Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell death induced by NLRP3–palmitate axis impairs pulmonary damage tolerance and aggravates immunopathology during obesity‐tuberculosis comorbidity

A low‐grade and persistent inflammation, which is the hallmark of obesity, requires the participation of NLRP3 and cell death. During Mycobacterium tuberculosis infection, NLRP3 signaling is important for bacterial killing by macrophages in vitro but was shown to be dispensable for host protection in vivo. We hypothesized that during obesity‐tuberculosis (TB) comorbidity, NLRP3 signaling might play a detrimental role by inducing excessive inflammation. We employed a model of high‐fat‐diet‐induced obesity, followed by M. tuberculosis infection in C57BL/6 mice. Obese mice presented increased susceptibility to infection and pulmonary immunopathology compared to lean mice. Using treatment with NLRP3 antagonist and Nlrp3−/− mice, we showed that NLRP3 signaling promoted cell death, with no effect in bacterial loads. The levels of palmitate were higher in the lungs of obese infected mice compared to lean counterparts, and we observed that this lipid increased M. tuberculosis‐induced macrophage death in vitro, which was dependent on NLRP3 and caspase‐1. At the chronic phase, although lungs of obese Nlrp3−/− mice showed an indication of granuloma formation compared to obese wild‐type mice, there was no difference in the bacterial load. Our findings indicate that NLRP3 may be a potential target for host‐directed therapy to reduce initial and severe inflammation‐mediated disease and to treat comorbidity‐associated TB. © 2022 The Pathological Society of Great Britain and Ireland.
Impact of Labelled Set Selection and Supervision Policies on Semi-supervised Learning

In semi-supervised representation learning frameworks, when the number of labelled data is very scarce, the quality and representativeness of these samples become increasingly important. Existing literature on semi-supervised learning randomly sample a limited number of data points for labelling. All these labelled samples are then used along with the unlabelled data throughout the training process. In this work, we ask two important questions in this context: (1) does it matter which samples are selected for labelling? (2) does it matter how the labelled samples are used throughout the training process along with the unlabelled data? To answer the first question, we explore a number of unsupervised methods for selecting specific subsets of data to label (without prior knowledge of their labels), with the goal of maximizing representativeness w.r. t. the unlabelled set. Then, for our second line of inquiry, we define a variety of different label injection strategies in the training process. Extensive experiments on four popular datasets, CIFAR-10, CIFAR-100, SVHN, and STL-10, show that unsupervised selection of samples that are more representative of the entire data improves performance by up to ~2% over the existing semi-supervised frameworks such as MixMatch, ReMixMatch, FixMatch and others with random sample labelling. We show that this boost could even increase to 7.5% for very few-labelled scenarios. However, our study shows that gradually injecting the labels throughout the training procedure does not impact the performance considerably versus when all the existing labels are used throughout the entire training.
How to Backpropagate through Hungarian in Your DETR?

The DEtection TRansformer (DETR) approach, which uses a transformer encoder-decoder architecture and a set-based global loss, has become a building block in many transformer based applications. However, as originally presented, the assignment cost and the global loss are not aligned, i.e., reducing the former is likely but not guaranteed to reduce the latter. And the issue of gradient is ignored when a combinatorial solver such as Hungarian is used. In this paper we show that the global loss can be expressed as the sum of an assignment-independent term, and an assignment-dependent term which can be used to define the assignment cost matrix. Recent results on generalized gradients of optimal assignment cost with respect to parameters of an assignment problem are then used to define generalized gradients of the loss with respect to network parameters, and backpropagation is carried out properly. Our experiments using the same loss weights show interesting convergence properties and a potential for further performance improvements.
Experimental Investigation of Local Heat Transfer in a Square Duct With Continuous and Truncated Ribs

In the present study, the turbulent heat transfer and fiction in a square duct roughened by continuous and truncated ribs on one wall has been investigated experimentally. The ribs are oriented transversely to the main stream in a periodic arrangement. For both cases, the rib height-to-hydraulic diameter ratio is 0.15, the rib pitch-to-height ratio is fixed at 12, and the Reynolds number varies from 8,000–20,000. Liquid crystal thermography is applied to demonstrate detailed distribution of heat transfer coefficient between a pair of ribs. The results show that the horseshoe vortices produced by truncated ribs are quite different from the flow structures altered by continuous ribs. It is noted that continuous ribs give higher heat transfer augmentation and pressure drop than truncated ribs. Moreover, the truncated ribs cannot be employed to eliminate hot spots which occur in the corresponding continuous types.
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality

Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. In this work, we analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias (one target sequence can only be mapped to one source sequence), and the tendency to memorize whole examples rather than separating structures from contents. We propose two techniques to address these two issues respectively: Mutual Exclusivity Training that prevents the model from producing seen generations when facing novel examples via an unlikelihood-based loss, and prim2primX data augmentation that automatically diversifies the arguments of every syntactic function to prevent memorizing and provide a compositional inductive bias without exposing test-set data. Combining these two techniques, we show substantial empirical improvements using standard sequence-to-sequence models (LSTMs and Transformers) on two widely-used compositionality datasets: SCAN and COGS. Finally, we provide analysis characterizing the improvements as well as the remaining challenges, and provide detailed ablations of our method.
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation

Recently, the contrastive language-image pre-training, e.g., CLIP, has demonstrated promising results on various downstream tasks. The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data. However, transferring the learned visual knowledge to open-vocabulary semantic segmentation is still under-explored. In this paper, we propose a CLIP-based model named SegCLIP for the topic of open-vocabulary segmentation in an annotation-free manner. The SegCLIP achieves segmentation based on ViT and the main idea is to gather patches with learnable centers to semantic regions through training on text-image pairs. The gathering operation can dynamically capture the semantic groups, which can be used to generate the final segmentation results. We further propose a reconstruction loss on masked patches and a superpixel-based KL loss with pseudo-labels to enhance the visual representation. Experimental results show that our model achieves comparable or superior segmentation accuracy on the PASCAL VOC 2012 (+0.3% mIoU), PASCAL Context (+2.3% mIoU), and COCO (+2.2% mIoU) compared with baselines. We release the code at https://github.com/ArrowLuo/SegCLIP.
Trapping of resonant metallic nanoparticles with engineered vectorial optical field

Abstract Optical trapping and manipulation using focused laser beams has emerged as a powerful tool in the biological and physical sciences. However, scaling this technique to metallic nanoparticles remains challenging due to the strong scattering force and optical heating effect. In this work, we propose a novel strategy to optically trap metallic nanoparticles even under the resonant condition using engineered optical field. The distribution of the optical forces can be tailored through optimizing the spatial distribution of a vectorial optical illumination to favour the stable trapping of a variety of metallic nanoparticles under various conditions. It is shown that this optical tweezers has the ability of generating negative scattering force and supporting stable three-dimensional trapping for gold nanoparticles at resonance while avoiding trap destabilization due to optical overheating. The technique presented in this work offers a versatile solution for trapping metallic nanoparticles and may open up new avenues for optical manipulation.
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations

Recently the Transformer structure has shown good performances in graph learning tasks. However, these Transformer models directly work on graph nodes and may have difficulties learning high-level information. Inspired by the vision transformer, which applies to image patches, we propose a new Transformer-based graph neural network: Patch Graph Transformer (PatchGT). Unlike previous transformer-based models for learning graph representations, PatchGT learns from non-trainable graph patches, not from nodes directly. It can help save computation and improve the model performance. The key idea is to segment a graph into patches based on spectral clustering without any trainable parameters, with which the model can first use GNN layers to learn patch-level representations and then use Transformer to obtain graph-level representations. The architecture leverages the spectral information of graphs and combines the strengths of GNNs and Transformers. Further, we show the limitations of previous hierarchical trainable clusters theoretically and empirically. We also prove the proposed non-trainable spectral clustering method is permutation invariant and can help address the information bottlenecks in the graph. PatchGT achieves higher expressiveness than 1-WL-type GNNs, and the empirical study shows that PatchGT achieves competitive performances on benchmark datasets and provides interpretability to its predictions. The implementation of our algorithm is released at our Github repo: https://github.com/tufts-ml/PatchGT.
Scaphy: Detecting Modern ICS Attacks by Correlating Behaviors in SCADA and PHYsical

