Miracle-dz/UniFAR-FTUs huggingface dataset:
SARS-CoV-2 pseudovirus enters the host cells through spike protein-CD147 in an Arf6-dependent manner

ABSTRACT The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants is threatening public health around the world. Endocytosis functions as an important way for viral infection, and SARS-CoV-2 bears no exception. However, the specific endocytic mechanism of SARS-CoV-2 remains unknown. In this study, we used endocytic inhibitors to evaluate the role of different endocytic routes in SARS-CoV-2 pseudovirus infection and found that the viral infection was associated with caveolar/lipid raft- and cytoskeleton-mediated endocytosis, but independent of the clathrin-mediated endocytosis and macropinocytosis. Meanwhile, the knockdown of CD147 and Rab5a in Vero E6 and Huh-7 cells inhibited SARS-CoV-2 pseudovirus infection, and the co-localization of spike protein, CD147, and Rab5a was observed in pseudovirus-infected Vero E6 cells, which was weakened by CD147 silencing, illustrating that SARS-CoV-2 pseudovirus entered the host cells via CD147-mediated endocytosis. Additionally, Arf6 silencing markedly inhibited pseudovirus infection in Vero E6 and Huh-7 cells, while little change was observed in CD147 knockout-Vero E6 cells. This finding indicated Arf6-mediated CD147 trafficking plays a vital role in SARS-CoV-2 entry. Taken together, our findings provide new insights into the CD147-Arf6 axis in mediating SARS-CoV-2 pseudovirus entry into the host cells, and further suggest that blockade of this pathway seems to be a feasible approach to prevent the SARS-CoV-2 infection clinically.
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects

We consider the task of identifying and estimating the causal eﬀect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational domain is subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period of time; hence, long-term eﬀects of treatment are unobserved but short-term eﬀects will be observed. Therefore, data from neither domain alone suﬃces for causal inference about the eﬀect of the treatment on the long-term outcome, and must be pooled in a principled way, instead. Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal eﬀect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we ﬁrst review their proposed approach and discuss the latent unconfoundedness assumption. Then we propose three alternative approaches for data fusion for the purpose of identifying and estimating average treatment eﬀect as well as the eﬀect of treatment on the treated. Our ﬁrst proposed approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second proposed approach is based on a relaxed version of the equi-confounding bias assumption, where we assume the existence of a binary confounder such that the confounding bias in the sub-populations corresponding to its realizations are the same. Our third proposed approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation. For the proximal data fusion framework, we also provide results on the estimation aspect of the task of causal inference.
Temporal Transductive Inference for Few-Shot Video Object Segmentation

Few-shot video object segmentation (FS-VOS) aims at segmenting video frames using a few labelled examples of classes not seen during initial training. In this paper, we present a simple but effective temporal transductive inference (TTI) approach that leverages temporal consistency in the unlabelled video frames during few-shot inference. Key to our approach is the use of both global and local temporal constraints. The objective of the global constraint is to learn consistent linear classifiers for novel classes across the image sequence, whereas the local constraint enforces the proportion of foreground/background regions in each frame to be coherent across a local temporal window. These constraints act as spatiotemporal regularizers during the transductive inference to increase temporal coherence and reduce overfitting on the few-shot support set. Empirically, our model outperforms state-of-the-art meta-learning approaches in terms of mean intersection over union on YouTube-VIS by 2.8%. In addition, we introduce improved benchmarks that are exhaustively labelled (i.e. all object occurrences are labelled, unlike the currently available), and present a more realistic evaluation paradigm that targets data distribution shift between training and testing sets. Our empirical results and in-depth analysis confirm the added benefits of the proposed spatiotemporal regularizers to improve temporal coherence and overcome certain overfitting scenarios.
A Formal Analysis of the Count-Min Sketch with Conservative Updates

Count-Min Sketch with Conservative Updates (CMS-CU) is a popular algorithm to approximately count items’ appearances in a data stream. Despite CMS-CU’s widespread adoption, the theoretical analysis of its performance is still wanting because of its inherent difficulty. In this paper, we propose a novel approach to study CMS-CU and derive new upper bounds on the expected value and the CCDF of the estimation error under an i.i. d. request process. Our formulas can be successfully employed to derive improved estimates for the precision of heavy-hitter detection methods and improved configuration rules for CMS-CU. The bounds are evaluated both on synthetic and real traces.
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman's repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data.
Estimation of Full Sky CMB Angular Power Spectrum from High Resolution Partial Sky CMB Map Using Artiﬁcial Neural Network

