Miracle-dz/UniFAR-FTUs huggingface dataset:
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing

Deep learning based methods hold state-of-the-art results in low-level image processing tasks, but remain difficult to interpret due to their black-box construction. Unrolled optimization networks present an interpretable alternative to constructing deep neural networks by deriving their architecture from classical iterative optimization methods without use of tricks from the standard deep learning tool-box. So far, such methods have demonstrated performance close to that of state-of-the-art models while using their interpretable construction to achieve a comparably low learned parameter count. In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising and joint denoising and demosaicing (JDD) performance both in low and high parameter count regimes. Specifically, we show that the proposed model outperforms state-of-the-art fully convolutional denoising and JDD models when scaled to a similar parameter count. In addition, we leverage the model’s interpretable construction to propose a noise-adaptive parameterization of thresholds in the network that enables state-of-the-art blind denoising performance, and near-perfect generalization on noise-levels unseen during training. Furthermore, we show that such performance extends to the JDD task and unsupervised learning.
Computing Class Hierarchies from Classifiers

A class or taxonomic hierarchy is often manually constructed, and part of our knowledge about the world. In this paper, we propose a novel algorithm for automatically acquiring a class hierarchy from a classifier which is often a large neural network these days. The information that we need from a classifier is its confusion matrix which contains, for each pair of base classes, the number of errors the classifier makes by mistaking one for another. Our algorithm produces surprisingly good hierarchies for some well-known deep neural network models trained on the CIFAR-10 dataset, a neural network model for predicting the native language of a non-native English speaker, a neural network model for detecting the language of a written text, and a classifier for identifying music genre. In the literature, such class hierarchies have been used to provide interpretability to the neural networks. We also discuss some other potential uses of the acquired hierarchies.
The Price of Differential Privacy under Continual Observation

We study the accuracy of differentially private mechanisms in the continual release model. A continual release mechanism receives a sensitive dataset as a stream of $T$ inputs and produces, after receiving each input, an accurate output on the obtained inputs. In contrast, a batch algorithm receives the data as one batch and produces a single output. We provide the first strong lower bounds on the error of continual release mechanisms. In particular, for two fundamental problems that are widely studied and used in the batch model, we show that the worst case error of every continual release algorithm is $\tilde \Omega(T^{1/3})$ times larger than that of the best batch algorithm. Previous work shows only a polylogarithimic (in $T$) gap between the worst case error achievable in these two models; further, for many problems, including the summation of binary attributes, the polylogarithmic gap is tight (Dwork et al., 2010; Chan et al., 2010). Our results show that problems closely related to summation -- specifically, those that require selecting the largest of a set of sums -- are fundamentally harder in the continual release model than in the batch model. Our lower bounds assume only that privacy holds for streams fixed in advance (the"nonadaptive"setting). However, we provide matching upper bounds that hold in a model where privacy is required even for adaptively selected streams. This model may be of independent interest.
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.Experiments show that our model outperforms other KEPLMs significantly over zero-shot knowledge probing tasks and multiple knowledge-aware language understanding tasks. To guarantee effective knowledge injection, previous studies integrate models with knowledge encoders for representing knowledge retrieved from knowledge graphs. The operations for knowledge retrieval and encoding bring significant computational burdens, restricting the usage of such models in real-world applications that require high inference speed. In this paper, we propose a novel KEPLM named DKPLM that decomposes knowledge injection process of the pre-trained language models in pre-training, fine-tuning and inference stages, which facilitates the applications of KEPLMs in real-world scenarios. Specifically, we first detect knowledge-aware long-tail entities as the target for knowledge injection, enhancing the KEPLMs' semantic understanding abilities and avoiding injecting redundant information. The embeddings of long-tail entities are replaced by ``pseudo token representations'' formed by relevant knowledge triples. We further design the relational knowledge decoding task for pre-training to force the models to truly understand the injected knowledge by relation triple reconstruction. Experiments show that our model outperforms other KEPLMs significantly over zero-shot knowledge probing tasks and multiple knowledge-aware language understanding tasks. We further show that DKPLM has a higher inference speed than other competing models due to the decomposing mechanism.
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent

Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Synchronous Stochastic Gradient Descent (SSGD) 1 is the de facto DDL optimization method. Using a sufficiently large batch size is critical to achieving DDL runtime speedup. In a large batch setting, the learning rate must be increased to compensate for the reduced number of parameter updates. However, a large learning rate may harm convergence in SSGD and training could easily diverge. Recently, Decentralized Parallel SGD (DPSGD) has been proposed to improve distributed training speed. In this paper, we find that DPSGD not only has a system-wise run-time benefit but also a significant convergence benefit over SSGD in the large batch setting. Based on a detailed analysis of the DPSGD learning dynamics, we find that DPSGD introduces additional landscape-dependent noise that automatically adjusts the effective learning rate to improve convergence. In addition, we theoretically show that this noise smoothes the loss landscape, hence allowing a larger learning rate. We conduct extensive studies over 18 state-of-the-art DL models/tasks and demonstrate that DPSGD often converges in cases where SSGD diverges for large learning rates in the large batch setting. Our findings are consistent across two different application domains: Computer Vision (CIFAR10 and ImageNet-1K) and Automatic Speech Recognition (SWB300 and SWB2000), and two different types of neural network models: Convolutional Neural Networks and Long Short-Term Memory Recurrent Neural Networks.
3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction

Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift is a competition held by NeurIPS 2021. The objective of this competition is to search for methods to solve the motion prediction problem in cross-domain. In the real world dataset, It exists variance between input data distribution and ground-true data distribution, which is called the domain shift problem. In this report, we propose a new architecture inspired by state of the art papers. The main contribution is the backbone architecture with self-attention mechanism and predominant loss function. Subsequently, we won 3rd place as shown on the leaderboard.
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved version of MViT that incorporates decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 APbox on COCO object detection as well as 86.1% on Kinetics-400 video classification. Code and models are available at https://github.com/facebookresearch/mvit.
Combined Heat, Air and Moisture (HAM) Transfer Model for Porous Building Materials

In the building science area, mathematical models are developed to provide better indoor thermal comfort with lower energy consumption. Although the fact moisture and air transfer can strongly affect the temperature distribution within constructions, whole-building simulation codes do not take into account the convective air transport in porous materials. In this way, this article presents a heat, air, and moisture (HAM) transfer model based on driving potentials of temperature, air pressure, and water vapor pressure gradients for consolidated porous material in both pendular and funicular states. The solution of the set of governing equations has been simultaneously obtained using the MTDMA (MultiTriDiagonal-Matrix Algorithm) for the three potentials. To conclude, results are presented showing the impact of convective terms on the HAM transfer through a two-layer porous building envelope.
Gravitational background from dynamical binaries and detectability with 2G detectors

We study the impact of young clusters on the gravitational wave background from compact binary coalescence. We simulate a catalog of sources from population I/II isolated binary stars and stars born in young clusters, corresponding to one year of observations with second-generation (2G) detectors. Taking into account uncertainties on the fraction of dynamical binaries and star formation parameters, we ﬁnd that the background is dominated by the population of binary black holes, and we obtain a value of Ω gw (25Hz) = 1 . 2 +1 . 38 − 0 . 65 × 10 − 9 for the energy density, in agreement with the actual upper limits derived from the latest observation run of LIGO–Virgo. We demonstrate that a large number of sources in a speciﬁc corrected mass range yields to a bump in the background. This background could be detected with 8 years of coincident data by a network of 2G detectors.
AutoGEL: An Automated Graph Neural Network with Explicit Link Information

Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-world scenarios. Despite the great success, the architecture design of GNNs heavily relies on manual labor. Thus, automated graph neural network (AutoGNN) has attracted interest and attention from the research community, which makes significant performance improvements in recent years. However, existing AutoGNN works mainly adopt an implicit way to model and leverage the link information in the graphs, which is not well regularized to the link prediction task on graphs, and limits the performance of AutoGNN for other graph tasks. In this paper, we present a novel AutoGNN work that explicitly models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can handle the link prediction task and improve the performance of AutoGNNs on the node classification and graph classification task. Specifically, AutoGEL proposes a novel search space containing various design dimensions at both intra-layer and inter-layer designs and adopts a more robust differentiable search algorithm to further improve efficiency and effectiveness. Experimental results on benchmark data sets demonstrate the superiority of AutoGEL on several tasks.
Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance

The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron’s infectivity, vaccine-breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data points and extensively validated by experimental data on SARS-CoV-2, reveals that Omicron may be over ten times more contagious than the original virus or about twice as infectious as the Delta variant. Based on 132 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may be twice more likely to escape current vaccines than the Delta variant. The Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from Celltrion and Rockefeller University. However, its impact to Regeneron mAb cocktail appears to be mild.
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows

Motivated by oceanographic observational datasets, we propose a probabilistic neural network (PNN) model for calculating turbulent energy dissipation rates from vertical columns of velocity and density gradients in density stratified turbulent flows. We train and test the model on high-resolution simulations of decaying turbulence designed to emulate geophysical conditions similar to those found in the ocean. The PNN model outperforms a baseline theoretical model widely used to compute dissipation rates from oceanographic observations of vertical shear, being more robust in capturing the tails of the output distributions at multiple different time points during turbulent decay. A differential sensitivity analysis indicates that this improvement may be attributed to the ability of the network to capture additional underlying physics introduced by density gradients in the flow.
Advanced Activity-Based Models in Context of Planning Decisions

Travel demand modeling today is undergoing a transition from the conventional four-step models to a new generation of advanced activity-based models. The new generation of travel models is characterized by such distinctive features as the use of tours instead of trips as the base unit of travel, the generation of travel in the framework of daily activity agendas of individuals, and the use of fully disaggregate microsimulation techniques instead of the aggregate zonal calculations. Although the theoretical advantages of activity-based models—in particular, behavioral realism and consistency across all travel dimensions—are well known, the practical advantages in the context of planning decisions have rarely been discussed and documented. Experiences to date are summarized for application of activity-based models for various planning purposes in metropolitan regions of New York City; Columbus, Ohio; Atlanta, Georgia; San Francisco, California; and Montreal, Canada. The focus is on the practical planning questions and policies that were analyzed with these models and their relative strengths and advanced features compared with the four-step models. The planning questions and policies include congestion pricing schemes, high-occupancy-vehicle facilities, parking policy, testing impacts of demographic scenarios, and so on. It is shown that activity-based models are capable of treating these planning and policy issues at the level at which four-step models become inadequate.
Learning Robust Recommender from Noisy Implicit Feedback

The ubiquity of implicit feedback makes it indispensable for building recommender systems. However, it does not actually reflect the actual satisfaction of users. For example, in E-commerce, a large portion of clicks do not translate to purchases, and many purchases end up with negative reviews. As such, it is of importance to account for the inevitable noises in implicit feedback. However, little work on recommendation has taken the noisy nature of implicit feedback into consideration. In this work, we explore the central theme of denoising implicit feedback for recommender learning, including training and inference. By observing the process of normal recommender training, we find that noisy feedback typically has large loss values in the early stages. Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes the noisy interactions by two paradigms (i.e., Truncated Loss and Reweighted Loss). Furthermore, we consider extra feedback (e.g., rating) as auxiliary signal and propose three strategies to incorporate extra feedback into ADT: finetuning, warm-up training, and colliding inference. We instantiate the two paradigms on the widely used binary cross-entropy loss and test them on three representative recommender models. Extensive experiments on three benchmarks demonstrate that ADT significantly improves the quality of recommendation over normal training without using extra feedback. Besides, the proposed three strategies for using extra feedback largely enhance the denoising ability of ADT.
A Calabi operator for Riemannian locally symmetric spaces

On a Riemannian manifold of constant curvature, the Calabi operator is a second order linear differential operator that provides local integrability conditions for the range of the Killing operator. We generalise this operator to provide linear second order local integrability conditions on Riemannian locally symmetric spaces, whenever this is possible. Specifically, we show that this generalised operator always works in the irreducible case and we identify precisely those products for which it fails.
COMPARISON OF JUMP HEIGHT VALUES DERIVED FROM A FORCE PLATFORM AND VERTEC

This study simultaneously assessed jump heights derived from a force platform and a Vertec as well as the reliability of each instrument. Twenty-one recreationally active adults performed 3 maximal countermovement jumps reaching to a Vertec that was placed above the force platform. A repeated measures ANOVA was used to assess differences between Vertec jump height and force platform derived jump height. Results revealed a 27% higher jump height when assessed by the Vertec, compared to the force platform. Intra-class correlations were used to assess trial-to-trial reliability. Both instruments displayed excellent reliability. Practitioners could use the following regression equation to interpret measurements from the force platform: Vertec jump height = force platform height (1.024) + 0.142m.
Evaluation of Surface Infiltration Testing Procedures in Permeable Pavement Systems

AbstractThe ASTM method for measuring the infiltration rate of in-place pervious concrete provides limited guidance on how to select test locations and how results should be interpreted to assess surface condition and maintenance needs. The ASTM method is written specifically for pervious concrete, so additional research is needed to determine the applicability of this method to other permeable pavement types. In 2009, the U.S. Environmental Protection Agency constructed a 0.4-ha parking lot surfaced with permeable interlocking concrete pavers (PICP), pervious concrete (PC), and porous asphalt (PA). Surface infiltration testing was conducted for almost three years, and two methods were used to select test locations: monthly testing at randomly selected locations and quarterly testing at fixed locations. Infiltration rates were significantly different for each pavement type. With almost three years of use, maintenance has yet to be required, although infiltration has decreased in areas immediately downgrad.
Neural correlates of acoustic dissonance in music: The role of musicianship, schematic and veridical expectations

In western music, harmonic expectations can be fulfilled or broken by unexpected chords. Musical irregularities in the absence of auditory deviance elicit well-studied neural responses (e.g. ERAN, P3, N5). These responses are sensitive to schematic expectations (induced by syntactic rules of chord succession) and veridical expectations about predictability (induced by experimental regularities). However, the cognitive and sensory contributions to these responses and their plasticity as a result of musical training remains under debate. In the present study, we explored whether the neural processing of pure acoustic violations is affected by schematic and veridical expectations. Moreover, we investigated whether these two factors interact with long-term musical training. In Experiment 1, we registered the ERPs elicited by dissonant clusters placed either at the middle or the ending position of chord cadences. In Experiment 2, we presented to the listeners with a high proportion of cadences ending in a dissonant chord. In both experiments, we compared the ERPs of musicians and non-musicians. Dissonant clusters elicited distinctive neural responses (an early negativity, the P3 and the N5). While the EN was not affected by syntactic rules, the P3a and P3b were larger for dissonant closures than for middle dissonant chords. Interestingly, these components were larger in musicians than in non-musicians, while the N5 was the opposite. Finally, the predictability of dissonant closures in our experiment did not modulate any of the ERPs. Our study suggests that, at early time windows, dissonance is processed based on acoustic deviance independently of syntactic rules. However, at longer latencies, listeners may be able to engage integration mechanisms and further processes of attentional and structural analysis dependent on musical hierarchies, which are enhanced in musicians.
Stronger Baseline for Person Re-Identification

Person re-identification (re-ID) aims to identify the same person of interest across non-overlapping capturing cameras, which plays an important role in visual surveillance applications and computer vision research areas. Fitting a robust appearance-based representation extractor with limited collected training data is crucial for person re-ID due to the high expanse of annotating the identity of unlabeled data. In this work, we propose a Stronger Baseline for person re-ID, an enhancement version of the current prevailing method, namely, Strong Baseline, with tiny modifications but a faster convergence rate and higher recognition performance. With the aid of Stronger Baseline, we obtained the third place (i.e., 0.94 in mAP) in 2021 VIPriors Re-identification Challenge without the auxiliary of ImageNet-based pre-trained parameter initialization and any extra supplemental dataset.