Modern Industrial Control Systems (ICS) attacks evade existing tools by using knowledge of ICS processes to blend their activities with benign Supervisory Control and Data Acquisition (SCADA) operation, causing physical world damages. We present Scaphy to detect ICS attacks in SCADA by leveraging the unique execution phases of SCADA to identify the limited set of legitimate behaviors to control the physical world in different phases, which differentiates from attacker’s activities. For example, it is typical for SCADA to setup ICS device objects during initialization, but anomalous during process-control. To extract unique behaviors of SCADA execution phases, Scaphy first leverages open ICS conventions to generate a novel physical process dependency and impact graph (PDIG) to identify disruptive physical states. Scaphy then uses PDIG to inform a physical process-aware dynamic analysis, whereby code paths of SCADA process-control execution is induced to reveal API call behaviors unique to legitimate process-control phases. Using this established behavior, Scaphy selectively monitors attacker’s physical world-targeted activities that violates legitimate process-control behaviors. We evaluated Scaphy at a U.S. national lab ICS testbed environment. Using diverse ICS deployment scenarios and attacks across 4 ICS industries, Scaphy achieved 95% accuracy & 3.5% false positives (FP), compared to 47.5% accuracy and 25% FP of existing work. We analyze Scaphy’s resilience to futuristic attacks where attacker knows our approach.
Learning to Learn: How to Continuously Teach Humans and Machines

Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, which also reinforces our addition and multiplication skills. Designing a curriculum for teaching either a human or a machine shares the underlying goal of maximizing knowledge transfer from earlier to later tasks, while also minimizing forgetting of learned tasks. Prior research on curriculum design for image classification focuses on the ordering of training examples during a single offline task. Here, we investigate the effect of the order in which multiple distinct tasks are learned in a sequence. We focus on the online class-incremental continual learning setting, where algorithms or humans must learn image classes one at a time during a single pass through a dataset. We find that curriculum consistently influences learning outcomes for humans and for multiple continual machine learning algorithms across several benchmark datasets. We introduce a novel-object recognition dataset for human curriculum learning experiments and observe that curricula that are effective for humans are highly correlated with those that are effective for machines. As an initial step towards automated curriculum design for online class-incremental learning, we propose a novel algorithm, dubbed Curriculum Designer (CD), that designs and ranks curricula based on inter-class feature similarities. We find significant overlap between curricula that are empirically highly effective and those that are highly ranked by our CD. Our study establishes a framework for further research on teaching humans and machines to learn continuously using optimized curricula. Our code and data are available through this link.
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation

Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previous attempts most conduct global-only/random augmentation. Their augmented samples are usually insufficient in diversity and informativeness, thus failing to cover the possible target domain distribution. In this paper, we rethink the data augmentation strategy for SDG in medical image segmentation. Motivated by the class-level representation invariance and style mutability of medical images, we hypothesize that unseen target data can be sampled from a linear combination of C (the class number) random variables, where each variable follows a location-scale distribution at the class level. Accordingly, data augmented can be readily made by sampling the random variables through a general form. On the empirical front, we implement such strategy with constrained Bezier transformation on both global and local (i.e. class-level) regions, which can largely increase the augmentation diversity. A Saliency-balancing Fusion mechanism is further proposed to enrich the informativeness by engaging the gradient information, guiding augmentation with proper orientation and magnitude. As an important contribution, we prove theoretically that our proposed augmentation can lead to an upper bound of the generalization risk on the unseen target domain, thus confirming our hypothesis. Combining the two strategies, our Saliency-balancing Location-scale Augmentation (SLAug) exceeds the state-of-the-art works by a large margin in two challenging SDG tasks. Code is available at https://github.com/Kaiseem/SLAug.
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning

Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often render an inaccurate estimate of the reflectance layer. Therefore, we propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. In the first stage, an initial reflectance layer free from shadows and specularities is obtained with the constraint of novel losses that are guided by prior-based shadow-free and specular-free images. To further enforce the reflectance layer to be independent of shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image. Our network employs a classifier to categorize shadow/shadow-free, specular/specular-free classes, enabling the activation features to function as attention maps that focus on shadow/specular regions. Our quantitative and qualitative evaluations show that our method outperforms the state-of-the-art methods in the reflectance layer estimation that is free from shadows and specularities.
A Light Touch Approach to Teaching Transformers Multi-view Geometry

Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors. This flexibility can be problematic in tasks that involve multiple-view geometry, due to the near-infinite possible variations in 3D shapes and viewpoints (requiring flexibility), and the precise nature of projective geometry (obeying rigid laws). To resolve this conundrum, we propose a “light touch” approach, guiding visual Transformers to learn multiple-view geometry but allowing them to break free when needed. We achieve this by using epipolar lines to guide the Transformer's cross-attention maps during training, penalizing attention values outside the epipolar lines and encouraging higher attention along these lines since they contain geometrically plausible matches. Unlike previous methods, our proposal does not require any camera pose information at test-time. We focus on pose-invariant object instance retrieval, where standard Transformer networks struggle, due to the large differences in viewpoint between query and retrieved images. Experimentally, our method outperforms state-of-the-art approaches at object retrieval, without needing pose information at test-time.
Effects of elastoviscoplastic properties of mucus on airway closure in healthy and pathological conditions

Airway mucus is a complex material with both viscoelastic and viscoplastic properties that vary with healthy and pathological lung conditions. In this study, the effects of these conditions on airway closure are examined in a model problem, where an elastoviscoplastic (EVP) single liquid layer lines the inner wall of a rigid pipe and surrounds the air core. The EVP liquid layer is modelled using the Saramito-HB model. Model parameters are obtained for the mucus in healthy, asthma, chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) conditions by fitting the rheological model to experimental data. Then, liquid plug formation is studied by varying the Laplace number and undisturbed liquid film thickness. According to previous studies, airway epithelial cells can be damaged by high peak of the wall stresses and stress gradients during liquid plug formation. Here, we show that these stresses are also related to the EVP features of the liquid layer. Yielded zones of the liquid layer are examined for different mucus conditions. Before closure, liquid layer is found to be unyielded, indicating that this phase is dominated by the elastic behaviour and solvent viscosity. This is further confirmed by showing that the elastic coefficient is important in determining whether closure occurs or not. Wall stresses are also investigated for pathological and healthy cases. Their peaks are higher in COPD and CF due to the viscoelastic extra stress contribution. Unlike the Newtonian case, COPD and CF wall stresses do not relax after closure but remain approximately as high as the Newtonian peak. Moreover, the local normal wall stress gradients are smaller in COPD and CF liquid layers since their higher stiffness causes smaller curvature at the capillary wave. The local tangential wall stress gradients are also lower in these cases due to slower accumulation of the liquid at the bulge.
Object transport by a confined active suspension

Abstract Numerical simulations in two space dimensions are used to examine the dynamics, transport and equilibrium behaviours of a neutrally buoyant circular object immersed in an active suspension within a larger circular container. The continuum model of Gao et al. (Phys. Rev. Fluids, vol. 2, issue 9, 2017, 093302) represents the suspension of non-interacting, immotile, extensor-type microscopic agents that have a direction and strength, and align in response to strain rate. Such a suspension is well known to be unstable above an activity strength threshold, which depends upon the length scale of the confinement. Introducing the object leads to additional phenomenology. It can confine fluid between it and the container wall, which suppresses local suspension activity. However, its motion also correlates strain rates near its surface, which induce a correspondingly correlated active-stress response. Depending on the suspension activity strength, these mechanisms lead to either an attraction toward or a repulsion away from the container wall. In addition, a persistent propagating behaviour is found for modest activity strength, which provides a mechanism for long-range transport. When activity is so weak that the mobility of the object is essential to support suspension instability and sustain flow, all flow terminates when its mobility is diminished as it nears the container wall. If activity strength is scheduled in time, then these mechanisms could be used to perform relatively complex tasks with simple active agents.
BJTU-WeChat’s Systems for the WMT22 Chat Translation Task

This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT’22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we apply the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 81.0 and 94.6 COMET scores on English-German and German-English, respectively. The COMET scores of English-German and German-English are the highest among all submissions.