Miracle-dz/UniFAR-FTUs huggingface dataset:
On the low ortho-to-para H2 ratio in star-forming filaments

The formation of stars and planetary systems is a complex phenomenon that relies on the interplay of multiple physical processes. Nonetheless, it represents a crucial stage for our understanding of the Universe, and in particular of the conditions leading to the formation of key molecules (e.g. water) on comets and planets. Herschel observations demonstrated that stars form in gaseous filamentary structures in which the main constituent is molecular hydrogen (H2). Depending on its nuclear spin H2 can be found in two forms: ‘ortho’ with parallel spins and ‘para’ where the spins are anti-parallel. The relative ratio among these isomers, the ortho-to-para ratio (OPR), plays a crucial role in a variety of processes related to the thermodynamics of star-forming gas and to the fundamental chemistry affecting the deuteration of water in molecular clouds, commonly used to determine the origin of water in Solar System bodies. Here, for the first time, we assess the evolution of the OPR starting from the warm neutral medium by means of state-of-the-art 3D magnetohydrodynamic simulations of turbulent molecular clouds. Our results show that star-forming clouds exhibit a low OPR (≪0.1) already at moderate densities (∼1000 cm−3). We also constrain the cosmic-ray ionisation rate, finding that 10−16 s−1 is the lower limit required to explain the observations of diffuse clouds. Our results represent a step forward in the understanding of the star and planet formation processes providing a robust determination of the chemical initial conditions for both theoretical and observational studies.
Individual Mobility Prediction via Attentive Marked Temporal Point Processes

Individual mobility prediction is an essential task for transportation demand management and traffic system operation. There exist a large body of works on modeling location sequence and predicting the next location of users; however, little attention is paid to the prediction of the next trip, which is governed by the strong spatiotemporal dependencies between diverse attributes, including trip start time $t$, origin $o$, and destination $d$. To fill this gap, in this paper we propose a novel point process-based model -- Attentive Marked temporal point processes (AMTPP) -- to model human mobility and predict the whole trip $(t,o,d)$ in a joint manner. To encode the influence of history trips, AMTPP employs the self-attention mechanism with a carefully designed positional embedding to capture the daily/weekly periodicity and regularity in individual travel behavior. Given the unique peaked nature of inter-event time in human behavior, we use an asymmetric log-Laplace mixture distribution to precisely model the distribution of trip start time $t$. Furthermore, an origin-destination (OD) matrix learning block is developed to model the relationship between every origin and destination pair. Experimental results on two large metro trip datasets demonstrate the superior performance of AMTPP.
POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling

Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.
SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"

Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as"Muslim"and"black". Such bias is commonly reflected in false-positive predictions, i.e. non-toxic comments with identity terms. In this work, we propose a novel approach to tackle such bias in toxic comment classification, leveraging the notion of subjectivity level of a comment and the presence of identity terms. We hypothesize that when a comment is made about a group of people that is characterized by an identity term, the likelihood of that comment being toxic is associated with the subjectivity level of the comment, i.e. the extent to which the comment conveys personal feelings and opinions. Building upon the BERT model, we propose a new structure that is able to leverage these features, and thoroughly evaluate our model on 4 datasets of varying sizes and representing different social media platforms. The results show that our model can consistently outperform BERT and a SOTA model devised to address identity term bias in a different way, with a maximum improvement in F1 of 2.43% and 1.91% respectively.
Unbiased Hate Speech Detection; Bringing the Bias back to the Table

Approaches for unbiased classiﬁcation tend to prevent models from overly relying on pre-served features of the input data. However, in the case of hate speech detection, while unbalanced distribution of the social group tokens, among the positive and negative instances, causes unintended bias in the models, it is not desirable to ignore these features due to their importance in recognizing hate speech. In this paper, we study hate speech detection as a sample problem in which the task is basically dependent on preserved features. We apply an unbiased model to a subtask of the problem – recognizing offensive language – which does not directly rely on the preserved features, and aggregate the results with mentioned preserved tokens to recognize hate speech.
Augmenting Question Answering with Natural Language Explanations CSCI-699 Final

