Miracle-dz/UniFAR-FTUs huggingface dataset:
Compositional Generalization via Semantic Tagging

Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they struggle to perform well on query-based data splits that require \emph{composition generalization}, an ability of systematically generalizing to unseen composition of seen components. Motivated by the explicitly built-in compositionality in traditional statistical semantic parsing, we propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models while featuring explicit lexicon-style alignments and disentangled information processing. Specifically, we decompose decoding into two phases where an input utterance is first tagged with semantic symbols representing the meanings of its individual words, and then a sequence-to-sequence model is used to predict the final meaning representation conditioning on the utterance and the predicted tag sequence. Experimental results on three semantic parsing datasets with query-based splits show that the proposed approach consistently improves compositional generalization of sequence-to-sequence models across different model architectures, domains and semantic formalisms.
Joint Power Allocation and User Association Optimization for IRS-Assisted mmWave Systems

Intelligent reflecting surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider a multi-IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impact of IRS on user association in multi-BS mmWave systems, we formulate a sum rate maximization problem by jointly optimizing passive beamforming at IRS, power allocation and user association. This leads to an intractable non-convex problem, for which to tackle we propose a computationally affordable iterative algorithm, capitalizing on alternating optimization, sequential fractional programming (SFP) and forward-reverse auction (FRA). In particular, passive beamforming at IRS is optimized by utilizing the SFP method, power allocation is solved through means of standard convex optimization method, and user association is handled by the network optimization based FRA algorithm. Simulation results demonstrate that the proposed algorithm can achieve significant performance gains, e.g., it can provide up to 147% higher sum rate compared with the benchmark and 116% higher energy efficiency compared with amplify-and-forward relay.
Measure Transport with Kernel Stein Discrepancy

Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler divergence (KLD) from the posterior to the approximation. The KLD is a strong mode of convergence, requiring absolute continuity of measures and placing restrictions on which transport maps can be permitted. Here we propose to minimise a kernel Stein discrepancy (KSD) instead, requiring only that the set of transport maps is dense in an $L^2$ sense and demonstrating how this condition can be validated. The consistency of the associated posterior approximation is established and empirical results suggest that KSD is competitive and more flexible alternative to KLD for measure transport.
STAR: A Schema-Guided Dialog Dataset for Transfer Learning

We present STAR, a schema-guided task-oriented dialog dataset consisting of 127,833 utterances and knowledge base queries across 5,820 task-oriented dialogs in 13 domains that is especially designed to facilitate task and domain transfer learning in task-oriented dialog. Furthermore, we propose a scalable crowd-sourcing paradigm to collect arbitrarily large datasets of the same quality as STAR. Moreover, we introduce novel schema-guided dialog models that use an explicit description of the task(s) to generalize from known to unknown tasks. We demonstrate the effectiveness of these models, particularly for zero-shot generalization across tasks and domains.
Evaluation of the Diagnostic Efficacy of Xpert CT/NG for Chlamydia trachomatis and Neisseria gonorrhoeae

Background Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) are widely spread across the world. Asymptomatic or inconspicuous CT/NG infections are difficult to diagnose and treat. Traditional methods have the disadvantages of low detection rate, inaccurate results, and long detection time. However, Xpert CT/NG makes up for the aforementioned shortcomings and has research value and popularization significance. Methods PubMed, Embase, Cochrane Library, and Web of Science were systematically searched, and studies were screened using Xpert CT/NG for diagnosing CT/NG. QUADAS-2 was used to evaluate the quality of the eligible studies. Then, two groups of researchers independently extracted data from these studies. Meta-analyses of sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve were conducted using Meta-DiSc 1.4. Finally, Deek's funnel plots were made using Stata 12.0 to evaluate publication bias. Results 14 studies were identified, and 46 fourfold tables were extracted in this meta-analysis. The pooled SEN, SPE, PLR, NLR, DOR, and AUC in diagnosing CT were 0.94 (95% confidence interval (CI): 0.93–0.95), 0.99 (95% CI: 0.99–1.00), 97.17 (95% CI: 56.76–166.32), 0.07 (95% CI: 0.04–0.12), 1857.25 (95% CI: 943.78–3654.86), and 0.9960, respectively. The pooled SEN, SPE, PLR, NLR, DOR, and AUC in diagnosing NG were 0.95 (95% CI: 0.93–0.96), 1.00 (95% CI: 1.00–1.00), 278.15 (95% CI: 152.41–507.63), 0.08 (95% CI: 0.06–0.12), 4290.70 (95% CI: 2161.78–8516.16), and 0.9980, respectively. Conclusions Xpert CT/NG had high diagnostic sensitivity and specificity for CT and NG. However, more evidence is required to confirm that Xpert CT/NG might serve as the primary method for detecting CT and NG and even the gold standard for diagnosis in the future.
Biophysical effect of climate change on summer crops

