Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction

Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood. Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit correlations. In this paper, We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples, namely, entity pairs and corresponding relations. To be specific, we design a hybrid prototypical learning mechanism that bridges text and knowledge concerning both entities and relations. Thus, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn more representative prototypes. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
Anomaly detection in average fuel consumption with XAI techniques for dynamic generation of explanations

In this paper we show a complete process for unsupervised anomaly detection for the average fuel consumption of fleet vehicles that is able to explain what variables are affecting the consumption in terms of feature relevance. For doing that, we combine the anomaly detection with a surrogate model that is able to provide that feature relevance. For this part, we evaluate both whitebox models from the literature, as well as novel variations over them, and blackbox models combined with local posthoc feature relevance techniques. The evaluation is done using real IoT data belonging to Telefonica, and is measured both in terms of model performance, as well as using Explainable AI metrics that compare the explanations generated in terms representativeness, fidelity, stability and contrastiveness. The explanations generate counterfactual recommendations that show what could have been done to reduce the average fuel consumption of a vehicle and turn it into an inlier. The procedure is combined with domain knowledge expressed in business rules, and is able to adequate the type of explanations depending on the target user profile.
Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA

With the explosively increasing demands on the network capacity, throughput and number of connected wireless devices, massive connectivity is an urgent problem for the next generation wireless communications. In this paper, we propose a grant-free access protocol for massive connectivity that utilizes a large number of antennas in a base station and is expected to be widely deployed in cellular networks. The scheme consists of a sparse structure in sparse code multiple access (SCMA) and receiver processing based on dictionary learning (DL). A large number of devices can transmit data without any scheduling process, which is attractive for resource utilization and transmission power efficiency. Through the design of transmission frames, DL can implement both channel estimation and data detection in SCMA system. The numerical results show that the proposed scheme has a promising bit error rate performance in massive connectivity scenarios of cellular networks.
TopicBERT for Energy Efficient Document Classification

Prior research notes that BERT’s computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document classification. Our work thus focuses on optimizing the computational cost of fine-tuning for document classification. We achieve this by complementary learning of both topic and language models in a unified framework, named TopicBERT. This significantly reduces the number of self-attention operations – a main performance bottleneck. Consequently, our model achieves a 1.4x ( 40%) speedup with 40% reduction in CO2 emission while retaining 99.9% performance over 5 datasets.
Approximation of the Filter Equation for Multiple Timescale, Correlated, Nonlinear Systems

This paper considers the approximation of the continuous time filtering equation for the case of a multiple timescale (slow-intermediate, and fast scales) that may have correlation between the slow-intermediate process and the observation process. The signal process is considered fully coupled, taking values in $\mathbb{R^{m}} \times \mathbb{R^{n}}$ and without periodicity assumptions on coefficients. It is proved that in the weak topology, the solution of the filtering equation converges in probability to a solution of a lower dimensional averaged filtering equation in the limit of large timescale separation. The method of proof uses the perturbed test function approach (method of corrector) to handle the intermediate timescale in showing tightness and characterization of limits. The correctors are solutions of Poisson equations.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark

In this paper, we introduce an advanced Russian general language understanding evaluation benchmark – Russian SuperGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We also provide baselines, human level evaluation, open-source framework for evaluating models, and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the translated diagnostic test set and offer the first steps to further expanding or assessing State-of-the-art models independently of language.
Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment

This paper studies the identification and estimation of unconditional policy effects when the treatment is binary and endogenous. We first characterize the asymptotic bias of the unconditional regression estimator that ignores the endogeneity and elaborate on the channels that the endogeneity can render the unconditional regressor estimator inconsistent. We show that even if the treatment status is exogenous, the unconditional regression estimator can still be inconsistent when there are common covariates affecting both the treatment status and the outcome variable. We introduce a new class of marginal treatment effects (MTE) based on the influence function of the functional underlying the policy target. We show that an unconditional policy effect can be represented as a weighted average of the newly defined MTEs over the individuals at the margin of indifference. Point identification is achieved using the local instrumental variable approach. Furthermore, the unconditional policy effects are shown to include the marginal policy-relevant treatment effect in the literature as a special case. Methods of estimation and inference for the unconditional policy effects are provided. In the empirical application, we estimate the effect of changing college enrollment status, induced by higher tuition subsidy, on the quantiles of the wage distribution.
The ReadoutCard Userspace Driver for the New Alice O2 Computing System