Towards building annotation-efﬁcient question answering (QA) systems for real information seeking needs, we propose a framework that efﬁciently augments training data by leveraging natural language explanations to annotate unanswered questions. Explanations describing how an answer is arrived for one reference QA instance are ﬁrst parsed into executable rules, and then applied to large-scale unanswered questions (e.g. questions raised in search engines, online forums). Answers with high conﬁdence will be regarded as a training instance and potentially improve QA model performance, especially in low-resource setting. We highlight (1) the generalization ability in rule matching and (2) annotation efﬁ-ciency in our proposed framework. Currently, we have collected 200+ explanations for SQuAD instances. The parser can successfully parse and validate 42% of them. We expect the parser to be further improved with the expansion of predicate dictionary. Meanwhile, we have several reasonable observations in our case study of rule hard-matching, such as the trade-off between precision and coverage and variations in acceptable answers.
SemAlign: Annotation-Free Camera-LiDAR Calibration with Semantic Alignment Loss

Multi-sensor solution has been widely adopted in real-world robotics systems (e.g., self-driving vehicles) due to its better robustness. However, its performance is highly dependent on the accurate calibration between different sensors, which is very time-consuming (i.e., hours of human efforts) to acquire. Recent learning-based solutions partially address this yet still require costly ground-truth annotations as supervision. In this paper, we introduce a novel self-supervised semantic alignment loss to quantitatively measure the quality of a given calibration. It is well correlated with conventional evaluation metrics while it does not require ground-truth calibration annotations as the reference. Based on this loss, we further propose an annotation-free optimization-based calibration algorithm (SemAlign) that first estimates a coarse calibration with loss-guided initialization and then refines it with gradient-based optimization. SemAlign reduces the calibration time from hours of human efforts to only seconds of GPU computation. It not only achieves comparable performance with existing supervised learning frameworks but also demonstrates a much better generalization capability when transferred to a different dataset.
Generative Adversarial Networks (GANs) Synthetic report

In 2014, Ian GOODFELLOW invented GANs [1] and was then nicknamed "the GANfather". GANs introduce the concept of adversarial learning, as they lie in the rivalry between two neural networks. These techniques have enabled researchers to create realistic-looking but entirely computergenerated photos of people’s faces. They have also allowed the creation of controversial "deepfake" videos. Actually, GANs can be used to imitate any data distribution (image, text, sound, etc.).
A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification

Recipe sharing websites have become very popular in the past years, allowing individuals to use such systems in an attempt to find a desired recipe. But sometimes finding recipes which best fit the user's wishes, while still satisfying his food restrictions, may become a very time consuming and difficult task. In this work, we propose a recipe multi-label classification approach as part of a recipe recommendation system for people with food restrictions, in an attempt to automatically identify whether an input recipe or list of ingredients fits into one or more food restrictions, satisfying both user's expectations and needs. The experimental evaluation includes two approaches for feature selection, as a manner to reduce the computational costs for the proposed system.
The pyglaf argumentation reasoner (ICCMA2021)

The PYGLAF reasoner takes advantage of circumscription to solve computational problems of abstract argumentation frameworks. In fact, many of these problems are reduced to circumscription by means of linear encodings, and a few others are solved by means of a sequence of calls to an oracle for circumscription. Within PYGLAF, Python is used to build the encodings and to control the execution of the external circumscription solver, which extends the SAT solver GLUCOSE and implements algorithms taking advantage of unsatisfiable core analysis and incremental computation.
Bayesian Data Selection

