Miracle-dz/UniFAR-FTUs huggingface dataset:
Stepwise progression of β-selection during T cell development involves histone deacetylation

T cells undergoing beta-selection differentiate via sequential expression of co-receptors CD28, CD5, and CD2, exposing new signalling controls that are disrupted by a cancer therapeutic. During T cell development, the first step in creating a unique T cell receptor (TCR) is genetic recombination of the TCRβ chain. The quality of the new TCRβ is assessed at the β-selection checkpoint. Most cells fail this checkpoint and die, but the coordination of fate at the β-selection checkpoint is not yet understood. We shed new light on fate determination during β-selection using a selective inhibitor of histone deacetylase 6, ACY1215. ACY1215 disrupted the β-selection checkpoint. Characterising the basis for this disruption revealed a new, pivotal stage in β-selection, bookended by up-regulation of TCR co-receptors, CD28 and CD2, respectively. Within this “DN3bPre” stage, CD5 and Lef1 are up-regulated to reflect pre-TCR signalling, and their expression correlates with proliferation. These findings suggest a refined model of β-selection in which a coordinated increase in expression of pre-TCR, CD28, CD5 and Lef1 allows for modulating TCR signalling strength and culminates in the expression of CD2 to enable exit from the β-selection checkpoint.
Untargeted metabolomic analysis of aqueous humor in diabetic macular edema

Background The mechanism of diabetic macular edema (DME) was explored by comparing the intraocular metabolite profiles of the aqueous humor of patients with DME to those of diabetic patients without DME using untargeted metabolomic analysis. Methods Aqueous samples from 18 type 2 diabetic patients with DME and 18 type 2 diabetic patients without DME used as controls were analyzed using liquid chromatography–mass spectrometry (LCMS). The two groups of patients were age and gender matched and had no systemic diseases other than diabetes mellitus (DM). The metabolites were analyzed using orthogonal partial least square discriminant analysis. Results The metabolite profiles in DME patients differed from those in DM controls. This indicates the following metabolic derangements in DME: (a) a higher amount of oxidized fatty acids but a lower amount of endogenous antioxidants (oxidative stress); (b) higher levels of β-glucose and homocysteine but a lower level of sorbitol (hyperglycemia); (c) a higher amount of prostaglandin metabolites (inflammation); (d) higher amounts of acylcarnitines, odd-numbered fatty acids, and 7,8-diaminononanoate (respiration deterioration); (e) a higher amount of neurotransmitter metabolites and homovanillic acid (neuronal damage); (f) a lower amount of extracellular matrix (ECM) constituents (ECM deterioration); and (g) a higher amount of di-amino peptides (microvascular damage). Conclusions The change in the metabolic profiles in the aqueous humor of DME patients compared to DM controls without DME indicates that DME patients may have less capability to resist various stresses or damaging pathological conditions, such as oxidative stress, mitochondrial insufficiency, inflammation, and ECM deterioration.
Guidelines on the use of disinfectants: comparison between Malaysia and other countries

Aim: Sanitation and cleanliness are essential factors in reducing the spread of pathogens and preventing healthcare-associated infections. Disinfectants are associated with better hygiene outcomes to reduce pathogen transmission risk and minimize risks to healthcare workers (HCWs) and patients. Methods: A literature search was undertaken using the electronic databases Scopus, Web of Science, Ovid and Google Scholar. The inclusion criteria for this study are observational and original research studies dating from the five-year period 2017–2021. Other inclusion criteria are full text, English language, qualitative or quantitative studies relevant to the research question. The exclusion criteria are animal studies, systematic reviews, conference proceedings, abstracts, projection modelling studies, in-vivo or in-vitro studies, and books. Results: Five study nations included the United States of America (USA), the United Kingdom (UK), China, India and South Korea, together with Malaysia. These nations have existing policies, regulations and guidelines regarding the use of disinfectants. HCWs should be aware of the national laws and guidelines that govern the purchase, distribution and use of disinfectants. They should also understand the different roles of the agencies involved, so the context for the guidance provided is clear. Coordination and collaboration across various stakeholders are required for creating solid policies. Conclusion: Product research and innovation are indispensable, as appropriate personal protective equipment and safety measures for HCWs and patients have top priority in every nation. Hence, clear guidelines for handling disinfectants, in addition to health education about scientific-evidence-based disinfectants, are required.
Optical Photometry of the Type II-P Supernova 2004dj in NGC 2403

