Miracle-dz/UniFAR-FTUs huggingface dataset:
Reading and listening comprehension in Cantonese-speaking people with right hemisphere versus left hemisphere brain damage

ABSTRACT The problem in language comprehension in people with right hemisphere damage (RHD) is more equivocal than people with left hemisphere damage. This study explores the reading and listening comprehension of Cantonese-speaking individuals with RHD, left hemisphere damage, and neurotypical healthy controls using the Cantonese Computerized Revised Token Test (CRTT-Cantonese) adapted from the English CRTT. Eighteen native Cantonese-speaking individuals with RHD, 32 individuals with left hemisphere damage and aphasia (PWA), and 42 healthy controls participated in this study. All the participants completed the Cantonese Aphasia Battery, Hong Kong Oxford Cognitive Screen, the listening comprehension version of CRTT-Cantonese (CRTT-L-Cantonese), and the reading comprehension version of CRTT-Cantonese (CRTT-R-WF-Cantonese) across different sessions. Linear mixed-effect analysis revealed significant differences among the groups in CRTT-Cantonese tests. However, there were no significant difference between CRTT-L-Cantonese and CRTT-R-WF-Cantonese within the PWA, RHD and healthy control groups. Tukey post-hoc pairwise comparisons showed that PWA scored significantly lower than RHD and healthy control groups (p < 0.0001) in both CRTT-L-Cantonese and CRTT-R-WF-Cantonese, and the RHD group scored significantly lower than healthy control group only on the CRTT-R-WF-Cantonese. The results demonstrate that the CRTT-L-Cantonese and CRTT-R-WF-Cantonese differentiate language comprehension abilities among PWA, RHD and healthy control groups. Although the current findings did not show any diversion between reading and listening comprehension in RHD group, this group showed poorer performance in reading comprehension when compared to healthy controls. The latter findings may support the view that the right hemisphere contributes to reading comprehension in Chinese.
Arthroscopic Bankart Repair for Adolescent Anterior Shoulder Instability: Clinical and Imaging Predictors of Revision Surgery and Recurrent Subjective Instability

Background: Multiple clinical and radiologic risk factors for recurrent instability after arthroscopic Bankart repair have been described. Humeral bone loss has gained more recent attention, particularly with respect to “off-track” lesions and increased rates of recurrent instability and revision surgery. Purpose: To evaluate clinical and radiologic predictors of failure after arthroscopic Bankart repair in adolescents. Study Design: Case series; Level of evidence, 4. Methods: A single-institution retrospective study was performed in patients <19 years of age treated with arthroscopic Bankart repair from 2011 to 2017. Magnetic resonance imaging measurements of glenoid and humeral bone loss, the glenoid track, and the presence of off-track Hill-Sachs (HS) lesions were assessed. All patients had a minimum follow-up of 24 months and completed patient-reported outcome scores. Failure was defined as revision surgery or postoperative subjective instability. Results: A total of 59 patients (46 male, 13 female) with a median age of 16 years (range, 12-18 years) were included. Ten patients (17%) had revision surgery and 8 patients (14%) had subjective instability without revision surgery. No clinical or radiologic factors were significantly different between the failure cohort and the nonfailure cohort. Four patients (7%) measured off-track, and 2 of these patients experienced failure. A total of 38 patients (64%) were identified to have an HS defect. Subgroup analysis of these patients identified a greater HS interval (HSI) in patients who underwent revision surgery as compared with those patients who did not have revision surgery. Among patients with GT ratio ≥15 mm, there was a 50% rate of revision surgery. The Pediatric/Adolescent Shoulder Survey (PASS) and Single Assessment Numeric Evaluation (SANE) scores at the final follow-up were not significantly different among patients with or without revision surgery. However, those with subjective instability who had not undergone revision surgery had significantly lower PASS and SANE scores as compared with the remainder of the cohort. Conclusion: Of the adolescents in this cohort, 31% either had revision surgery (17%) or reported subjective feelings of instability (14%) after arthroscopic Bankart repair. Off-track instability was identified in 7% of the cohort but was not predictive of failure. Among the subgroup of patients with an HS defect, those who underwent revision surgery had a significantly larger HSI.
An EPTAS for Budgeted Matching and Budgeted Matroid Intersection