Insights into complex, high-dimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduce the "data selection" problem: finding a lower-dimensional statistic—such as a subset of variables—that is well fit by a given parametric model of interest. A fully Bayesian approach to data selection would be to parametrically model the value of the statistic, nonparametrically model the remaining "background" components of the data, and perform standard Bayesian model selection for the choice of statistic. However, fitting a nonparametric model to high-dimensional data tends to be highly inefficient, statistically and computationally. We propose a novel score for performing data selection, the "Stein volume criterion (SVC)", that does not require fitting a nonparametric model. The SVC takes the form of a generalized marginal likelihood with a kernelized Stein discrepancy in place of the Kullback–Leibler divergence. We prove that the SVC is consistent for data selection, and establish consistency and asymptotic normality of the corresponding generalized posterior on parameters. We apply the SVC to the analysis of single-cell RNA sequencing data sets using probabilistic principal components analysis and a spin glass model of gene regulation.
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing the full picture. Such systems have inspired development of artificial intelligence algorithms in areas such as swarm optimization and cellular automata. Motivated by the emergence of collective behavior from complex cellular systems, we build systems that feed each sensory input from the environment into distinct, but identical neural networks, each with no fixed relationship with one another. We show that these sensory networks can be trained to integrate information received locally, and through communication via an attention mechanism, can collectively produce a globally coherent policy. Moreover, the system can still perform its task even if the ordering of its inputs is randomly permuted several times during an episode. These permutation invariant systems also display useful robustness and generalization properties that are broadly applicable. Interactive demo and videos of our results: https://attentionneuron.github. io/
Deficits in Working Memory and Theory of Mind May Underlie Difficulties in Social Perception of Children with ADHD

Children with attention deficit hyperactivity disorder (ADHD) are prone to peer rejection and disliking due to difficulties in social perception and interaction. To address social perception impairments in ADHD, we examined children with ADHD in a noisy biological motion (BM) direction discrimination paradigm in association with sociocognitive factors including emotion regulation, theory of mind (TOM), and working memory compared to healthy controls. Our results showed that children with ADHD were poorer in discriminating BM direction in noisy environments (F (1, 36) = 4.655, p=0.038). Moreover, a significant correlation was found between working memory and TOM with BM discrimination in an ADHD group (r = 0.442, p=0.01, and r = 0.403, p=0.05, respectively). Our findings could suggest that social perception in noisy scenarios may be affected by memory and social cognitive abilities of children with ADHD.
Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.
Aspartix-V21

In this solver description we present ASPARTIX-V, in its 2021 edition, which participates in the International Competition on Computational Models of Argumentation (ICCMA) 2021. ASPARTIX-V is capable of solving all classical (static) reasoning tasks part of ICCMA'21 and extends the ASPARTIX system suite by incorporation of recent ASP language constructs (e.g. conditional literals), domain heuristics within ASP, and multi-shot methods. In this light ASPARTIX-V deviates from the traditional focus of ASPARTIX on monolithic approaches (i.e., one-shot solving via a single ASP encoding) to further enhance performance.
Interferometric sensing of a commercial geophone

We present a modified commercial L-4C geophone with interferometric readout that demonstrates a resolution 60 times lower than the included coil-magnet readout at low frequencies. The intended application for the modified sensor is in vibration isolation platforms that require improved performance at frequencies lower than 1 Hz. To illustrate it’s application a controls- and noise-model of an Advanced LIGO ‘HAM-ISI’ vibration isolation system was developed, and shows that our sensor can reduce the residual motion of the platforms by a factor of 70 at 0.1 Hz.
Comprehensive Analysis of YTH Domain Family in Lung Adenocarcinoma: Expression Profile, Association with Prognostic Value, and Immune Infiltration

