Miracle-dz/UniFAR-FTUs huggingface dataset:
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training

The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research on stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with a limited number of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training (PET), proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in few-shot learning settings compared to several strong baselines.
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech

Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics. Despite much attention being paid to characterize and detect discriminatory speech, most work has focused on explicit or overt hate speech, failing to address a more pervasive form based on coded or indirect language. To fill this gap, this work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. We present systematic analyses of our dataset using contemporary baselines to detect and explain implicit hate speech, and we discuss key features that challenge existing models. This dataset will continue to serve as a useful benchmark for understanding this multifaceted issue.
Refocusing on Relevance: Personalization in NLG

Many NLG tasks such as summarization, dialogue response, or open domain question answering, focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user’s intent or context of work is not easily recoverable based solely on that source text– a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.
Region Invariant Normalizing Flows for Mobility Transfer

There exists a high variability in mobility data volumes across different regions, which deteriorates the performance of spatial recommender systems that rely on region-specific data. In this paper, we propose a novel transfer learning framework called Reformd, for continuous-time location prediction for regions with sparse checkin data. Specifically, we model user-specific checkin-sequences in a region using a marked temporal point process (MTPP) with normalizing flows to learn the inter-checkin time and geo-distributions. Later, we transfer the model parameters of spatial and temporal flows trained on a data-rich origin region for the next check-in and time prediction in a target region with scarce checkin data. We capture the evolving region-specific checkin dynamics for MTPP and spatial-temporal flows by maximizing the joint likelihood of next checkin with three channels (1) checkin-category prediction, (2) checkin-time prediction, and (3) travel distance prediction. Extensive experiments on different user mobility datasets across the U.S. and Japan show that our model significantly outperforms state-of-the-art methods for modeling continuous-time sequences. Moreover, we also show that Reformd can be easily adapted for product recommendations i.e., sequences without any spatial component.
Sachdev-Ye-Kitaev models and beyond: Window into non-Fermi liquids

We present a review of the Sachdev-Ye-Kitaev (SYK) model of compressible quantum many-body systems without quasiparticle excitations, and its connections to various theoretical studies of non-Fermi liquids in condensed matter physics. The review is placed in the context of numerous experimental observations on correlated electron materials. Strong correlations in metals are often associated with their proximity to a Mott transition to an insulator created by the local Coulomb repulsion between the electrons. We explore the phase diagrams of a number of models of such local electronic correlation, employing a dynamical mean field theory in the presence of random spin exchange interactions. Numerical analyses and analytical solutions, using renormalization group methods and expansions in large spin degeneracy, lead to critical regions which display SYK physics. The models studied include the single-band Hubbard model, the $t$-$J$ model and the two-band Kondo-Heisenberg model in the presence of random spin exchange interactions. We also examine non-Fermi liquids obtained by considering each SYK model with random four-fermion interactions to be a multi-orbital atom, with the SYK-atoms arranged in an infinite lattice. We connect to theories of sharp Fermi surfaces without any low-energy quasiparticles in the absence of spatial disorder, obtained by coupling a Fermi liquid to a gapless boson; a systematic large $N$ theory of such a critical Fermi surface, with SYK characteristics, is obtained by averaging over an ensemble of theories with random boson-fermion couplings. Finally, we present an overview of the links between the SYK model and quantum gravity and end with an outlook on open questions.
Online Influence Maximization with Node-level Feedback Using Standard Offline Oracles

