an important role in the prevention of non-communicable diseases, particularly cardiovascular diseases.
Rayleigh–Taylor instability of cylindrical water droplet induced by laser-produced cavitation bubble

Abstract This study gives insights into the interfacial instability of water droplets through a combination of laboratory experiments, numerical simulations and analytical modelling. An experiment is conducted in a narrow gap between two plates to model the two-dimensional cylindrical geometry, and a pulsed laser beam is focused inside a water droplet to generate a cavitation bubble. Three distinct characteristics of droplet deformation can be observed: (i) splashing; (ii) ventilating; and (iii) a stable state. In addition, an analytical model considering the Rayleigh–Taylor instability and bubble oscillation is developed based on the assumption that fluid is inviscid and incompressible. The analytical model is solved to obtain a phase diagram describing three distinct phenomena. Two dimensionless parameters, $\hat {\eta }_{ins}$ and $\hat {\eta }_{sta}$, are used to determine the boundaries between different regimes. The parameter $\hat {\eta }_{ins}$ is defined by the ratio of the perturbation amplitude to the difference between the droplet and bubble radii, whereas the other parameter $\hat {\eta }_{sta}$ is defined by the ratio of the perturbation amplitude to the initial droplet radius. Interfacial instability is induced when the perturbed droplet surface penetrates the bubble boundary, namely, $\rvert \hat {\eta }_{ins} \rvert \geq 1$. Splashing and ventilating phenomena occur for $\hat {\eta }_{ins}\geq 1$ and $\hat {\eta }_{ins}\leq -1$, respectively. A stable state occurs when droplet fragmentation does not appear despite small-amplitude perturbations for $\hat {\eta }_{sta}\leq 0.1$. There is a transition zone between the ventilating and stable state, which is bounded by $\hat {\eta }_{ins} = -1$ and $\hat {\eta }_{sta} = 0.1$. Finally, the phase diagram is verified by the experimental and numerical results.
Settlement Patterns and Urbanization in the Yautepec Valley of Central Mexico

Abstract We carried out a full-coverage survey of the Yautepec Valley in the 1990s to reconstruct demography and settlements and their changes through time. We investigated the extent to which well-documented developments in the adjacent Basin of Mexico were paralleled in Yautepec, as well as the impact of regional empires and economies on local society. Our analyses focused on Teotihuacan relations in the Classic period and relations with the Aztec empire and the Mesoamerican world system in the Middle and Late Postclassic periods. In addition to locating, mapping, and describing sites and taking grab-bag artifact collections, we also made a series of systematic intensive surface collections (5 × 5 m) and test excavations at samples of Classic and Postclassic sites. In this paper, we describe the survey and changing settlement patterns in the Yautepec Valley. We also present several analyses of changing patterns of urbanization through the Prehispanic era. We conclude with a synthesis of changing social and cultural dynamics in this region.
From ancient pigments to modern optoelectronic applications of arsenic sulfides: bonazziite, the natural analogue of β-As4S4 from Khaidarkan deposit, Kyrgyzstan

Abstract Bonazziite is a new mineral from Khaidarkan deposit, Kyrgyzstan and represents the natural analogue of the β-form of the well known As4S4 compound. It occurs as rare crystals up to 100 mm across associated with realgar, sulfur, wakabayashilite, alacra’nite, non-stoichiometric As4S4+x sulfides and stibnite in a calcite matrix. In thick section, bonazziite is opaque with a resinous lustre and a dark-orange streak. It is brittle; the Vickers hardness (VHN15) is 70 kg/mm2 (range: 60-76) (Mohs hardness of ~2½). In planepolarized incident light, bonazziite is strongly bireflectant and pleochroic from orange to light red. The mineral shows orange to red internal reflections. Between crossed polars, the mineral is strongly anisotropic with greyish to light-blue rotation tints. Reflectance percentages in air for Rmin and Rmax are 19.9, 22.2 (471.1 nm), 19.1, 21.3 (548.3 nm), 18.8, 19.7 (586.6 nm) and 17.8, 18.9 (652.3 nm), respectively. Bonazziite is monoclinic, space group C2/c, with a = 9.956(1), b = 9.308(1), c = 8.869(1) Å, b = 102.55(2)° and V = 802.3(2) Å3, Z = 4. The crystal structure [R1 = 0.0263 for 735 reflections with Fo > 4σ(Fo)] is based on the As4S4 cage-like molecule, in which each As atom links one As and two S atoms. The As4S4 molecule is identical to that found in the structure of realgar. The six strongest powder diffraction lines [d in Å (I/I0) (hkl)] are: 5.74 (100) (1̄11); 4.10 (60) (021); 3.92 (50) (1̄12); 3.12 (60) (022, 310); 2.95 (50) (221, 202); 2.86 (80) (2̄22, 1,31). A mean of six electron microprobe analyses gave the formula As3.95S4. 05, on the basis of eight atoms. The new mineral has been approved by the International Mineralogical Association Commission on New Minerals, Nomenclature and Classification (IMA No. 2013-141) and named for Paola Bonazzi, in recognition of her seminal contributions to the study of arsenic sulfides and their alteration induced by exposure to light.
Distinct phosphorylation signals drive acceptor versus self-ubiquitination selection by Parkin