We present photometric data of the Type II-P supernova (SN) 2004dj in NGC 2403. The multicolor light curves cover the SN from ∼60 to 200 days after explosion and are measured with a set of intermediate-band filters that have the advantage of tracing the strength variations of some spectral features. The light curves show a flat evolution in the middle of the plateau phase, then decline exponentially at the late times, with a rate of 0.10 ± 0.03 mag (10 days)-1 in most of the filters. In the nebular phase, the spectral energy distribution of SN 2004dj shows a steady increase in the flux near 6600 and 8500 Å, which may correspond to the emission lines of Hα and the Ca II near-IR triplet, respectively. The photometric behavior suggests that SN 2004dj is a normal SN II-P. Compared with the light curves of another typical SN II-P, 1999em, we estimate the explosion date to be 2004 June 10 ± 21 UT (JD 2,453,167 ± 21) for SN 2004dj. We also estimate the ejected nickel mass during the explosion to be M(56Ni) = 0.023 ± 0.005 M⊙ from two different methods, which is typical for a SN II-P. We derive the explosion energy to be E ≈ 0.75 × 1051 ergs, the ejecta mass to be M ≈ 10.0 M⊙, and the initial radius to be R ≈ 282 R⊙ for the presupernova star of SN 2004dj, which are consistent with other typical SNe II-P.
Benchmarking Language Models for Code Syntax Understanding

Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence without explicitly modeling its structure. Some prior works show that pre-trained language models can capture the syntactic rules of natural languages without finetuning on syntax understanding tasks. However, there is limited understanding of how well pre-trained models understand the code structure so far. In this work, we perform the first thorough benchmarking of the state-of-the-art pre-trained models for identifying the syntactic structures of programs. Specifically, we introduce CodeSyntax, a large-scale dataset of programs annotated with the syntactic relationships in their corresponding abstract syntax trees. Our key observation is that existing language models pretrained on code still lack the understanding of code syntax. In fact, these pre-trained programming language models fail to match the performance of simple baselines based on positional offsets and keywords. We also present a natural language benchmark to highlight the differences between natural languages and programming languages in terms of syntactic structure understanding. Our findings point out key limitations of existing pre-training methods for programming languages, and suggest the importance of modeling code syntactic structures.
Decoupled Mixup for Generalized Visual Recognition

Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models often largely degrade on testing data which is unseen and Out-Of-the-Distribution (OOD). To address this issue, we propose a novel"Decoupled-Mixup"method to train CNN models for OOD visual recognition. Different from previous work combining pairs of images homogeneously, our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions of image pairs to train CNN models. Since the observation is that noise-prone regions such as textural and clutter backgrounds are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair. To further improve the generalization ability of trained models, we propose to disentangle discriminative and noise-prone regions in frequency-based and context-based fashions. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts, where our method achieves 85.76\% top-1 accuracy in Track-1 and 79.92\% in Track-2 in the NICO Challenge. The source code is available at https://github.com/HaozheLiu-ST/NICOChallenge-OOD-Classification.
FedX: Federated Learning for Compositional Pairwise Risk Optimization

In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of compositional pairwise risks, to which no existing FL algorithms are applicable. In particular, the objective has the form of E z ∼S 1 f ( E z (cid:48) ∼S 2 (cid:96) ( w , z , z (cid:48) )), where two sets of data S 1 , S 2 are distributed over multiple machines, (cid:96) ( · ; · , · ) is a pairwise loss that only depends on the prediction outputs of the input data pairs ( z , z (cid:48) ), and f ( · ) is possibly a non-linear non-convex function. This problem has important applications in machine learning, e.g., AUROC maximization with a pairwise loss, and partial AUROC maximization with a compositional loss. The challenges for designing an FL algorithm lie in the non-decomposability of the objective over multiple machines and the interdependency between diﬀerent machines. We propose two provable FL algorithms (FedX) for handling linear and nonlinear f , respectively. To address the challenges, we decouple the gradient’s components with two types, namely active parts and lazy parts, where the active parts depend on local data that are computed with the local model and the lazy parts depend on other machines that are communicated/computed based on historical models and samples. We develop a novel theoretical analysis to combat the latency of the lazy parts and the interdependency between the local model parameters and the involved data for computing local gradient estimators. We establish both iteration and communication complexities and show that using the historical samples and models for computing the lazy parts do not degrade the complexities. We conduct empirical studies of FedX for deep AUROC and partial AUROC maximization, and demonstrate their performance compared with several baselines.
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network

The information bottleneck (IB) method is a feasible defense solution against adversarial attacks in deep learning. However, this method suffers from the spurious correlation, which leads to the limitation of its further improvement of adversarial robustness. In this paper, we incorporate the causal inference into the IB framework to alleviate such a problem. Specifically, we divide the features obtained by the IB method into robust features (content information) and non-robust features (style information) via the instrumental variables to estimate the causal effects. With the utilization of such a framework, the influence of non-robust features could be mitigated to strengthen the adversarial robustness. We make an analysis of the effectiveness of our proposed method. The extensive experiments in MNIST, FashionMNIST, and CIFAR-10 show that our method exhibits the considerable robustness against multiple adversarial attacks. Our code would be released.
VAEs with structured image covariance applied to compressed sensing MRI

Objective.This paper investigates how generative models, trained on ground-truth images, can be used as priors for inverse problems, penalizing reconstructions far from images the generator can produce. The aim is that learned regularization will provide complex data-driven priors to inverse problems while still retaining the control and insight of a variational regularization method. Moreover, unsupervised learning, without paired training data, allows the learned regularizer to remain flexible to changes in the forward problem such as noise level, sampling pattern or coil sensitivities in MRI. Approach. We utilize variational autoencoders that generate not only an image but also a covariance uncertainty matrix for each image. The covariance can model changing uncertainty dependencies caused by structure in the image, such as edges or objects, and provides a new distance metric from the manifold of learned images. Main results. We evaluate these novel generative regularizers on retrospectively sub-sampled real-valued MRI measurements from the fastMRI dataset. We compare our proposed learned regularization against other unlearned regularization approaches and unsupervised and supervised deep learning methods. Significance. Our results show that the proposed method is competitive with other state-of-the-art methods and behaves consistently with changing sampling patterns and noise levels.
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github. io/diffusiondb.
An adaptive finite element/finite difference domain decomposition method for applications in microwave imaging

A new domain decomposition method for Maxwell’s equations in conductive media is presented. Using this method, reconstruction algorithms are developed for the determination of the dielectric permittivity function using time-dependent scattered data of an electric field. All reconstruction algorithms are based on an optimization approach to find the stationary point of the Lagrangian. Adaptive reconstruction algorithms and space-mesh refinement indicators are also presented. Our computational tests show the qualitative reconstruction of the dielectric permittivity function using an anatomically realistic breast phantom.
Bump of sound velocity in dense 2-color QCD

We obtain the equation of state (EoS) and the sound velocity for 2-color QCD at low temperature and high density and find that in the superfluid phase, $c_s^2/c^2>1/3$, where $1/3$ is the value at the relativistic limit. Several independent Monte Carlo studies on 2-color QCD have been conducted intensively in recent years. These works have shown a clear evidence of phase transition between hadronic and superfluid phases. In our paper[1], we have investigated the EoS and sound velocity in both phases. Our result is consistent with chiral perturbation theory in a low $\mu$ regime of the superfluid phase including the Bose-Einstein condensed phase, and shows a peak of sound velocity in the high-density BCS phase. We also give detailed simulation results and a comment on the holography bound in this proceedings.
Productivity and water-use efficiency of spring-planted sugarcane (Saccharum sp. hybrid complex) under various planting methods and irrigation regimes

A field experiment was conducted during the spring seasons of 201012 at Sugarcane Research Institute, Bihar, Pusa (Samastipur), to investigate the effect of planting methods (conventional, paired row and furrow-irri- gated raised bed system) and irrigation regimes [Irrigation water : cumulative pan evaporation (IW: CPE) ratios of 0.50, 0.75 and 1.00] on productivity and water-use efficiency of sugarcane ( Saccharum sp. hybrid complex). Num- ber of millable canes increased by 12.4% in conventional method, followed by 7.2% in paired row over furrow-irri- gated raised bed system (FIRBS) respectively. Cane yield obtained under conventional method of planting was higher over paired row and FIRBS by 5.7 and 12.9% respectively. The water requirement, water-use efficiency and water productivity were higher under conventional method followed by paired row and FIRBS planting. Conven- tional method of planting fetched significantly higher net returns ( 142.4 103/ha) followed by paired row ( 136.4 103/ha) and FIRBS ( 112.5 103/ha). Significantly higher benefit : cost ratio was recorded in paired row planting (3.21) than FIRBS planting (2.68) whereas it remained at par with conventional method (3.13) of planting. Among the irrigation regimes, irrigation at an IW: CPE ratio 1.00 significantly enhanced all the growth and yield-attributing characters. Though the differences between IW: CPE ratios of 1.00 and 0.75 and 0.75 and 0.50 were not signifi- cant. However, crop irrigated at an IW: CPE ratio of 1.00 gave significantly higher number of millable canes (110 103/ha) and cane yield (98.0 t/ha) being at par with an IW: CPE ratio of 0.75 and both the treatments showed supe- riority over that of an IW: CPE ratio of 0.50. Sucrose content in juice was not influenced significantly due to differ- ent irrigation regimes. Crop irrigated with an IW: CPE ratio 1.00 showed the maximum values of water requirement, water-use efficiency and water productivity. Net return increased markedly with increase in irrigation regimes from an IW: CPE ratio of 0.50 to 1.00. Irrigation at 0.75 and 1.00 IW: CPE ratio increased the net returns by 29.0 and 39.5% over that with the 0.50 respectively. However, an IW: CPE ratio of 1.00 (3.24) being at par with that of 0.75 (3.11) gave significantly higher benefit: cost ratio than that of 0.50.
Arc travel time and path choice model estimation subsumed