We study the online influence maximization (OIM) problem in social networks, where in multiple rounds the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade. We focus on two major challenges in this paper. First, we work with node-level feedback instead of edge-level feedback. The edge-level feedback reveals all edges that pass through information in a cascade, whereas the node-level feedback only reveals the activated nodes with timestamps. The node-level feedback is arguably more realistic since in practice it is relatively easy to observe who is influenced but very difficult to observe from which relationship (edge) the influence comes. Second, we use standard offline oracles instead of offline pair-oracles. To compute a good seed set for the next round, an offline pair-oracle finds the best seed set and the best parameters within the confidence region simultaneously, and such an oracle is difficult to compute due to the combinatorial core of the OIM problem. So we focus on how to use the standard offline influence maximization oracle which finds the best seed set given the edge parameters as input. In this paper, we resolve these challenges for the famous independent cascade (IC) diffusion model. The past research only achieves edge-level feedback, while we present the first optimal regret algorithm for the node-level feedback. For the first challenge above, we apply a novel adaptation of the maximum likelihood estimation (MLE) approach to learn the graph parameters and its confidence region (a confidence ellipsoid). For the second challenge, we adjust the update procedure to dissect the confidence ellipsoid into confidence intervals on each parameter, so that the standard offline influence maximization oracle is enough.
Attention-based Contrastive Learning for Winograd Schemas

Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This paper investigates whether contrastive learning can be extended to Transfomer attention to tackling the Winograd Schema Challenge. To this end, we propose a novel self-supervised framework, leveraging a contrastive loss directly at the level of self-attention. Experimental analysis of our attention-based models on multiple datasets demonstrates superior commonsense reasoning capabilities. The proposed approach outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones.
Allogeneic hematopoietic cell transplantation in patients with myelodysplastic syndrome using treosulfan based compared to other reduced‐intensity or myeloablative conditioning regimens. A report of the chronic malignancies working party of the EBMT

Allogeneic haematopoietic‐cell transplantation (allo‐HCT) is a potentially curative therapy for high‐risk myelodysplastic syndrome (MDS). Reduced‐intensity conditioning (RIC) is usually associated with lower non‐relapse mortality (NRM), higher relapse rate and similar overall‐survival (OS) as myeloablative‐conditioning (MAC). Fludarabine/treosulfan (FT) is a reduced‐toxicity regimen with intense anti‐leukaemia activity and a favourable toxicity profile. We investigated post‐transplant outcomes in 1722 MDS patients following allo‐HCT with FT (n = 367), RIC (n = 687) or MAC (n = 668). FT and RIC recipients were older than MAC recipients, median age 59, 59 and 51 years, respectively (P < 0·001) but other disease characteristics were similar. The median follow‐up was 64 months (1–171). Five‐year relapse rates were 25% (21–30), 38% (34–42) and 25% (22–29), after FT, RIC and MAC, respectively, (P < 0·001). NRM was 30% (25–35), 27% (23–30) and 34% (31–38, P = 0·008), respectively. Five‐year OS was 50% (44–55), 43% (38–47), and 43% (39–47), respectively (P = 0·03). In multivariate analysis, FT was associated with a lower risk of relapse (HR 0·55, P < 0·001) and better OS (HR 0·72, P = 0·01). MAC was associated with higher NRM (HR 1·44, P = 0·001). In conclusion, FT is associated with similar low relapse rates as MAC and similar low NRM as RIC, resulting in improved OS. FT may be the preferred regimen for allo‐HCT in MDS.
Clinical effect of alfacalcidol in children with Henoch-Schönlein purpura: a prospective randomized controlled trial.

OBJECTIVES To study the effects of alfacalcidol on serum 25-(OH)D3 level, cellular immune function, and inflammatory factors in children with Henoch-Schönlein purpura (HSP). METHODS A total of 200 children with HSP were prospectively enrolled from June 2018 to June 2020. According to the random number table method, they were divided into an observation group and a control group (n=100 each). The control group was treated with vitamin C, rutin tablets, dipyridamole, cimetidine, calcium supplements, and glucocorticoids. In addition to the treatment for the control group, the observation group received alfacalcidol capsules (0.25 μg/d) orally before bed for 4 weeks. The two groups were compared in terms of the level of 25-(OH)D3, the percentages of T lymphocyte subsets (CD3+, CD4+, and CD8+) and NK cells, and the levels of inflammatory factors, interleukin-6 (IL-6), interleukin-17 (IL-17), interleukin-21 (IL-21), and tumor necrosis factor-α (TNF-α), before treatment and after 4 weeks of treatment. The children were followed up for 6 months to determine the recurrence rate and the incidence of renal damage. RESULTS After treatment, the observation group showed a significantly higher serum 25-(OH)D3 level, significantly higher percentages of CD3+T cells, CD4+T cells, and NK cells, and significantly lower levels of IL-6, IL-17, IL-21, and TNF-α compared with the control group (P<0.05). After 6 months of follow-up, the recurrence rate and the incidence of renal damage in the observation group were significantly lower than those in the control group (P<0.05). CONCLUSIONS Alfacalcidol can increase the serum 25-(OH)D3 level, improve cellular immune function, decrease inflammatory factor levels, and reduce recurrence and renal damage in children with HSP.
Editors' Choice of Noteworthy Clinical Endoscopy Publications in the First Decade