The RBR E3 ligase parkin is recruited to the outer mitochondrial membrane (OMM) during oxidative stress where it becomes activated and ubiquitinates numerous proteins. Parkin activation involves binding of a phosphorylated ubiquitin (pUb), followed by phosphorylation of parkin itself, both mediated by the OMM kinase, PINK1. However, targeted mitochondrial proteins have little structural or sequence similarity, with the commonality between substrates being proximity to the OMM. Here, we demonstrate that parkin efficiently ubiquitinates a mitochondrial acceptor pre-ligated to pUb and phosphorylation of parkin triggers autoubiquitination activity. Mitochondrial target proteins, Miro1 or CISD1, tethered to pUb are ubiquitinated by parkin more efficiently than if alone or Ub-tethered and ubiquitin molecules are ligated to acceptor protein lysines and not pUb. Parkin phosphorylation is not required for acceptor-pUb ubiquitination. In fact, only phospho-parkin induced self-ubiquitination and deletion of Ubl or mutation at K211N inhibited self-ubiquitination. We propose divergent parkin mechanisms whereby parkin-mediated ubiquitination of acceptor proteins is driven by binding to pre-existing pUb and subsequent parkin phosphorylation triggers autoubiquitination. This finding is critical for understanding parkin’s role in mitochondrial homeostasis and has implications on targets for therapeutics.
The liverwort Marchantia polymorpha operates a depolarization-activated Slowpoke (SLO) K+ channel that recognises pH changes in the environment

Voltage-dependent ion channels are a prerequisite for cellular excitability and electrical communication – important traits for multicellular organisms to thrive in a changeable terrestrial environment. Based on their presence in extant embryophytes and closely-related green algae, the first plants to survive on land likely possessed genes encoding channels with homology to large-conductance calcium-activated K+ channels (BK channels from the Slo family) in addition to primary voltage-gated potassium channels from the plant VG-type family (Shaker or Kv channels). While the function and gating of Shaker channels has been characterised in flowering plants, so far knowledge of BK channels has been limited to animal models. In humans, BK-mediated K+ efflux has a critical role in sperm motility and membrane polarisation to enable fertilisation. In the liverwort Marchantia polymorpha, the MpBK2a channel gene is most highly expressed in male reproductive tissue, suggesting that these channels may function in sexual reproduction. We characterised MpBK2a channels and found them to be strongly K+-selective, outward-rectifying, 80-pS channels capable of repolarising the membrane after stimulus-dependent depolarisation. In contrast to its animal counterpart, MpBK2a is insensitive to cytoplasmic Ca2+ variations but effectively gated by pH changes. Given that this plant BK channel is active even in the presence of trace amounts of external K+ and at low pH, the liverwort channel could have stabilised the membrane potential under stressful pre-historic conditions including nutrient-depleted and acid environments as early plant pioneers conquered land.
Analysis of 56K genomes identifies the relationship between antibiotic and metal resistance co-Occurrence and the spread of multidrug-resistant non-typhoidal Salmonella

Salmonella enterica is common foodborne pathogen that generates both enteric and systemic infections in hosts. Antibiotic resistance is common is certain serovars of the pathogen and of great concern to public health. Recent reports have documented the co-occurrence of metal resistance with antibiotic resistance in one serovar of S. enterica. Therefore, we sought to identify possible co-occurrence in a large genomic dataset. Genome assemblies of 56,348 strains of S. enterica comprising 20 major serovars were downloaded from NCBI. The downloaded assemblies were quality controlled and in silico serotyped to ensure consistency and avoid improper annotation from public databases. Metal and antibiotic resistance genes were identified in the genomes as well as plasmid replicons. Co-current genes were identified by constructing a co-occurrence matrix and group the genes using k-means clustering. Three groups of co-occurrent genes were identified using k-means clustering. Group 1 was comprised of the pco and sil operons that confer resistance to copper and silver respectively. Group 1 was distributed across four serovars. Group 2 contained the majority of the genes and little to no co-occurrence was observed. Metal and antibiotic co-occurrence was identified in group 3 that contained genes conferring resistance to: arsenic, mercury, beta-lactams, sulfonamides, and tetracyclines. Group 3 genes were also associated with an IncQ1 class plasmid replicon. Metal and antibiotic cooccurrence was isolated to one clade of S. enterica I 4,[5],12:i:.