A large ion collider experiment (ALICE) experiment focuses on the study of the quark-gluon plasma as a product of heavy-ion collisions at the CERN large hadron collider (LHC). During the long shutdown 2 of the LHC in 2019–2020, a major upgrade is underway in order to cope with a hundredfold input data rate increase with peaks of up to 3.5 TB/s. This upgrade includes the new online–offline computing system called <inline-formula> <tex-math notation="LaTeX">$O^{2}$ </tex-math></inline-formula>. The <inline-formula> <tex-math notation="LaTeX">$O^{2}$ </tex-math></inline-formula> readout chain runs on commodity Linux servers equipped with custom peripheral component interconnect express (PCIe) field-programmable gate array (FPGA)-based readout cards: the PCIe Gen <inline-formula> <tex-math notation="LaTeX">$3\,\,\times16$ </tex-math></inline-formula>, Intel Arria 10-based common readout unit (CRU), and the PCIe Gen <inline-formula> <tex-math notation="LaTeX">$2\,\,\times8$ </tex-math></inline-formula>, Xilinx Vertex 6-based Common ReadOut Receiver Card (CRORC). Access to the cards is provided through the <inline-formula> <tex-math notation="LaTeX">$O^{2}$ </tex-math></inline-formula> ReadoutCard userspace driver, which handles synchronization and communication for direct memory access (DMA) transfers, provides base address registers (BAR) access, and facilitates card configuration and monitoring. The ReadoutCard driver is the lowest level interface to the readout cards within <inline-formula> <tex-math notation="LaTeX">$O^{2}$ </tex-math></inline-formula> and is in use by all central systems and detector teams of the ALICE experiment. This communication presents the architecture of the driver and the suite of tools used for card configuration and monitoring. It also discusses its interaction with the tangent subsystems within the <inline-formula> <tex-math notation="LaTeX">$O^{2}$ </tex-math></inline-formula> framework.
Polar Coded Repetition for Low-Capacity Channels

Constructing efficient low-rate error-correcting codes with low-complexity encoding and decoding have become increasingly important for applications involving ultra-low-power devices such as Internet-of-Things (IoT) networks. To this end, schemes based on concatenating the state-of-the-art codes at moderate rates with repetition codes have emerged as practical solutions deployed in various standards. In this paper, we propose a novel mechanism for concatenating outer polar codes with inner repetition codes which we refer to as polar coded repetition. More specifically, we propose to transmit a slightly modified polar codeword by deviating from Arıkan’s standard 2 × 2 Kernel in a certain number of polarization recursions at each repetition block. We show how this modification can improve the asymptotic achievable rate of the polar-repetition scheme, while ensuring that the overall encoding and decoding complexity is kept almost the same. The achievable rate is analyzed for the binary erasure channels (BEC).
Modern models of trophic meta-communities

Dispersal and foodweb dynamics have long been studied in separate models. However, over the past decades, it has become abundantly clear that there are intricate interactions between local dynamics and spatial patterns. Trophic meta-communities, i.e. meta-foodwebs, are very complex systems that exhibit complex and often counterintuitive dynamics. Over the past decade, a broad range of modelling approaches have been used to study these systems. In this paper, we review these approaches and the insights that they have revealed. We focus particularly on recent papers that study trophic interactions in spatially extensive settings and highlight the common themes that emerged in different models. There is overwhelming evidence that dispersal (and particularly intermediate levels of dispersal) benefits the maintenance of biodiversity in several different ways. Moreover, some insights have been gained into the effect of different habitat topologies, but these results also show that the exact relationships are much more complex than previously thought, highlighting the need for further research in this area. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.
Impacts of stormwater on coastal ecosystems: the need to match the scales of management objectives and solutions

Despite their limited area relative to the global ocean, coastal zones—the regions where land meets the sea—play a disproportionately important role in generating ecosystem services. However, coastal ecosystems are under increasing pressure from human populations. In particular, urban stormwater is an increasingly important threat to the integrity of coastal systems. Urban catchments exhibit altered flow regimes that impact ecosystem processes and coastal foodwebs. In addition, urban stormwater contains complex and unpredictable mixtures of chemicals that result in a multitude of lethal and sublethal impacts on species in coastal systems. Along the western coast of the United States, we estimate that hundreds of billions of kilograms of suspended solids flow off land surfaces and enter the Northern California Current each year. However, 70% of this pollution could be addressed by treating only 1.35% of the land area. Determining how to prioritize treatment of stormwater in this region requires a clear articulation of objectives—spatial distribution of appropriate management actions is dependent on the life histories of species, and management schemes optimized for one species may not achieve desired objectives for other species. In particular, we highlight that the scale of stormwater interventions must match the ecological scale relevant to species targeted by management. In many cases, management and policy will require mechanisms in order to ensure that local actions scale-up to efficiently and effectively achieve management objectives. In the face of rapid urbanization of coastal zones, failure to consider the match of management and ecological scales will result in the continued decline of coastal ecosystems and the species they support. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.
Intraventricular vector flow mapping 3-D by triplane Doppler echocardiography

Heart failure affects the heart’s ability to meet the oxygen needs of the organs. It concerns more than one million people in France. It can be defined by the reduced capacity of the heart to be filled with blood (diastolic dysfunction) and/or emptied (systolic dysfunction). Although systolic dysfunction is relatively easy to diagnose, the diagnosis of diastolic dysfunction is more challenging. The accessibility of echocardiography, and its ability to provide information non-invasively and in real time, makes ultrasound the preferred technique for evaluating left ventricular diastolic function. However, the diagnosis of diastolic dysfunction often remains uncertain, as the recommended ultrasound indices may lead to conflicting conclusions. Since intracardiac blood flow is very sensitive to changes in the myocardium, we hypothesized that a thorough examination of intraventricular blood flow by color Doppler echocardiography could improve the assessment of diastolic function in patients with heart failure.
Perovskite Puzzle for Revolutionary Functional Materials

Widely spread crystal lattices of perovskites represent a natural flexible platform for chemical design of various advanced functional materials with unique features. An interplay between chemical bonding, defects and crystallochemical peculiarities makes the perovskite structure a “LEGO designer” utilizing natural features of chemical elements of the renowned Mendeleev's Periodic Table (PTE) celebrating its 150-year anniversary. In this mini-review, crystal chemistry and bonding features, physical and functional properties, preparation methods and tuning functional properties with periodicity “tools” of the PTE will be exemplified for legendary families of high-temperature superconductive cuprates, colossal magnetoresistive manganites and hybrid lead halides for a new generation of solar cells.
The Interplay Between Beta-Amyloid 1–42 (Aβ1–42)-Induced Hippocampal Inflammatory Response, p-tau, Vascular Pathology, and Their Synergistic Contributions to Neuronal Death and Behavioral Deficits

Alzheimer’s disease (AD), the most common chronic neurodegenerative disorder, has complex neuropathology. The principal neuropathological hallmarks of the disease are the deposition of extracellular β-amyloid (Aβ) plaques and neurofibrillary tangles (NFTs) comprised of hyperphosphorylated tau (p-tau) protein. These changes occur with neuroinflammation, a compromised blood-brain barrier (BBB) integrity, and neuronal synaptic dysfunction, all of which ultimately lead to neuronal cell loss and cognitive deficits in AD. Aβ1–42 was stereotaxically administered bilaterally into the CA1 region of the hippocampi of 18-month-old male C57BL/6 mice. This study aimed to characterize, utilizing immunohistochemistry and behavioral testing, the spatial and temporal effects of Aβ1–42 on a broad set of parameters characteristic of AD: p-tau, neuroinflammation, vascular pathology, pyramidal cell survival, and behavior. Three days after Aβ1–42 injection and before significant neuronal cell loss was detected, acute neuroinflammatory and vascular responses were observed. These responses included the up-regulation of glial fibrillary acidic protein (GFAP), cell adhesion molecule-1 (PECAM-1, also known as CD31), fibrinogen labeling, and an increased number of activated astrocytes and microglia in the CA1 region of the hippocampus. From day 7, there was significant pyramidal cell loss in the CA1 region of the hippocampus, and by 30 days, significant localized up-regulation of p-tau, GFAP, Iba-1, CD31, and alpha-smooth muscle actin (α-SMA) in the Aβ1–42-injected mice compared with controls. These molecular changes in Aβ1–42-injected mice were accompanied by cognitive deterioration, as demonstrated by long-term spatial memory impairment. This study is reporting a comprehensive examination of a complex set of parameters associated with intrahippocampal administration of Aβ1–42 in mice, their spatiotemporal interactions and combined contribution to the disease progression. We show that a single Aβ injection can reproduce aspects of the inflammatory, vascular, and p-tau induced pathology occurring in the AD human brain that lead to cognitive deficits.
A Renewed Focus on Health Disparities in the Year 2020: Reexamining Geospatial Disparities

To achieve the ultimate goal of health equity for all people in the United States, new methods are necessary to further understand health disparities in cancer. Geospatial approaches to examine outcomes in cancer control and population sciences in the United States is an area of growing interest, but traditional methods are hindered by the use of a single location at the time of diagnosis. Incorporating residential histories into geospatial analyses overcomes this challenge and is a step forward in improving our understanding of geospatial disparities in cancer outcomes. See related article by Wiese et al., p. 2119
Cholesterol-Rich Lipid Rafts in the Cellular Membrane Play an Essential Role in Avian Reovirus Replication