This is a special review to celebrate the 10th anniversary of Clinical Endoscopy. Each deputy editor has selected articles from one’s subspecialty that are significant in terms of the number of downloads, citations, and clinical importance. The articles included original articles, review articles, systematic reviews, and meta-analyses.
The α subunit of the heterotrimeric G protein regulates mesophyll CO2 conductance and drought tolerance in rice

Summary Mesophyll conductance g m determines CO2 diffusion rates from mesophyll intercellular air spaces to the chloroplasts and is an important factor limiting photosynthesis. Increasing g m in cultivated plants is a potential strategy to increase photosynthesis and intrinsic water use efficiency (WUEi). The anatomy of the leaf and metabolic factors such as aquaporins and carbonic anhydrases have been identified as important determinants of g m. However, genes involved in the regulation and modulation of g m remain largely unknown. In this work, we investigated the role of heterotrimeric G proteins in g m and drought tolerance in rice d1 mutants, which harbor a null mutation in the Gα subunit gene, RGA1. d1 mutants in both cv Nipponbare and cv Taichung 65 exhibited increased g m, fostering improvement in photosynthesis, WUEi, and drought tolerance compared with wild‐type. The increased surface area of mesophyll cells and chloroplasts exposed to intercellular airspaces and the reduced cell wall and chloroplast thickness in the d1 mutant are evident contributors to the increase in g m. Our results indicate that manipulation of heterotrimeric G protein signaling has the potential to improve crop WUEi and productivity under drought.
Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks

Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence [Farquhar et al., 2020] suggests that existing solutions dependent on simple Gaussian formulations may not be sufficient. However, moving to other distributions necessitates Monte Carlo (MC) sampling to estimate quantities such as the KL divergence: it could be expensive and scales poorly as the dimensions of both the input data and the model grow. This is directly related to the structure of the computation graph, which can grow linearly as a function of the number of MC samples needed. Here, we construct a framework to describe these computation graphs, and identify probability families where the graph size can be independent or only weakly dependent on the number of MC samples. These families correspond directly to large classes of distributions. Empirically, we can run a much larger number of iterations for MC approximations for larger architectures used in computer vision with gains in performance measured in confident accuracy, stability of training, memory and training time.
A 52‐week update of a multicentre real‐life experience on effectiveness and safety of risankizumab in psoriasis

The long-term efficacy and safety of the IL-23 inhibitor risankizumab, has been demonstrated in clinical trials, and only in a few real-world studies. We recently reported an Italian multicentre 16-week real-life experience in 57 adult patients with moderate-to-severe psoriasis treated with risankizumab. Herein, we report an extension analysis of our study beyond 16 weeks, collecting data on effectiveness of risankizumab on psoriasis characteristics (PASI, PGA, DLQI, NRS itch, NRS pain) after 36 and 52 week treatment.
Remaining Useful Life Estimation of Hard Disk Drives using Bidirectional LSTM Networks