We study the budgeted versions of the well known matching and matroid intersection problems. While both problems admit a polynomial-time approximation scheme (PTAS) [Berger et al. (Math. Programming, 2011), Chekuri, Vondrak and Zenklusen (SODA 2011)], it has been an intriguing open question whether these problems admit a fully PTAS (FPTAS), or even an efficient PTAS (EPTAS). In this paper we answer the second part of this question affirmatively, by presenting an EPTAS for budgeted matching and budgeted matroid intersection. A main component of our scheme is a novel construction of representative sets for desired solutions, whose cardinality depends only on $\varepsilon$, the accuracy parameter. Thus, enumerating over solutions within a representative set leads to an EPTAS. This crucially distinguishes our algorithms from previous approaches, which rely on exhaustive enumeration over the solution set. Our ideas for constructing representative sets may find use in tackling other budgeted optimization problems, and are thus of independent interest.
AGN candidates in the VVV near-IR galaxy catalogue

The goal of this work is to search for Active Galactic Nuclei (AGN) in the Galactic disc at very low latitudes with |b| < 2○. For this, we studied the five sources from the VVV near-infrared galaxy catalogue that have also WISE counterparts and present variability in the VIrac VAriable Classification Ensemble (VIVACE) catalogue. In the near-infrared colour-colour diagrams, these objects have in general redder colours compared to the rest of the sources in the field. In the mid-infrared ones, they are located in the AGN region, however there is a source that presents the highest interstellar extinction and different mid-IR colours to be a young stellar object (YSO). We also studied the source variability using two different statistical methods. The fractional variability amplitude σrms ranges from 12.6 to 33.8, being in concordance with previous results found for type-1 AGNs. The slopes of the light curves are in the range (2.6−4.7) × 10−4 mag/day, also in agreement with results reported on quasars variability. The combination of all these results suggest that four galaxies are type-1 AGN candidates whereas the fifth source likely a YSO candidate.
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training

Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios. Modern learning-based approaches require large labeled datasets and tend to perform poorly outside the training domain. Our first contribution is to develop a robust corrector module that corrects pose estimates using depth information, thus enabling existing methods to better generalize to new test domains; the corrector operates on semantic keypoints (but is also applicable to other pose estimators) and is fully differentiable. Our second contribution is an ensemble self-training approach that simultaneously trains multiple pose estimators in a self-supervised manner. Our ensemble self-training architecture uses the robust corrector to refine the output of each pose estimator; then, it evaluates the quality of the outputs using observable correctness certificates; finally, it uses the observably correct outputs for further training, without requiring external supervision. As an additional contribution, we propose small improvements to a regression-based keypoint detection architecture, to enhance its robustness to outliers; these improvements include a robust pooling scheme and a robust centroid computation. Experiments on the YCBV and TLESS datasets show the proposed ensemble self-training outperforms fully supervised baselines while not requiring 3D annotations on real data.
Which Invariance Should We Transfer? A Causal Minimax Learning Approach

A major barrier to deploying current machine learning models lies in their non-reliability to dataset shifts. To resolve this problem, most existing studies attempted to transfer stable information to unseen environments. Particularly, independent causal mechanisms-based methods proposed to remove mutable causal mechanisms via the do-operator. Compared to previous methods, the obtained stable predictors are more effective in identifying stable information. However, a key question remains: which subset of this whole stable information should the model transfer, in order to achieve optimal generalization ability? To answer this question, we present a comprehensive minimax analysis from a causal perspective. Specifically, we first provide a graphical condition for the whole stable set to be optimal. When this condition fails, we surprisingly find with an example that this whole stable set, although can fully exploit stable information, is not the optimal one to transfer. To identify the optimal subset under this case, we propose to estimate the worst-case risk with a novel optimization scheme over the intervention functions on mutable causal mechanisms. We then propose an efficient algorithm to search for the subset with minimal worst-case risk, based on a newly defined equivalence relation between stable subsets. Compared to the exponential cost of exhaustively searching over all subsets, our searching strategy enjoys a polynomial complexity. The effectiveness and efficiency of our methods are demonstrated on synthetic data and the diagnosis of Alzheimer's disease.
ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

—Recent studies have shown that learning theories have been very successful in hydrocarbon exploration. Inversion of seismic into various attributes through the relationship of 1D well-logs and 3D seismic is an essential step in reservoir description, among which, acoustic impedance is one of the most critical attributes, and although current deep learning-based impedance inversion obtains promising results, it relies on a large number of logs (1D labels, typically more than 30 well-logs are required per inversion), which is unacceptable in many practical explorations. In this work, we deﬁne acoustic impedance inversion as a regression task for learning sparse 1D labels from 3D volume data and propose a voxel-wise semi-supervised contrastive learning framework, ContrasInver, for regression tasks under sparse labels. ConstraInver consists of several key components, including a novel pre-training method for 3D seismic data inversion, a contrastive semi-supervised strategy for diffusing well-log information to the global, and a continuous-value vectorized characterization method for a contrastive learning-based regression task, and also designed the distance TopK sampling method for improving the training efﬁciency. We performed a complete ablation study on SEAM Phase I synthetic data to verify the effectiveness of each component and compared our approach with the current mainstream methods on this data, and our approach demonstrated very signiﬁcant advantages. In this data we achieved an SSIM of 0.92 and an MSE of 0.079 with only four well-logs. ConstraInver is the ﬁrst purely data-driven approach to invert two classic ﬁeld data, F3 Netherlands (only four well-logs) and Delft (only three well-logs) and achieves very reasonable and reliable results.
Compositional Exemplars for In-context Learning

Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task via a prompt consisting of input-output examples as the demonstration, without any parameter updates. The performance of ICL is highly dominated by the quality of the selected in-context examples. However, previous selection methods are mostly based on simple heuristics, leading to sub-optimal performance. In this work, we formulate in-context example selection as a subset selection problem. We propose CEIL (Compositional Exemplars for In-context Learning), which is instantiated by Determinantal Point Processes (DPPs) to model the interaction between the given input and in-context examples, and optimized through a carefully-designed contrastive learning objective to obtain preference from LMs. We validate CEIL on 12 classification and generation datasets from 7 distinct NLP tasks, including sentiment analysis, paraphrase detection, natural language inference, commonsense reasoning, open-domain question answering, code generation, and semantic parsing. Extensive experiments demonstrate not only the state-of-the-art performance but also the transferability and compositionality of CEIL, shedding new light on effective and efficient in-context learning. Our code is released at https://github.com/HKUNLP/icl-ceil.
Sparse Dimensionality Reduction Revisited

The sparse Johnson-Lindenstrauss transform is one of the central techniques in dimensionality reduction. It supports embedding a set of $n$ points in $\mathbb{R}^d$ into $m=O(\varepsilon^{-2} \lg n)$ dimensions while preserving all pairwise distances to within $1 \pm \varepsilon$. Each input point $x$ is embedded to $Ax$, where $A$ is an $m \times d$ matrix having $s$ non-zeros per column, allowing for an embedding time of $O(s \|x\|_0)$. Since the sparsity of $A$ governs the embedding time, much work has gone into improving the sparsity $s$. The current state-of-the-art by Kane and Nelson (JACM'14) shows that $s = O(\varepsilon ^{-1} \lg n)$ suffices. This is almost matched by a lower bound of $s = \Omega(\varepsilon ^{-1} \lg n/\lg(1/\varepsilon))$ by Nelson and Nguyen (STOC'13). Previous work thus suggests that we have near-optimal embeddings. In this work, we revisit sparse embeddings and identify a loophole in the lower bound. Concretely, it requires $d \geq n$, which in many applications is unrealistic. We exploit this loophole to give a sparser embedding when $d = o(n)$, achieving $s = O(\varepsilon^{-1}(\lg n/\lg(1/\varepsilon)+\lg^{2/3}n \lg^{1/3} d))$. We also complement our analysis by strengthening the lower bound of Nelson and Nguyen to hold also when $d \ll n$, thereby matching the first term in our new sparsity upper bound. Finally, we also improve the sparsity of the best oblivious subspace embeddings for optimal embedding dimensionality.
MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures

In this paper, we present a novel approach to knowledge extraction and retrieval using Natural Language Processing (NLP) techniques for material science. Our goal is to automatically mine structured knowledge from millions of research articles in the field of polycrystalline materials and make it easily accessible to the broader community. The proposed method leverages NLP techniques such as entity recognition and document classification to extract relevant information and build an extensive knowledge base, from a collection of 9.5 Million publications. The resulting knowledge base is integrated into a search engine, which enables users to search for information about specific materials, properties, and experiments with greater precision than traditional search engines like Google. We hope our results can enable material scientists quickly locate desired experimental procedures, compare their differences, and even inspire them to design new experiments. Our website will be available at Github \footnote{https://github.com/Xianjun-Yang/PcMSP.git} soon.
Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming

Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to deep learning models to provide robustness and reliability. Due to the growth of the models in deep learning, using ensemble pruning is highly important to deal with computational complexity. Hence, this study proposes a mathematical model which prunes the ensemble of Convolutional Neural Networks (CNN) consisting of different depths and layers that maximizes accuracy and diversity simultaneously with a sparse second order conic optimization model. The proposed model is tested on CIFAR-10, CIFAR-100 and MNIST data sets which gives promising results while reducing the complexity of models, significantly.
Optical flow estimation from event-based cameras and spiking neural networks

Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or “spikes”, when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent qualities, such as their low power consumption, low latency, and high dynamic range, they seem particularly tailored to applications with challenging temporal constraints and safety requirements. Event-based sensors are an excellent fit for Spiking Neural Networks (SNNs), since the coupling of an asynchronous sensor with neuromorphic hardware can yield real-time systems with minimal power requirements. In this work, we seek to develop one such system, using both event sensor data from the DSEC dataset and spiking neural networks to estimate optical flow for driving scenarios. We propose a U-Net-like SNN which, after supervised training, is able to make dense optical flow estimations. To do so, we encourage both minimal norm for the error vector and minimal angle between ground-truth and predicted flow, training our model with back-propagation using a surrogate gradient. In addition, the use of 3d convolutions allows us to capture the dynamic nature of the data by increasing the temporal receptive fields. Upsampling after each decoding stage ensures that each decoder's output contributes to the final estimation. Thanks to separable convolutions, we have been able to develop a light model (when compared to competitors) that can nonetheless yield reasonably accurate optical flow estimates.
A photometric map of interstellar reddening within 300 parsecs.

An observational program is described which has as its goal a high resolution, three-dimensional survey of the dust component of the interstellar medium in the solar neighborhood. A network of approx.3450 bright A and F stars was established in the northern hemisphere with the intent that the stars act as probes for the detection of interstellar reddening. In conjunction with empirical calibrations by Crawford, uupsilonby.beta. photometries yielded precise intrinsic colors and absolute magnitudes that, in turn, allowed the determination of the color excess and the position of each star in a rectangular coordinate system centered on the Sun. The results are summarized in a series of plots where several dust concentrations are readily apparent. In order of galactic longitude, these regions correspond to the inner portions of the Ophiuchus, Cepheus, and Taurus cloud complexes. A bridge of absorbing material, passing through Cassiopeia and Perseus, connects the latter two concentrations; the Ophiuchus complex extends into Aquila. These concentrations are characterized by small-scale irregularities both in density and spatial event. Comparison of the present results with previous investigations shows that this study provides a more definitive map of the spatial distribution of interstellar dust within several hundred parsecs of the Sun.
Late emission from supernovae - A window on stellar nucleosynthesis

Monte Carlo techniques are used to study the energy deposition of gamma rays from the radioactive decay of Co-56 in Type Ib and Type II SN. It is found that above an electron fraction of about 0.1, direct excitations are negligible, and the line emission arises from thermal processes. The results indicate that the emission is dominated by neutral and singly ionized lines, including forbiden O I, forbidden Ca II, forbidden C I, and forbidden Si I. The late spectrum is shown to be useful as a diagnostic of the nucleosynthetic structure of the ejecta. The present model has been applied to observations of the Type Ib SN 1985F, and it is suggested that Type Ib SN come from stars of greater than 25 solar masses. 114 refs.
CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models

CatAlyst uses generative models to help workers’ progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers to resume their tasks by generating a continuation of their work and presenting it as an intervention that is more context-aware than conventional (predetermined) feedback. The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy. This frees CatAlyst from domain-specific model-tuning and makes it applicable to various tasks. Our studies involving writing and slide-editing tasks demonstrated CatAlyst’s effectiveness in helping workers swiftly resume tasks with a lowered cognitive load. The results suggest a new form of human-AI collaboration where large generative models publicly available but imperfect for each individual domain can contribute to workers’ digital well-being.
A Reparameterized Discrete Diffusion Model for Text Generation

This work studies discrete diffusion probabilistic models with applications to natural language generation. We derive an alternative yet equivalent formulation of the sampling from discrete diffusion processes and leverage this insight to develop a family of reparameterized discrete diffusion models. The derived generic framework is highly flexible, offers a fresh perspective of the generation process in discrete diffusion models, and features more effective training and decoding techniques. We conduct extensive experiments to evaluate the text generation capability of our model, demonstrating significant improvements over existing diffusion models.
Topological dimensions of random attractors for stochastic partial differential equations with delay

The aim of this paper is to obtain an estimation of Hausdorff as well as fractal dimensions of random attractors for a class of stochastic partial differential equations with delay. The stochastic equation is first transformed into a delayed random partial differential equation by means of a random conjugation, which is then recast into an auxiliary Hilbert space. For the obtained equation, it is firstly proved that it generates a random dynamical system (RDS) in the auxiliary Hilbert space. Then it is shown that the equation possesses random attractors by a uniform estimate of the solution and the asymptotic compactness of the generated RDS. After establishing the variational equation in the auxiliary Hilbert space and the $\mathbb{P}$ almost surely differentiable properties of the RDS, an upper estimate of both Hausdorff and fractal dimensions of the random attractors are obtained.