Cholesterol is an essential component of lipid rafts in cellular plasma membranes. Although lipid rafts have been reported to have several functions in multiple stages of the life cycles of many different enveloped viruses, the mechanisms by which non-enveloped viruses, which lack outer lipid membranes, infect host cells remain unclear. In this study, to investigate the dependence of non-enveloped avian reovirus (ARV) infection on the integrity of cholesterol-rich membrane rafts, methyl-β-cyclodextrin (MβCD) was used to deplete cellular membrane cholesterol at the ARV attachment, entry, and post-entry stages. Treatment with MβCD significantly inhibited ARV replication at both the entry and post-entry stages in a dose-dependent manner, but MβCD had a statistically insignificant effect when it was added at the attachment stage. Moreover, MβCD treatment markedly reduced syncytium formation, which occurs at a relatively late stage of the ARV life cycle and is involved in cell-cell transmission and release. Furthermore, the addition of exogenous cholesterol reversed the effects mentioned above. Colocalization data also showed that the ARV proteins σC, μNS, and p10 prefer to localize to cholesterol-rich lipid raft regions during ARV infection. Altogether, these results suggest that cellular cholesterol in lipid rafts plays a critical role in ARV replication.
Cue-Reactivity Among Young Adults With Problematic Instagram Use in Response to Instagram-Themed Risky Behavior Cues: A Pilot fMRI Study

Background Problematic Instagram use (PIGU), a specific type of internet addiction, is prevalent among adolescents and young adults. In certain instances, Instagram acts as a platform for exhibiting photos of risk-taking behavior that the subjects with PIGU upload to gain likes as a surrogate for gaining peer acceptance and popularity. Aims The primary objective was to evaluate whether addiction-specific cues compared with neutral cues, i.e., negative emotional valence cues vs. positive emotional valence cues, would elicit activation of the dopaminergic reward network (i.e., precuneus, nucleus accumbens, and amygdala) and consecutive deactivation of the executive control network [i.e., medial prefrontal cortex (mPFC) and dorsolateral prefrontal cortex (dlPFC)], in the PIGU subjects. Method An fMRI cue-induced reactivity study was performed using negative emotional valence, positive emotional valence, and truly neutral cues, using Instagram themes. Thirty subjects were divided into PIGU and healthy control (HC) groups, based on a set of diagnostic criteria using behavioral tests, including the Modified Instagram Addiction Test (IGAT), to assess the severity of PIGU. In-scanner recordings of the subjects’ responses to the images and regional activity of the neural addiction pathways were recorded. Results Negative emotional valence > positive emotional valence cues elicited increased activations in the precuneus in the PIGU group. A negative and moderate correlation was observed between PSC at the right mPFC with the IGAT scores of the PIGU subjects when corrected for multiple comparisons [r = −0.777, (p < 0.004, two-tailed)]. Conclusion Addiction-specific Instagram-themed cues identify the neurobiological underpinnings of Instagram addiction. Activations of the dopaminergic reward system and deactivation of the executive control network indicate converging neuropathological pathways between Instagram addiction and other types of addictions.
The Effect of Hunger and Satiety on Mood-Related Food Craving

In addition to hunger, which results from food deprivation, fasting, or restrained eating, food craving is thought to be one of the major factors influencing eating behavior. Although food craving occurs in healthy humans with normal eating behavior, research suggests pronounced food craving to be a risk factor for binging food, which in turn might cause weight gain and obesity. In addition, negative mood seems to induce binge eating, although the underlying processes are not clearly understood. To examine these effects on momentary food craving more closely, we implemented a food cue-reactivity paradigm in 33 normal-weight females (mean age = 25.1 years, mean BMI = 21.6). Mood (negative vs. neutral) and state of food deprivation (hunger vs. satiety) were varied systematically. Self-rated craving was significantly higher in hungry compared to sated states. Moreover, in neutral mood, high-caloric foods modulated craving, i.e., hungry and sated participants preferred savory food and sweet food, respectively. In negative mood, this differential effect of savory and sweet items was absent. In summary, the state of hunger has a huge impact on food craving, which is further modulated by emotional state. Future research should consider hunger to be of high relevance for effects investigated in food-related paradigms. Our findings in healthy women of normal weight support etiological models of disturbed eating behavior and are in line with common interventions targeting eating schedules and mood regulation.