The effect of climate change on yield of soybean and maize crops was determined in Azul, Buenos Aires, Argentina. DSSAT crop models, previously calibrated, were used, under local conditions. Projections of PRECIS regional climate model in the SRESA2 stage were used, for the decades 2020-29 and 2050-59, in order to obtain potential yields. Later, the effect of heat waves during the growth cycle 2013-14 was evaluated. Climate projections indicated an increase of yields of crops during dryland farming crops. In soybean (Glycine max (L.) Merr. ), the increase was 25 and 28 %, and in maize (Zea mays), it was 19 % and 26 %, for the decades 2020-29 and 2050-59, respectively. However, considering thermal variability imposed by the heat wave, it was evident that soybean and maize showed a reduction of yield when they are sown without irrigation during those extreme events. Yield decreases reach 19 % in soybean and 36 % in maize. Comparing yields of crops irrigated in a year considered as typical with respect of the year of heat wave, the increase of temperature benefits maize with a 22 % of yield increase, but harms soybean with a decrease of 13 %. Influence of temperature increase, together with water deficiencies, produces different effects on C3 species like soybean, and in C4 like maize. It is suggested to develop more research on the effects of extreme phenomena on different phenological phases of crops so the sustainability of agroecosystems from the center of Buenos Aires province could be mantained. Key words: food safety, heat wave, soybean, maize
A Graph-Based Framework for Structured Prediction Tasks in Sanskrit

Abstract We propose a framework using energy-based models for multiple structured prediction tasks in Sanskrit. Ours is an arc-factored model, similar to the graph-based parsing approaches, and we consider the tasks of word segmentation, morphological parsing, dependency parsing, syntactic linearization, and prosodification, a “prosody-level” task we introduce in this work. Ours is a search-based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes. Typically, the state-of-the-art models for morphosyntactic tasks in morphologically rich languages still rely on hand-crafted features for their performance. But here, we automate the learning of the feature function. The feature function so learned, along with the search space we construct, encode relevant linguistic information for the tasks we consider. This enables us to substantially reduce the training data requirements to as low as 10%, as compared to the data requirements for the neural state-of-the-art models. Our experiments in Czech and Sanskrit show the language-agnostic nature of the framework, where we train highly competitive models for both the languages. Moreover, our framework enables us to incorporate language-specific constraints to prune the search space and to filter the candidates during inference. We obtain significant improvements in morphosyntactic tasks for Sanskrit by incorporating language-specific constraints into the model. In all the tasks we discuss for Sanskrit, we either achieve state-of-the-art results or ours is the only data-driven solution for those tasks.
Metapath- and Entity-aware Graph Neural Network for Recommendation

Due to the shallow structure, classic graph neural networks (GNNs) failed in modelling high-order graph structures that deliver critical insights of task relevant relations. The negligence of those insights lead to insufficient distillation of collaborative signals in recommender systems. In this paper, we propose PEAGNN, a unified GNN framework tailored for recommendation tasks, which is capable of exploiting the rich semantics in metapaths. PEAGNN trains multilayer GNNs to perform metapath-aware information aggregation on collaborative subgraphs, $h$-hop subgraphs around the target user-item pairs. After the attentive fusion of aggregated information from different metapaths, a graph-level representation is then extracted for matching score prediction. To leverage the local structure of collaborative subgraphs, we present entity-awareness that regularizes node embedding with the presence of features in a contrastive manner. Moreover, PEAGNN is compatible with the mainstream GNN structures such as GCN, GAT and GraphSage. The empirical analysis on three public datasets demonstrate that our model outperforms or is at least on par with other competitive baselines. Further analysis indicates that trained PEAGNN automatically derives meaningful metapath combinations from the given metapaths.
This is NOT spam!: An analysis of predatory publication invitations in library and information science

Predatory journal publications pose a significant threat to the quality of scholarly publications across disciplines, including in library and information science. These publications have been known to disproportionately target researchers in developing countries and for whom English is not their primary language. There is a lack of knowledge about how predatory journals solicit library and information science researchers. This research serves as a partial replication of Clemons et al., The Oncologist, 2017, 22, 236–240, study of predatory publication invitations, with the focus shifted to researchers in ontology to those in library and information science. Spam invitations received by two researchers from predatory publications over a 6‐week period (n = 98) were analyzed. Reported in the findings of this preliminary study are the predatory publishers from whom the most spam invitations were received.
A Systematic Mapping Study on Software Testing for Systems-of-Systems