Physical and cloud storage services are well-served by functioning and reliable high-volume storage systems. Recent observations point to hard disk reliability as one of the most pressing reliability issues in data centers containing massive volumes of storage devices such as HDDs. In this regard, early detection of impending failure at the disk level aids in reducing system downtime and reduces operational loss making proactive health monitoring a priority for AIOps in such settings. In this work, we introduce methods of extracting meaningful attributes associated with operational failure and of pre-processing the highly imbalanced health statistics data for subsequent prediction tasks using data-driven approaches. We use a Bidirectional LSTM with a multi-day look back period to learn the temporal progression of health indicators and baseline them against vanilla LSTM and Random Forest models to come up with several key metrics that establish the usefulness of and superiority of our model under some tightly defined operational constraints. For example, using a 15 day look back period, our approach can predict the occurrence of disk failure with an accuracy of 96.4% considering test data 60 days before failure. This helps to alert operations maintenance well in-advance about potential mitigation needs. In addition, our model reports a mean absolute error of 0.12 for predicting failure up to 60 days in advance, placing it among the state-of-the-art in recent literature.
Gibbs Measure for the Focusing Fractional NLS on the Torus

We study the construction of the Gibbs measures for the {\it focusing} mass-critical fractional nonlinear Schr\"odinger equation on the multi-dimensional torus. We identify the sharp mass threshold for normalizability and non-normalizability of the focusing Gibbs measures, which generalizes the influential works of Lebowitz-Rose-Speer (1988), Bourgain (1994), and Oh-Sosoe-Tolomeo (2021) on the one-dimensional nonlinear Schr\"odinger equations. To this purpose, we establish an almost sharp fractional Gagliardo-Nirenberg-Sobolev inequality on the torus, which is of independent interest.
Follow the Curve: Robotic Ultrasound Navigation with Learning Based Localization of Spinous Processes for Scoliosis Assessment

The scoliosis progression in adolescents requires close monitoring to timely take treatment measures. Ultrasound imaging is a radiation-free alternative in scoliosis assessment to X-ray, which is typically used in clinical practice. However, ultrasound images are prone to speckle noise, making it challenging for sonographers to detect bony features and follow the spinal curvature. This study introduces a novel robotic ultrasound approach for spinous process localization and automatic spinal curvature tracking for scoliosis assessment. The positions of the spinous processes are computed using a fully connected network with a deconvolutional head. A 5-fold cross-validation was performed on a dataset of ultrasound images from 25 human subjects with scoliosis. The resulting percentage of correct keypoints of the spinous process is 0.966 ± 0.027 with a mean distance error of 1.0 ± 0.99mm. We use this machine learning-based method to guide the motion of the robot-held ultrasound probe and to follow the spinal curvature while capturing ultrasound images. We present a new force-driven controller that automatically adjusts the pose and orientation of the probe relative to the skin surface, which ensures a good acoustic coupling between the probe and skin. We extended the network architecture to additionally perform classification of the spine into its regions, i.e., sacrum, lumbar, and thoracic, which are used to adjust the probe's orientation to account for the varying curvature along the spine. After the autonomous scanning, the acquired data is used to reconstruct the coronal spinal image, where the deformity of the scoliosis spine can be assessed and measured. The proposed learning-based method for anatomical landmarks localization was compared to conventional methods based on phase symmetry and image intensity. The learning-based method proved to be more precise for spinous process localization while processing images at a faster rate, which is advantageous for real-time scoliosis scanning. To evaluate the performance of our robotic method, we conducted an experimental study with human scoliosis subjects where deviations of the spinous process from the image center can be compared to those appearing in a manual scan. Our results show that the robotic approach reduces the mean error of spinal curvature following for mild scoliosis from 4.6 ± 4.6mm (manual scanning) to 1.0 ± 0.8mm (robotic scanning); For moderate scoliosis from 4.3 ± 3.9mm (manual scanning) to 2.8 ± 1.8mm (robotic scanning). The angles of spinal deformity measured on spinal reconstruction images were similar for both methods, implying that they equally reflect human anatomy. The spinal region-specific moment-based probe orientation control showed to improve the scanning performance. An ablation study was performed to investigate the importance of each component of the proposed system.
Improved Latent Tree Induction with Distant Supervision via Span Constraints

For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset.