Miracle-dz/UniFAR-FTUs huggingface dataset:
Anytime Dense Prediction with Confidence Adaptivity

Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classiﬁcation. We propose the ﬁrst uniﬁed and end-to-end approach for anytime dense prediction. A cascade of “exits” is attached to the model to make multiple predictions. We redesign the exits to account for the depth and spatial resolution of the features for each exit. To reduce total computation, and make full use of prior predictions, we develop a novel spatially adaptive approach to avoid further computation on regions where early predictions are already sufﬁciently conﬁdent. Our full method, named anytime dense prediction with conﬁdence (ADP-C), achieves the same level of ﬁnal accuracy as the base model, and meanwhile signiﬁcantly reduces total computation. We evaluate our method on Cityscapes semantic segmentation and MPII human pose estimation: ADP-C en-ables anytime inference without sacriﬁcing accuracy while also reducing the total FLOPs of its base models by 44.4% and 59.1%. We compare with anytime inference by deep equilibrium networks and feature-based stochastic sampling, show-ing that ADP-C dominates both across the accuracy-computation curve. Our code is available at https://github.com/liuzhuang13/anytime . ﬁnal We
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate

Cross-Lingual Summarization (CLS) is a task that extracts important information from a source document and summarizes it into a summary in another language. It is a challenging task that requires a system to understand, summarize, and translate at the same time, making it highly related to Monolingual Summarization (MS) and Machine Translation (MT). In practice, the training resources for Machine Translation are far more than that for cross-lingual and monolingual summarization. Thus incorporating the Machine Translation corpus into CLS would be beneficial for its performance. However, the present work only leverages a simple multi-task framework to bring Machine Translation in, lacking deeper exploration. In this paper, we propose a novel task, Cross-lingual Summarization with Compression rate (CSC), to benefit Cross-Lingual Summarization by large-scale Machine Translation corpus. Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%. Hence they can be trained as a unified task, sharing knowledge more effectively. However, a huge gap exists between the MT task and the CLS task, where samples with compression rates between 30% and 90% are extremely rare. Hence, to bridge these two tasks smoothly, we propose an effective data augmentation method to produce document-summary pairs with different compression rates. The proposed method not only improves the performance of the CLS task, but also provides controllability to generate summaries in desired lengths. Experiments demonstrate that our method outperforms various strong baselines in three cross-lingual summarization datasets. We released our code and data at https://github.com/ybai-nlp/CLS_CR.
Noncooperative Herding With Control Barrier Functions: Theory and Experiments

In this paper, we consider the problem of protecting a high-value unit from inadvertent attack by a group of agents using defending robots. Specifically, we develop a control strategy for the defending agents that we call "dog robots" to prevent a flock of "sheep agents" from breaching a protected zone. The sheep agents have no knowledge about the presence of the high-value unit and follow flocking dynamics to reach their goal. We take recourse to control barrier functions to pose this problem and exploit the interaction dynamics between the sheep and dogs to find dogs’ velocities that result in the sheep getting repelled from the zone. We solve a QP reactively that incorporates the defending constraints to compute the desired velocities for all dogs. Our proposed framework also allows for simultaneous inclusion of multiple protected zones by augmenting constraints on dogs’ velocities. We provide a theoretical proof of the feasibility of our strategy for the one dog/one sheep case. Additionally, we provide empirical results of two dogs defending the zone from up to ten sheep, averaged over a hundred simulations, and report high success rates. We also demonstrate this algorithm experimentally on non-holonomic robots. Videos of these results are available at https://tinyurl.com/4dj2kjwx.
MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron

Applications of face recognition systems for authentication purposes are growing rapidly. Although state-of-the-art (SOTA) face recognition systems have high recognition accuracy, the features which are extracted for each user and are stored in the system's database contain privacy-sensitive information. Accordingly, compromising this data would jeopardize users' privacy. In this paper, we propose a new cancelable template protection method, dubbed MLP-hash, which generates protected templates by passing the extracted features through a user-specific randomly-weighted multi-layer perceptron (MLP) and binarizing the MLP output. We evaluated the unlinkability, irreversibility, and recognition accuracy of our proposed biometric template protection method to fulfill the ISO/IEC 30136 standard requirements. Our experiments with SOTA face recognition systems on the MOBIO and LFW datasets show that our method has competitive performance with the BioHashing and IoM Hashing (IoM-GRP and IoM-URP) template protection algorithms. We provide an open-source implementation of all the experiments presented in this paper so that other researchers can verify our findings and build upon our work.
Phase diagram of superconductivity in the integer quantum Hall regime

Significance Interaction between electrons leads to ordered states, such as magnets and superconductors, which have had tremendously successful technological applications. States with “topological” order have attracted much attention in recent years, canonical examples being integer and fractional quantum Hall effects, phenomena that occur when electrons in two dimensions are subjected to a magnetic field. We consider here theoretically electrons in the quantum Hall regime with superconducting correlations and find a rich phase diagram with states supporting exotic emergent entities such as “Majoranas” and “skyrmions. ” The former are thought to be examples of particles that are neither fermions nor bosons but obey non-Abelian statistics.
Complete Policy Regret Bounds for Tallying Bandits

Policy regret is a well established notion of measuring the performance of an online learning algorithm against an adaptive adversary. We study restrictions on the adversary that enable efﬁcient minimization of the complete policy regret , which is the strongest possible version of policy regret. We identify a gap in the current theoretical understanding of what sorts of restrictions permit tractability in this challenging setting. To resolve this gap, we consider a generalization of the stochastic multi armed bandit, which we call the tallying bandit . This is an online learning setting with an m -memory bounded adversary, where the average loss for playing an action is an unknown function of the number (or tally) of times that the action was played in the last m timesteps. For tallying bandit problems with K actions and time horizon T , we provide an algorithm that w.h. p achieves a complete policy regret guarantee of (cid:101) O ( mK √ T ) , where the (cid:101) O notation hides only logarithmic factors. We additionally prove an (cid:101) Ω( √ mKT ) lower bound on the expected complete policy regret of any tallying bandit algorithm, demonstrating the near optimality of our method.
Fine-tuning Pruned Networks with Linear Over-parameterization

Structured pruning compresses neural networks by reducing channels (ﬁlters) for fast inference and low footprint at run-time. To restore accuracy after pruning, ﬁne-tuning is usually applied to pruned networks. However, too few remaining parameters in pruned networks inevitably bring a great challenge to ﬁne-tuning to restore accuracy. To address this challenge, we propose a novel method that ﬁrst linearly over-parameterizes the compact layers in pruned networks to enlarge the number of ﬁne-tuning parameters and then re-parameterizes them to the original layers after ﬁne-tuning. Speciﬁcally, we equivalently expand the convolution/linear layer with several consecutive convolution/linear layers that do not alter the current output feature maps. Furthermore, we utilize similarity-preserving knowledge distillation that encourages the over-parameterized block to learn the immediate data-to-data similarities of the corresponding dense layer to maintain its feature learning ability. The proposed method is comprehensively evaluated on CIFAR-10 and ImageNet which signiﬁcantly outperforms the vanilla ﬁne-tuning strategy, especially for large pruning ratio.
Modeling the Eﬀect of Observational Social Learning on Vaccination and Diseases using Household Networks

In this paper, we study observational social learning and vaccine-preventable childhood disease spread over a bi-layer household network. The two overlapping networks are either Erd˝os-R´enyi (random) network or Barab´asi-Albert network. We introduce a new Bayesian rule of observational social learning over the social network of which other decision models, like voting and DeGroot models, are special cases. We show how social learning can lead to convergence of opinions and aﬀect levels of vaccine uptake. We also study the eﬀect of the existence of two cultures of social learning on the establishment of social norms and levels of vaccine uptake and so on the disease spread. The mutual inﬂuence between the opinion change through observational social learning and the disease spread shows dependence on the network’s topology and on the vaccine safety and availability.
GFCL: A GRU-based Federated Continual Learning Framework against Adversarial Attacks in IoV