Context: Smart Cities and other Systems-of-Systems (SoS) have attracted attention due to their potential for innovation in the services provided to the society. SoS are composed of several independent systems and often support critical domains. As such, failures can cause serious damage or even financial losses that can be avoided by performing quality assurance (QA). Among QA techniques, software testing is an essential activity that checks software behaviors against a subset of parameters. However, testing SoS can be even more complex since several other factors further elevate the complexity of the test, such as interoperability links and multiple independent systems. Goal: Our goal in this paper is to analyze the current state of research on software testing for SoS domain. Method: A protocol was formulated and executed according to the guidelines for performing systematic literature mappings in Software Engineering. Results: This study identified the tools, approaches, methods and processes that the literature has addressed to test SoS. Conclusions: Software Testing for SoS still faces several problems and presents many questions to be answered, particularly in the definition of processes and metrics.
A preliminary investigation of COVID‐19 transmission in the United States by incorporating social media sentiments

COVID‐19 has now become a global pandemic. During the widespread of COVID‐19, Twitter, as an online social media platform, has been a preferred channel for interaction and communication. As a result, it provides huge amount of information from which latent signals such as sentiments can be mined for a better understanding of COVID‐19 transmission patterns. As a preliminary attempt, we reveal a strongly positive zero‐order correlation between sentiments of tweets and COVID‐19 confirmed cases in U.S. Considering the unique hierarchical structure of the U.S. government, state governments exert their own power to issue public health policies. Indeed, there are different patterns of correlations between sentiments and COVID‐19 confirmed cases, affirming that country‐level characteristics suppress that of state‐level. Diving deeper into the textual content of COVID‐19 related tweets, there manifests a diverse set of topics which in turn lead to dispersed sentiments. Our preliminary investigation paves the way for a finer‐grained analysis of the COVID‐19 transmission and social media activities by considering varying situations across states and topics.
Reduce, Reuse, Recycle: Repurposing Existing Measurements to Identify Stale Traceroutes

Many systems rely on traceroutes to monitor or characterize the Internet. The quality of the systems' inferences depends on the completeness and freshness of the traceroutes, but the refreshing of traceroutes is constrained by limited resources at vantage points. Previous approaches predict which traceroutes are likely out-of-date in order to allocate measurements, or monitor BGP feeds for changes that overlap traceroutes. Both approaches miss many path changes for reasons including the difficulty in predicting changes and the coarse granularity of BGP paths. This paper presents techniques to identify out-of-date traceroutes without issuing any measurements, even if a change is not visible at BGP granularity. We base our techniques on two observations. First, although BGP updates encode routes at AS granularity, routers issue updates when they change intra-domain routes or peering points within the same AS path. Second, route changes correlate across paths, and many publicly available traceroutes exist. Our techniques maintain an atlas of traceroutes by monitoring BGP updates and publicly available traceroutes for signals to mark overlapping atlas traceroutes as stale. We focus our analysis of traceroute path changes at the granularity of border router IPs which provides an abstraction finer than AS- or PoP-level but is not affected by the periodicity of intra-domain load balancers. Our evaluation indicates that 80% of the traceroutes that our techniques signal as stale have indeed changed, even though the AS hops remained the same. Our techniques combine to identify 79% of all border IP changes, without issuing a single online measurement.
Conceptualizing and studying information creation: From production and processes to makers and making

An emerging body of literature has begun to address the earlier under‐researched topic of information creation. This panel inquires into the state‐of‐the‐art of research on information creation to highlight how different conceptualisations of information creation can foreground various key aspects of how information is made and produced, what types of actors and activities are involved, how information creation can be studied empirically, and what novel insights can be drawn to support practitioners, for instance, in information literacy instruction, information organisation and management, and information systems and services development.
Investigating the information practices of parents of transgender or gender non‐conforming (GNC) youth: Preliminary findings

This poster presents preliminary results of a pilot study of the information practices of parents of gender non‐conforming (GNC) youth. Semi‐structured interviews with six parents of transgender or non‐binary youth (ages 12–25) were conducted. Parents prioritized their children, regardless of age, as epistemic and cognitive authorities. Secondarily, parents prized the lived experiences of other transgender people and the families that supported them. Data analysis is ongoing and is anticipated to produce a fuller explication of these findings.
Opportunities and challenges of self‐tracking technologies: Understanding information, technology, and users through the lens of information science