Reliable extraction of cosmological information from observed cosmic microwave background (CMB) maps requires accurate removal of foreground components. Since some regions of the sky are inevitably contaminated by strong foreground signals, it may be desired to excise such strongly contaminated regions even after a foreground subtraction has been performed. In this article, we employ an artiﬁcial neural network (ANN) to predict the full sky CMB angular power spectrum from the partial sky spectrum obtained from masked CMB temperature anisotropy map, at a high pixel resolution. We use a simple ANN architecture with one hidden layer containing 895 neurons. Using 1 . 2 × 10 5 training samples of full sky and corresponding partial sky CMB angular power spectra at Healpix pixel resolution parameter N side = 256 , we show that predicted spectrum by our ANN agrees well with the target spectrum at each realization for the multipole range 2 ≤ l ≤ 512 . The predicted spectra are statistically unbiased and they preserve the cosmic variance accurately. Statistically, the differences between the mean predicted and underlying theoretical spectra are within 3 σ . More-over, the probability densities obtained from predicted angular power spectra agree very well with those obtained from ’actual’ full sky CMB angular power spectra for each multipole. Equally interesting is that the correlation matrix calculated from predicted angular power spectra does not show any signature of correlation between the angular power spectrum of different multipoles. Our work shows that the predicted spectra do not need to
Influence-aware Task Assignment in Spatial Crowdsourcing (Technical Report)

With the widespread diffusion of smartphones, Spatial Crowdsourcing (SC), which aims to assign spatial tasks to mobile workers, has drawn increasing attention in both academia and industry. One of the major issues is how to best assign tasks to workers. Given a worker and a task, the worker will choose to accept the task based on her affinity towards the task, and the worker can propagate the information of the task to attract more workers to perform it. These factors can be measured as worker-task influence. Since workers' affinities towards tasks are different and task issuers may ask workers who performed tasks to propagate the information of tasks to attract more workers to perform them, it is important to analyze worker-task influence when making assignments. We propose and solve a novel influence-aware task assignment problem in SC, where tasks are assigned to workers in a manner that achieves high worker-task influence. In particular, we aim to maximize the number of assigned tasks and worker-task influence. To solve the problem, we first determine workers' affinities towards tasks by identifying workers' historical task-performing patterns. Next, a Historical Acceptance approach is developed to measure workers' willingness of performing a task, i.e., the probability of workers visiting the location of the task when they are informed. Next, we propose a Random reverse reachable-based Propagation Optimization algorithm that exploits reverse reachable sets to calculate the probability of workers being informed about tasks in a social network. Based on worker-task influence derived from the above three factors, we propose three influence-aware task assignment algorithms that aim to maximize the number of assigned tasks and worker-task influence. Extensive experiments on two real-world datasets offer detailed insight into the effectiveness of our solutions.
Nanocryotron-driven Charge Configuration Memory

Cryo-computing is presently severely limited by the absence of a suitable fast and energy efficient cryo-memory 1,2 . Ideally, such memory should be compatible with single-flux quantum (SFQ) logic in terms of speed, switching energy and matching impedance. Here we present an implementation of non-volatile charge configuration memory (CCM) 3,4 in a cryo-computing environment by combining it in parallel with a pulse-triggered superconducting nanowire cryotron (nTron) 5 . The combined device is modeled in terms of the dynamical response of the SC order parameter in a current-controlled nanowire with a CCM shunt 6 . Analysis of time-dynamics and current-voltage characteristics based on measured device parameters show that single flux quantum (SFQ)-level pulses can drive non-volatile CCM on the picosecond timescale, while allowing the nTron to operate in non-latching mode. The inherent high energy efficiency and ultrahigh speed makes this hybrid device an ideal memory for use in cryo-computing and quantum computing peripheral devices.
piRank: A Probabilistic Intent Based Ranking Framework for Facebook Search

While numerous studies have been conducted in the literature exploring different types of machine learning approaches for search ranking, most of them are focused on specific pre-defined problems but only a few of them have studied the ranking framework which can be applied in a commercial search engine in a scalable way. In the meantime, existing ranking models are often optimized for normalized discounted cumulative gains (NDCG) or online click-through rate (CTR), and both types of machine learning models are built based on the assumption that high-quality training data can be easily obtained and well applied to unseen cases. In practice at Facebook search, we observed that our training data for ML models have certain issues. First, tail query intents are hardly covered in our human rating dataset. Second, search click logs are often noisy and hard to clean up due to various reasons. To address the above issues, in this paper, we propose a probabilistic intent based ranking framework (short for piRank), which can: 1) provide a scalable framework to address various ranking issues for different query intents in a divide-and-conquer way; 2) improve system development agility including iteration speed and system debuggability; 3) combine both machine learning and empirical-based algorithmic methods in a systematic way. We conducted extensive experiments and studies on top of Facebook search engine system and validated the effectiveness of this new ranking architecture.
Enhanced Versatility of Table‐Top X‐Rays from Van der Waals Structures

Van der Waals (vdW) materials have attracted much interest for their myriad unique electronic, mechanical, and thermal properties. In particular, they are promising candidates for monochromatic, table‐top X‐ray sources. This work reveals that the versatility of the table‐top vdW X‐ray source goes beyond what has been demonstrated so far. By introducing a tilt angle between the vdW structure and the incident electron beam, it is theoretically and experimentally shown that the accessible photon energy range is more than doubled. This allows for greater versatility in real‐time tuning of the vdW X‐ray source. Furthermore, this work shows that the accessible photon energy range is maximized by simultaneously controlling both the electron energy and the vdW structure tilt. These results will pave the way for highly tunable, compact X‐ray sources, with potential applications including hyperspectral X‐ray fluoroscopy and X‐ray quantum optics.
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension

Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models’ fine-grained learning skills. Second, the dataset supports question generation (QG) task in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Nematic Colloidal Micro‐Robots as Physically Intelligent Systems

Physically intelligent micro‐robotic systems exploit information embedded in micro‐robots, their colloidal cargo, and their milieu to interact, assemble, and form functional structures. Nonlinear anisotropic fluids such as nematic liquid crystals (NLCs) provide untapped opportunities to embed interactions via their topological defects, complex elastic responses, and ability to dramatically restructure in dynamic settings. Here a four‐armed ferromagnetic micro‐robot is designed and fabricated to embed and dynamically reconfigure information in the nematic director field, generating a suite of physical interactions for cargo manipulation. The micro‐robot shape and surface chemistry are designed to generate a nemato‐elastic energy landscape in the domain that defines multiple modes of emergent, bottom‐up interactions with passive colloids. Micro‐robot rotation expands the ability to sculpt interactions; the energy landscape around a rotating micro‐robot is dynamically reconfigured by complex far‐from‐equilibrium dynamics of the micro‐robot's companion topological defect. These defect dynamics allow transient information to be programmed into the domain and exploited. Robust micro‐robotic manipulation strategies are demonstrated that exploit these diverse modes of nemato‐elastic interaction to achieve cargo docking, transport, release, and assembly of complex reconfigurable structures at multi‐stable sites. Such structures are of great interest to future developments of LC‐based advanced optical device and micro‐manufacturing in anisotropic environments.
Remix-Cycle-Consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation

A new learning algorithm for speech separation networks is designed to explicitly reduce residual noise and artifacts in the separated signal in an unsupervised manner. Generative adversarial networks are known to be effective in constructing separation networks when the ground truth for the observed signal is inaccessible. Still, weak objectives aimed at distribution-to-distribution mapping make the learning unstable and limit their performance. This study introduces the remix-cycle-consistency loss as a more appropriate objective function and uses it to fine-tune adversarially learned source separation models. The remix-cycle-consistency loss is de-fined as the difference between the mixed speech observed at microphones and the pseudo-mixed speech obtained by alternating the process of separating the mixed sound and remixing its outputs with another combination. The minimization of this loss leads to an explicit reduction in the distortions in the output of the separation network. Experimental comparisons with multichannel speech separation demonstrated that the proposed method achieved high separation accuracy and learning stability comparable to supervised learning.
Multi-model Ensemble Learning Method for Human Expression Recognition

Analysis of human affect plays a vital role in human-computer interaction (HCI) systems. Due to the difficulty in capturing large amounts of real-life data, most of the current methods have mainly focused on controlled environments, which limit their application scenarios. To tackle this problem, we propose our solution based on the ensemble learning method. Specifically, we formulate the problem as a classification task, and then train several expression classification models with different types of backbones--ResNet, EfficientNet and InceptionNet. After that, the outputs of several models are fused via model ensemble method to predict the final results. Moreover, we introduce the multi-fold ensemble method to train and ensemble several models with the same architecture but different data distributions to enhance the performance of our solution. We conduct many experiments on the AffWild2 dataset of the ABAW2022 Challenge, and the results demonstrate the effectiveness of our solution.
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise but lack complex background image modeling capability; representation-based methods are good at defective region localization but lack accuracy in defective region shape contour extraction; reconstruction-based methods detected defective region match well with the ground truth defective region shape contour but are noisy. To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation. In the training stage, we learn the common background as a deep image prior by a patch autoencoder (PAE) network. In the inference stage, we formulate anomaly detection as an image decomposition problem with the deep image prior and domain-specific regularizations. By adopting the proposed approach, the defective regions in the image can be accurately extracted in an unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in simulation studies and an industrial dataset in the case study.
MWA tied-array processing IV: A multi-pixel beamformer for pulsar surveys and ionospheric corrected localisation

Abstract The Murchison Widefield Array (MWA) is a low-frequency aperture array capable of high-time and frequency resolution astronomy applications such as pulsar studies. The large field-of-view of the MWA (hundreds of square degrees) can also be exploited to attain fast survey speeds for all-sky pulsar search applications, but to maximise sensitivity requires forming thousands of tied-array beams from each voltage-capture observation. The necessity of using calibration solutions that are separated from the target observation both temporally and spatially makes pulsar observations vulnerable to uncorrected, frequency-dependent positional offsets due to the ionosphere. These offsets may be large enough to move the source away from the centre of the tied-array beam, incurring sensitivity drops of ${\sim}30{-}50\%$ in Phase II extended array configuration. We analyse these offsets in pulsar observations and develop a method for mitigating them, improving both the source position accuracy and the sensitivity. This analysis prompted the development of a multi-pixel beamforming functionality that can generate dozens of tied-array beams simultaneously, which runs a factor of ten times faster compared to the original single-pixel version. This enhancement makes it feasible to observe multiple pulsars within the vast field of view of the MWA and supports the ongoing large-scale pulsar survey efforts with the MWA. We explore the extent to which ionospheric offset correction will be necessary for the MWA Phase III and the low-frequency square kilometre array (SKA-low).