We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of diﬀerent levels of granularity. Hitherto these two tasks have been tackled separately under strong assumptions. Using a small example, we illustrate that this can lead to biased results. Results on both real (New York yellow cab) and simulated data show strong performance of our method compared to existing baselines. models and loss functions. It is designed to estimate arc travel time and path choice model parameters simultaneously. We showed that by marginalizing the unobserved variables and using stochastic gradient estimates, we obtain a maximum likelihood estimation even for observations at diﬀerent level of granularity. We showed that we can mix diﬀerent data type when computing the MLE without needing to use a linear combination of losses as
Don’t Prompt, Search! Mining-based Zero-Shot Learning with Language Models

Masked language models like BERT can perform text classification in a zero-shot fashion by reformulating downstream tasks as text infilling. However, this approach is highly sensitive to the template used to prompt the model, yet practitioners are blind when designing them in strict zero-shot settings. In this paper, we propose an alternative mining-based approach for zero-shot learning. Instead of prompting language models, we use regular expressions to mine labeled examples from unlabeled corpora, which can optionally be filtered through prompting, and used to finetune a pretrained model. Our method is more flexible and interpretable than prompting, and outperforms it on a wide range of tasks when using comparable templates. Our results suggest that the success of prompting can partly be explained by the model being exposed to similar examples during pretraining, which can be directly retrieved through regular expressions.
Brief introduction to the nonlinear stability of Kerr

This a brief introduction to the sequence of works [61], [39], [59], [60] and [79] which establish the nonlinear stability of Kerr black holes with small angular momentum. We are delighted to dedicate this article to Demetrios Christodoulou for whom we both have great admiration. The ﬁrst author would also like to thank Demetrios for the magic moments of friendship, discussions and collaboration he enjoyed together with him.
HSVI can solve zero-sum Partially Observable Stochastic Games

State-of-the-art methods for solving 2-player zero-sum imperfect information games rely on linear programming or regret minimization, though not on dynamic programming (DP) or heuristic search (HS), while the latter are often at the core of state-of-the-art solvers for other sequential decision-making problems. In partially observable or collaborative settings (e.g., POMDPs and Dec- POMDPs), DP and HS require introducing an appropriate statistic that induces a fully observable problem as well as bounding (convex) approximators of the optimal value function. This approach has succeeded in some subclasses of 2-player zero-sum partially observable stochastic games (zs- POSGs) as well, but how to apply it in the general case still remains an open question. We answer it by (i) rigorously defining an equivalent game to work with, (ii) proving mathematical properties of the optimal value function that allow deriving bounds that come with solution strategies, (iii) proposing for the first time an HSVI-like solver that provably converges to an $\epsilon$-optimal solution in finite time, and (iv) empirically analyzing it. This opens the door to a novel family of promising approaches complementing those relying on linear programming or iterative methods.
Visual Answer Localization with Cross-Modal Mutual Knowledge Transfer

The goal of visual answering localization (VAL) in the video is to obtain a relevant and concise time clip from a video as the answer to the given natural language question. Early methods are based on the interaction modelling between video and text to predict the visual answer by the visual predictor. Later, using the textual predictor with subtitles for the VAL proves to be more precise. However, these existing methods still have cross-modal knowledge deviations from visual frames or textual subtitles. In this paper, we propose a cross-modal mutual knowledge transfer span localization (MutualSL) method to reduce the knowledge deviation. MutualSL has both visual predictor and textual predictor, where we expect the prediction results of these both to be consistent, so as to promote semantic knowledge understanding between cross-modalities. On this basis, we design a one-way dynamic loss function to dynamically adjust the proportion of knowledge transfer. We have conducted extensive experiments on three public datasets for evaluation. The experimental results show that our method outperforms other competitive state-of-the-art (SOTA) methods, demonstrating its effectiveness1.