Health self‐tracking technologies are used for various reasons, from tracking of physical activity (e.g., counting steps) and supporting weight loss to managing chronic illness and monitoring fertility. In many cases, the technology is characterized as a facilitator, the user as the main actor, and the information as a foundation for achieving the desired outcome. Apart from obvious benefits such as gaining awareness of one's body and health, users also face a range of challenges when using various self‐tracking technologies. This panel focuses the discussion on opportunities and challenges of self‐tracking technologies from the perspective of information science. Particularly, the panelists will address the importance of the information provided by self‐tracking technologies, which is a critical component of the conceptual triad (information, technology, and users) to comprehensively understand the nature of human interactions with such technologies.
SIRT1 promotes pulmonary artery endothelial cell proliferation by targeting the Akt signaling pathway

Pulmonary arterial hypertension (PAH) is a disease characterized by a progressive increase in pulmonary vascular resistance and obliterative pulmonary vascular remodeling; however, the pathogenesis of the disease is not completely understood. Sirtuin 1 (SIRT1) is a histone deacetylase involved in cell survival and metabolism. The present study explored the potential role of SIRT1 in human pulmonary arterial endothelial cells (HPAECs) under hypoxic conditions. In vitro HPAECs were cultured and exposed to hypoxic conditions. Subsequently, SIRT1 expression levels were measured via western blotting, the generation of reactive oxygen species (ROS) was evaluated, and the interaction between SIRT1 and Akt was assessed via reverse transcription-quantitative PCR and western blotting. In addition, the effects of SIRT1 on cell proliferation and apoptosis were also investigated. The results indicated that hypoxia induced SIRT1 expression in pulmonary arterial endothelial cells, which may be associated with ROS generation. SIRT1 expression activated the Akt signaling pathway, which increased the expression levels of Bcl-2 and hypoxia-inducible factor-1 in HPAECs. Moreover, SIRT1 promoted HPAEC proliferation and inhibited HPAEC apoptosis. ROS generation enhanced the SIRT1/Akt axis, which was essential for epithelial cell injury under hypoxic conditions. Therefore, blocking SIRT1 may reduce hypoxia-induced pathological damage in HPAECs.
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion

Non-parallel voice conversion (VC) is a technique for learning mappings between source and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial network (CycleGAN)-VC and CycleGAN-VC2 have shown promising results regarding this problem and have been widely used as benchmark methods. However, owing to the ambiguity of the effectiveness of CycleGAN-VC/VC2 for mel-spectrogram conversion, they are typically used for mel-cepstrum conversion even when comparative methods employ mel-spectrogram as a conversion target. To address this, we examined the applicability of CycleGAN-VC/VC2 to mel-spectrogram conversion. Through initial experiments, we discovered that their direct applications compromised the time-frequency structure that should be preserved during conversion. To remedy this, we propose CycleGAN-VC3, an improvement of CycleGAN-VC2 that incorporates time-frequency adaptive normalization (TFAN). Using TFAN, we can adjust the scale and bias of the converted features while reflecting the time-frequency structure of the source mel-spectrogram. We evaluated CycleGAN-VC3 on inter-gender and intra-gender non-parallel VC. A subjective evaluation of naturalness and similarity showed that for every VC pair, CycleGAN-VC3 outperforms or is competitive with the two types of CycleGAN-VC2, one of which was applied to mel-cepstrum and the other to mel-spectrogram. Audio samples are available at this http URL.
Geometry-Aware Hamiltonian Variational Auto-Encoder

Variational auto-encoders (VAEs) have proven to be a well suited tool for performing dimensionality reduction by extracting latent variables lying in a potentially much smaller dimensional space than the data. Their ability to capture meaningful information from the data can be easily apprehended when considering their capability to generate new realistic samples or perform potentially meaningful interpolations in a much smaller space. However, such generative models may perform poorly when trained on small data sets which are abundant in many real-life fields such as medicine. This may, among others, come from the lack of structure of the latent space, the geometry of which is often under-considered. We thus propose in this paper to see the latent space as a Riemannian manifold endowed with a parametrized metric learned at the same time as the encoder and decoder networks. This metric is then used in what we called the Riemannian Hamiltonian VAE which extends the Hamiltonian VAE introduced by arXiv:1805.11328 to better exploit the underlying geometry of the latent space. We argue that such latent space modelling provides useful information about its underlying structure leading to far more meaningful interpolations, more realistic data-generation and more reliable clustering.