Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of human leukocyte antigen mismatch on recipient outcomes in living‐donor liver transplantation

Donor–recipient human leukocyte antigen (HLA) compatibility has not been considered to significantly affect liver transplantation (LT) outcomes; however, its significance in living‐donor LT (LDLT), which is mostly performed between blood relatives, remains unclear. This retrospective cohort study included 1954 LDLTs at our institution (1990–2020). The primary and secondary endpoints were recipient survival and the incidence of T cell–mediated rejection (TCMR) after LDLT, respectively, according to the number of HLA mismatches at all five loci: HLA‐A, HLA‐B, HLA‐C, HLA‐DR, and HLA‐DQ. Subgroup analyses were also performed in between‐siblings that characteristically have widely distributed 0–10 HLA mismatches. A total of 1304 cases of primary LDLTs were finally enrolled, including 631 adults (recipient age at LT ≥18 years) and 673 children (<18 years). In adult‐to‐adult LDLT, the more HLA mismatches at each locus, the significantly worse the recipient survival was (p = 0.03, 0.01, 0.03, 0.001, and <0.001 for HLA‐A, HLA‐B, HLA‐C, HLA‐DR, and HLA‐DQ, respectively). This trend was more pronounced when multiple loci were combined (all p < 0.001 for A + B + DR, A + B + C, DR + DQ, and A + B + C + DR + DQ). Notably, a total of three or more HLA‐B + DR mismatches was an independent risk factor for both TCMR (hazard ratio [HR] 2.66, 95% confidence interval [CI] 1.21–5.87; p = 0.02) and recipient survival (HR 2.44, 95% CI 1.11–5.35; p = 0.03) in between‐siblings. By contrast, HLA mismatch did not affect pediatric LDLT outcomes at any locus or in any combinations; however, it should be noted that all donor–recipient relationships are parent‐to‐child that characteristically possesses one or less HLA mismatch at each locus and maximally five or less mismatches in total. In conclusion, HLA mismatch significantly affects not only TCMR development but also recipient survival in adult LDLT, but not in children.
Attractive $N$-$\phi$ Interaction and Two-Pion Tail from Lattice QCD near Physical Point

First results on the interaction between the $\phi$-meson and the nucleon ($N$) are presented based on the ($2+1$)-flavor lattice QCD simulations with nearly physical quark masses. Using the HAL QCD method, the spacetime correlation of the $N$-$\phi$ system in the spin 3/2 channel is converted into the $N$-$\phi$ scattering phase shift through the interaction potential. The $N$-$\phi$ potential appears to be a combination of a short-range attractive core and a long-range attractive tail. The latter is found to be consistent with the two-pion exchange (TPE) obtained from the interaction between a color-dipole and the nucleon. The resultant scattering length and effective range for $m_{\pi}=$ 146.4 MeV are $ a^{(3/2)}_0=-1.43(23)_{\rm stat.}\left(^{+36}_{-06}\right)_{\rm syst. } {\rm fm}$ and $ r^{(3/2)}_{\rm eff}=2.36(10)_{\rm stat.}\left(^{+02}_{-48}\right)_{\rm syst. } {\rm fm}$, respectively. The magnitude of the scattering length is shown to have nontrivial dependence of $m_{\pi}$ and is sensitive to the existence of the long-range tail from TPE.
The Astrophysical Distance Scale. V. A 2% Distance to the Local Group Spiral M33 via the JAGB Method, Tip of the Red Giant Branch, and Leavitt Law

The J-region asymptotic giant branch (JAGB) method is a new standard candle that is based on the stable intrinsic J-band magnitude of color-selected carbon stars, and has a precision comparable to other primary distance indicators such as Cepheids and the TRGB. We further test the accuracy of the JAGB method in the Local Group galaxy M33. M33's moderate inclination, low metallicity, and nearby proximity make it an ideal laboratory for tests of systematics in local distance indicators. Using high-precision optical BVI and near-infrared JHK photometry, we explore the application of three independent distance indicators: the JAGB method, the Cepheid Leavitt law, and the TRGB. We find: μ 0(TRGB I ) = 24.72 ± 0.02 (stat) ± 0.07 (sys) mag, μ 0(TRGBNIR) = 24.72 ± 0.04 (stat) ± 0.10 (sys) mag, μ 0(JAGB) = 24.67 ± 0.03 (stat) ± 0.04 (sys) mag, and μ 0(Cepheid) = 24.71 ± 0.04 (stat) ± 0.01 (sys) mag. For the first time, we also directly compare a JAGB distance using ground-based and space-based photometry. We measure μ 0(JAGBF110W) = 24.71 ± 0.06 (stat) ± 0.05 (sys) mag using the (F814W−F110W) color combination to effectively isolate the JAGB stars. In this paper, we measure a distance to M33 accurate to 2% and provide further evidence that the JAGB method is a powerful extragalactic distance indicator that can effectively probe a local measurement of the Hubble constant using spaced-based observations. We expect to measure the Hubble constant via the JAGB method in the near future, using observations from the James Webb Space Telescope.
Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy

: We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames. This unique capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition using in-situ environmental TEM data, informing models of morphological evolution and catalytic properties. The model performance and achieved accuracy of predictions are desirable based on, for scientific data characteristic, based on limited size of training data sets. The model convergence and values for the loss function mean square error show dependence on the training strategy, and structural similarity measure between predicted structure images and ground truth reaches the value of about 0.7. This computed structural similarity is smaller than values obtained when the deep learning architecture is trained using much larger benchmark data sets, it is sufficient to show the structural transition of Au nanoparticles. While performance parameters of our model applied to scientific data fall short of those achieved for the non-scientific big data sets, we demonstrate model ability to predict the evolution, even including the particle structural phase transformation, of Au nano particles as catalyst for CO oxidation under the chemical reaction conditions. Using this approach, it may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms.
Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation

We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as well as predictions from the previous frame, to predict the bounding cuboid and 6- DoF pose (up to scale). Internally, a deep network predicts distributions over object keypoints (vertices of the bounding cuboid) in image coordinates, after which a novel probabilistic filtering process integrates across estimates before computing the final pose using PnP. Our framework allows the system to take previous uncertainties into consideration when predicting the current frame, resulting in predictions that are more accurate and stable than single frame methods. Extensive experiments show that our method outperforms existing approaches on the challenging Objectron benchmark of annotated object videos. We also demonstrate the usability of our work in an augmented reality setting.
What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys

Generating follow-up questions on the fly could significantly improve conversational survey quality and user experiences by enabling a more dynamic and personalized survey structure. In this paper, we proposed a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge in the context of conversational surveys. Along with the dataset, we designed and validated a set of reference-free Gricean-inspired evaluation metrics to systematically evaluate the quality of generated follow-up questions. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions by using knowledge to steer the generation process. The experiments demonstrate that compared to GPT-based baseline models, our two-staged model generates more informative, coherent, and clear follow-up questions.
Noether symmetries and first integrals of damped harmonic oscillator

Noether’s theorem establishes an interesting connection between symmetries of the action integral and conservation laws (ﬁrst integrals) of a dynamical system. The aim of the present work is to classify the damped harmonic oscillator problem with respect to Noether symmetries and to construct corresponding conservation laws for all over-damped, under-damped and critical damped cases. For each case we obtain maximum ﬁve linearly independent group generators which provide related ﬁve conserved quantities. Remarkably, after obtaining complete set of invariant quantities we obtain analytical solutions for each case. In the current work, we also introduce a new Lagrangian for the damped harmonic oscillator. Though the form of this new Lagrangian and presented by Bateman are completely diﬀerent, yet it generates same set of Noether symmetries and conserved quantities. So, this new form of Lagrangian we are presenting here may be seriously interesting for the physicists. Moreover, we also ﬁnd the Lie algebras of Noether symmetries and point out some interesting aspects of results related to Noether symmetries and ﬁrst integrals of damped harmonic oscillator which perhaps not reported in the earlier studies.
Improving automatically generated code from Codex via Automated Program Repair

Large language models, e.g., Codex and AlphaCode, have shown capability in producing working code for many programming tasks. However, the success rate of existing models remains low, especially for complex programming tasks. One of the reasons is that language models lack awareness of program semantics (e.g., type information), resulting in incorrect programs (or even programs which do not compile). In this paper, we systematically study whether automated program repair (APR) techniques can fix the incorrect solutions produced by language models in LeetCode contests. The goal is to study whether APR techniques can enhance confidence in the code produced by language models. Our study revealed that: (1) automatically generated codes share some common programming mistakes with human-crafted solutions, indicating existing APR tools have the potential to fix auto-generated code; (2) TBar and Recoder, two well-known Java APR tools based on templates and learning respectively, increase the number of solved tasks from 37 to 42 on 60 easy-level tasks, while increase from 5 to 9 on 53 medium-level programming tasks; (3) given bug location information provided by a statistical fault localization approach, the newly released Codex edit mode, which supports changing existing code, may outperform existing APR tools in fixing incorrect solutions. By analyzing the experimental results generated by these tools, we provide several suggestions: (1) as existing APR techniques are still quite limited, including limited patch space, fix locations and patch size, enhancing APR tool to surpass these limitations (e.g., introducing a more flexible fault localization strategy) is desirable; (2) as large language models can derive more fix patterns by training on more data, future APR tools should shift focus from adding more patterns to encoding more program semantics
Adaptive estimation for the nonparametric bivariate additive model in random design with long-memory dependent errors

We investigate the nonparametric bivariate additive regression estimation in the random design and long-memory errors and construct adaptive thresholding estimators based on wavelet series. The proposed approach achieves asymptotically near-optimal convergence rates when the unknown function and its univariate additive components belong to Besov space. We consider the problem under two noise structures; (1) homoskedastic Gaussian long memory errors and (2) heteroskedastic Gaussian long memory errors. In the homoskedastic long-memory error case, the estimator is completely adaptive with respect to the long-memory parameter. In the heteroskedastic long-memory case, the estimator may not be adaptive with respect to the long-memory parameter unless the heteroskedasticity is of polynomial form. In either case, the convergence rates depend on the long-memory parameter only when long-memory is strong enough, otherwise, the rates are identical to those under i.i. d. errors. The proposed approach is extended to the general $r$-dimensional additive case, with $r>2$, and the corresponding convergence rates are free from the curse of dimensionality.
Collaborative Adversarial Training

The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to local non-smoothness and steepness of normally obtained loss landscapes. Training augmented with adversarial examples ( a.k.a. , adversarial training) is considered as an effective remedy. In this paper, we highlight that some collaborative examples, nearly perceptually indistinguishable from both adversarial and benign examples yet show extremely lower prediction loss, can be utilized to enhance adversarial training. A novel method called collaborative adversarial training (CoAT) is thus proposed to achieve new state-of-the-arts. Code will be made publicly available.
Housekeep: Tidying Virtual Households using Commonsense Reasoning

We introduce Housekeep, a benchmark to evaluate commonsense reasoning in the home for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house. Specifically, we collect a dataset of where humans typically place objects in tidy and untidy houses constituting 1799 objects, 268 object categories, 585 placements, and 105 rooms. Next, we propose a modular baseline approach for Housekeep that integrates planning, exploration, and navigation. It leverages a fine-tuned large language model (LLM) trained on an internet text corpus for effective planning. We show that our baseline agent generalizes to rearranging unseen objects in unknown environments. See our webpage for more details: https://yashkant.github. io/housekeep/
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding

There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language. This work concerns addressing two major problems in existing Arabic PLMs which constraint progress of the Arabic NLU and NLG fields.First, existing Arabic PLMs are not well-explored and their pre-trainig can be improved significantly using a more methodical approach. Second, there is a lack of systematic and reproducible evaluation of these models in the literature. In this work, we revisit both the pre-training and evaluation of Arabic PLMs. In terms of pre-training, we explore improving Arabic LMs from three perspectives: quality of the pre-training data, size of the model, and incorporating character-level information. As a result, we release three new Arabic BERT-style models ( JABER, Char-JABER, and SABER), and two T5-style models (AT5S and AT5B). In terms of evaluation, we conduct a comprehensive empirical study to systematically evaluate the performance of existing state-of-the-art models on ALUE that is a leaderboard-powered benchmark for Arabic NLU tasks, and on a subset of the ARGEN benchmark for Arabic NLG tasks. We show that our models significantly outperform existing Arabic PLMs and achieve a new state-of-the-art performance on discriminative and generative Arabic NLU and NLG tasks. Our models and source code to reproduce of results will be made available shortly.
A Domain-adaptive Pre-training Approach for Language Bias Detection in News

Media bias is a multi-faceted construct influencing individual behavior and collective decision-making. Slanted news reporting is the result of one-sided and polarized writing which can occur in various forms. In this work, we focus on an important form of media bias, i.e. bias by word choice. Detecting biased word choices is a challenging task due to its linguistic complexity and the lack of representative gold-standard corpora. We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0.814. In addition, we also train, DA-BERT and DA-BART, two more transformer models adapted to the bias domain. Our proposed domain-adapted models outperform prior bias detection approaches on the same data. CCS CONCEPTS • Computing methodologies → Natural language processing; • Information systems → Clustering and classification.
Human Vehicle Dynamics Coupling Effect of Miniature Vehicle Roll Characteristic

In order to study the dynamics coupling effect between human and vehicle on the roll characteristics of miniature electric vehicle (MEV), firstly mathematical models for roll motion and stable steering of human-vehicle system were established, respectively. And then, virtual prototype models of driver and MEVs were built by ADAMS/Car. Finally, simulations were conducted. The influences of human-vehicle dynamics coupling effect (HVDCE) on roll characteristics of MEV with different curb weight were compared and analysed. Results show that the vehicle dynamics models with HVDCE exhibit the characteristic of lower equivalent roll angular stiffness and equivalent roll angular damping under transient excitation, and the characteristic of greater roll angular damping at low frequency excitation. Besides, the HVDCE will decrease natural frequency of roll motion. The greater the proportion of the driver mass to the curb weight is, the more obvious the effect of HVDCE on the roll characteristics will be.
Tackling Provably Hard Representative Selection via Graph Neural Networks

Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset. In this paper, we study RS for attributed graphs, and focus on finding representative nodes that optimize the accuracy of a model trained on the selected representatives. Theoretically, we establish a new hardness result forRS (in the absence of a graph structure) by proving that a particular, highly practical variant of it (RS for Learning) is hard to approximate in polynomial time within any reasonable factor, which implies a significant potential gap between the optimum solution of widely-used surrogate functions and the actual accuracy of the model. We then study the setting where a (homophilous) graph structure is available, or can be constructed, between the data points.We show that with an appropriate modeling approach, the presence of such a structure can turn a hard RS (for learning) problem into one that can be effectively solved. To this end, we develop RS-GNN, a representation learning-based RS model based on Graph Neural Networks. Empirically, we demonstrate the effectiveness of RS-GNN on problems with predefined graph structures as well as problems with graphs induced from node feature similarities, by showing that RS-GNN achieves significant improvements over established baselines on a suite of eight benchmarks.
mRNA based vaccines provide broad protection against different SARS-CoV-2 variants of concern

ABSTRACT In order to overcome the pandemic of COVID-19, messenger RNA (mRNA)-based vaccine has been extensively researched as a rapid and versatile strategy. Herein, we described the immunogenicity of mRNA-based vaccines for Beta and the most recent Omicron variants. The homologous mRNA-Beta and mRNA-Omicron and heterologous Ad5-nCoV plus mRNA vaccine exhibited high-level cross-reactive neutralization for Beta, original, Delta, and Omicron variants. It indicated that the COVID-19 mRNA vaccines have great potential in the clinical use against different SARS-CoV-2 variants.
Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR. We find that strong semantic correlations exist within each image and across different images, and these correlations can help transfer the knowledge possessed by the known labels to retrieve the unknown labels and thus improve the performance of the MLR-PL task (see Figure 1). In this work, we propose a novel heterogeneous semantic transfer (HST) framework that consists of two complementary transfer modules that explore both within-image and cross-image semantic correlations to transfer the knowledge possessed by known labels to generate pseudo labels for the unknown labels. Specifically, an intra-image semantic transfer (IST) module learns an image-specific label co-occurrence matrix for each image and maps the known labels to complement the unknown labels based on these matrices. Additionally, a cross-image transfer (CST) module learns category-specific feature-prototype similarities and then helps complement the unknown labels that have high degrees of similarity with the corresponding prototypes. Finally, both the known and generated pseudo labels are used to train MLR models. Extensive experiments conducted on the Microsoft COCO, Visual Genome, and Pascal VOC 2007 datasets show that the proposed HST framework achieves superior performance to that of current state-of-the-art algorithms. Specifically, it obtains mean average precision (mAP) improvements of 1.4%, 3.3%, and 0.4% on the three datasets over the results of the best-performing previously developed algorithm.
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute two new datasets, EcommerceMKP and AcademicMKP, covering six languages. Technically, we propose a retrieval-augmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages. The retrieval-augmented model leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. Given a non-English passage, a cross-lingual dense passage retrieval module finds relevant English passages. Then the associated English keyphrases serve as external knowledge for keyphrase generation in the current language. Moreover, we develop a retriever-generator iterative training algorithm to mine pseudo parallel passage pairs to strengthen the cross-lingual passage retriever. Comprehensive experiments and ablations show that the proposed approach outperforms all baselines.