Background All YTH domain family members are m6A reader proteins accounting for the methylation modulation involved in the process of tumorgenesis and tumor progression. However, the expression profiles and roles of the YTH domain family in lung adenocarcinoma (LUAD) remain to be further illustrated. Methods GEPIA2 and TNMplot databases were used to generate the expression profiles of the YTH family. Kaplan-Meier plotter database was employed to analysis the prognostic value of the YTH family. Coexpression profiles and genetic alterations analysis of the YTH family were undertaken using the cBioPortal database. YTH family protein-associated protein-protein interaction (PPI) network was identified by using STRING. Functional enrichment analysis was performed with the help of the WebGestalt database. The correlation analysis between the YTH family and immune cell infiltration in LUAD was administrated by using the TIMER2.0 database. Results mRNA expression of YTHDC1 and YTHDC2 was significantly lower in LUAD, whereas YTHDF1, YTHDF2, and YTHDF3 with apparently higher expression. YTHDF2 expression was observed to be the highest in the nonsmoker subgroup, and its expression gradually decreased with the increased severity of smoking habit. LUAD patients with low expression of YTHDC2, YTHDF1, and YTHDF2 were correlated with a better overall survival (OS) time. The YTHDF1 genetic alteration rate was 26%, which was the highest in the YTH family. The major cancer-associated functions of YTH family pointed in the direction of immunomodulation, especially antigen processing and presentation. Most of the YTH family members were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, and neutrophils, indicating the deep involvement of the YTH domain family in the immune cell infiltration in LUAD. Conclusion The molecular and expression profiles of the YTH family were dysregulated in LUAD. YTH family members (especially YTHDC2) were promising biomarkers and potential therapeutic targets that may bring benefit for the patients with LUAD.
Revisiting Recursive Least Squares for Training Deep Neural Networks

Recursive least squares (RLS) algorithms were once widely used for training small-scale neural networks, due to their fast convergence. However, previous RLS algorithms are unsuitable for training deep neural networks (DNNs), since they have high computational complexity and too many preconditions. In this paper, to overcome these drawbacks, we propose three novel RLS optimization algorithms for training feedforward neural networks, convolutional neural networks and recurrent neural networks (including long short-term memory networks), by using the error backpropagation and our average-approximation RLS method, together with the equivalent gradients of the linear least squares loss function with respect to the linear outputs of hidden layers. Compared with previous RLS optimization algorithms, our algorithms are simple and elegant. They can be viewed as an improved stochastic gradient descent (SGD) algorithm, which uses the inverse autocorrelation matrix of each layer as the adaptive learning rate. Their time and space complexities are only several times those of SGD. They only require the loss function to be the mean squared error and the activation function of the output layer to be invertible. In fact, our algorithms can be also used in combination with other first-order optimization algorithms without requiring these two preconditions. In addition, we present two improved methods for our algorithms. Finally, we demonstrate their effectiveness compared to the Adam algorithm on MNIST, CIFAR-10 and IMDB datasets, and investigate the influences of their hyperparameters experimentally.
Large-Scale System Identification Using a Randomized SVD

Learning a dynamical system from input/output data is a fundamental task in the control design pipeline. In the partially observed setting there are two components to identification: parameter estimation to learn the Markov parameters, and system realization to obtain a state space model. In both sub-problems it is implicitly assumed that standard numerical algorithms such as the singular value decomposition (SVD) can be easily and reliably computed. When trying to fit a high-dimensional model to data, even computing an SVD may be intractable. In this work we show that an approximate matrix factorization obtained using randomized methods can replace the standard SVD in the realization algorithm while maintaining the finite-sample performance and robustness guarantees of classical methods.
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning

Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.However, the joint training scheme of multitask learning suffers from inter-task interference, and diagnosis prediction among the multiple tasks has the generalizability issue due to rare diseases or unseen diagnoses.To solve these challenges, we propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning.We also incorporate semantic task information to improve the generalizability of our task-conditioned multitask model. Experiments on early and discharge notes extracted from the real-world MIMIC database show our method can achieve better performance on multitask patient outcome prediction than strong baselines in most cases.Besides, our method can effectively handle the scenario with limited information and improve zero-shot prediction on unseen diagnosis categories.