—The integration of ML in 5G-based Internet of Vehicles (IoV) networks has enabled intelligent transportation and smart trafﬁc management. Nonetheless, the security against adversarial attacks is also increasingly becoming a challenging task. Speciﬁcally, Deep Reinforcement Learning (DRL) is one of the widely used ML designs in IoV applications. The standard ML security techniques are not effective in DRL where the algorithm learns to solve sequential decision-making through continuous interaction with the environment, and the environment is time-varying, dynamic, and mobile. In this paper, we propose a Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly detection framework against adversarial attacks in IoV. The objective is to present a lightweight and scalable framework that learns and detects the illegitimate behavior without having a-priori training dataset consisting of attack samples. We use GRU to predict a future data sequence to analyze and detect illegitimate behavior from vehicles in a federated learning-based distributed manner. We investigate the performance of our framework using real-world vehicle mobility traces. The results demonstrate the effectiveness of our proposed solution for different performance metrics.
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization

Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional features widely exist in the era of big data. Safe screening is a popular technique to speed up high-dimensional models by discarding the inactive features with zero coefficients. Nevertheless, existing safe screening methods are limited to the sequential setting. In this paper, we propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively, which can achieve significant speedup without any loss of accuracy by simultaneously enjoying the sparsity of the model and dataset. To the best of our knowledge, this is the first work of distributed safe dynamic screening method. Theoretically, we prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely. Finally, extensive experimental results on benchmark datasets confirm the superiority of our proposed method.
Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy

Recently, audio-visual scene classification (AVSC) has attracted increasing attention from multidisciplinary communities. Previous studies tended to adopt a pipeline training strategy, which uses well-trained visual and acoustic encoders to extract high-level representations (embeddings) first, then utilizes them to train the audio-visual classifier. In this way, the extracted embeddings are well suited for uni-modal classifiers, but not necessarily suited for multi-modal ones. In this paper, we propose a joint training framework, using the acoustic features and raw images directly as inputs for the AVSC task. Specifically, we retrieve the bottom layers of pre-trained image models as visual encoder, and jointly optimize the scene classifier and 1D-CNN based acoustic encoder during training. We evaluate the approach on the development dataset of TAU Urban Audio-Visual Scenes 2021. The experimental results show that our proposed approach achieves significant improvement over the conventional pipeline training strategy. Moreover, our best single system outperforms previous state-of-the-art methods, yielding a log loss of 0.1517 and accuracy of 94.59% on the official test fold.
On The large Time Asymptotics of Klein-Gordon type equations with General Data-I

We study the Klein-Gordon equation with general interaction term, which may be linear or nonlinear, and space-time dependent. The initial data is general, large and non-radial. We prove that global solutions are asymptotically given by a free wave and a weakly localized part. The proof is based on constructing in a new way the Free Channel Wave Operator, and further tools from the recent works \cite{Liu-Sof1,Liu-Sof2,SW2020,SW2022}. This work generalizes the results of the first part of \cite{Liu-Sof1,Liu-Sof2} on the Schr\"odinger equation to arbitrary dimension, and non-radial data.
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems

Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.
Simulating bulk viscosity in neutron stars. I. Formalism

The faithful inclusion of the effects of bulk viscosity induced by the presence of chemical reactions is an important issue for simulations of core-collapse supernovae, binary neutron star mergers, and neutron star oscillations, where particle abundances are locally pushed out of chemical equilibrium by rarefaction and compression of the fluid elements. In this work, we discuss three different approaches that can be used to implement bulk viscosity in general relativistic hydrodynamic simulations of neutron stars: the exact multi-component reacting fluid, and two M\"uller-Israel-Stewart theories, namely the second order Hiscock-Lindblom model and its linear limit, the Maxwell-Cattaneo model. After discussing the theory behind the three approaches, we specialize their dynamics equations to spherical symmetry in the radial gauge-polar slicing (i.e., Schwarzschild) coordinates. We also discuss a particular choice for the equation of state of the fluid and the associated neutrino emission rates, which are used in a companion paper for the numerical comparison of the three frameworks, and we obtain the effective sound speed for the Hiscock-Lindblom theory in the non-linear regime.
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition

High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch. Based on the latent dependency between emotions and AUs, an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies. Finally, a re-labeling strategy corrects the ambiguous annotations by comparing their feature similarities with semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively, outperform the baseline and state-of-the-art methods.
A census of young stellar objects in two line-of-sight star forming regions toward IRAS 22147+5948 in the outer Galaxy

(abridged) Star formation in the outer Galaxy, i.e., outside of the Solar circle, has been lightly studied in part due to low CO brightness of molecular clouds linked with the negative metallicity gradient. Recent infrared surveys provide an overview of dust emission in large sections of the Galaxy, but suffer from cloud confusion and poor spatial resolution at far-infrared wavelengths. We aim to develop a methodology to identify and classify young stellar objects (YSOs) in star-forming regions in the outer Galaxy, and use it to solve a long-standing confusion with the distance and evolutionary status of IRAS 22147+5948. We use Support Vector Machine learning algorithm to complement standard color-color and color-magnitude diagrams in search for YSOs in the IRAS 22147 region using publicly available data from the `Spitzer Mapping of the Outer Galaxy' survey. The agglomerative hierarchical clustering algorithm is used to identify clusters, along with the Robitaille et al. (2017) code to calculate physical properties of individual YSOs. We identify 13 Class I and 13 Class II YSO candidates using the color-color diagrams, and additional 2 and 21 sources, respectively, using the machine learning techniques. Spectral energy distributions of 23 sources are modelled with a star and a passive disk, corresponding to Class II objects. Models of 3 sources include envelopes typical for Class I objects. The objects are grouped in 2 clusters located at the distance of ~2.2 kpc, and 5 clusters at ~5.6 kpc. The spatial extent of CO, radio continuum, and dust emission confirms the origin of YSOs in two distinct star-forming regions along a similar line-of-sight. The outer Galaxy might serve as a unique laboratory of star formation across environments on condition that complementary methods and ancillary data are used to properly account for cloud confusion and distance uncertainties.
Improving Self-Supervised Learning-based MOS Prediction Networks

MOS (Mean Opinion Score) is a subjective method used for the evaluation of a system’s quality. Telecommunications (for voice and video), and speech synthesis systems (for gener-ated speech) are a few of the many applications of the method. While MOS tests are widely accepted, they are time-consuming and costly since human input is required. In addition, since the systems and subjects of the tests differ, the results are not re-ally comparable. On the other hand, a large number of previous tests allow us to train machine learning models that are capable of predicting MOS value. By automatically predicting MOS values, both the aforementioned issues can be resolved. The present work introduces data-, training- and post-training speciﬁc improvements to a previous self-supervised learning-based MOS prediction model. We used a wav2vec 2.0 model pre-trained on LibriSpeech, extended with LSTM and non-linear dense layers. We introduced transfer learning, target data preprocessing a two- and three-phase training method with different batch formulations, dropout accumulation (for larger batch sizes) and quantization of the predictions. The methods are evaluated using the shared synthetic speech dataset of the ﬁrst Voice MOS challenge.
NVM-ESR: Using Non-Volatile Memory in Exact State Reconstruction of Preconditioned Conjugate Gradient

HPC systems are a critical resource for scientific research and advanced industries. The demand for computational power and memory is increasing and ushers in the exascale era , in which supercomputers are designed to provide enormous computing power to meet these needs. These complex supercomputers consist of many compute nodes and are consequently expected to experience frequent faults and crashes. Exact state reconstruction ( ESR ) has been proposed as a mechanism to alleviate the impact of frequent failures on long-term computations. ESR has shown great potential in the context of iterative linear algebra solvers , a key building block in numerous scientific applications. Recent designs of supercomputers feature the emerging non-volatile memory ( NVM ) technology. For example, the Exascale Aurora supercomputer is planned to integrate Intel Optane™ DCPMM. This work investigates how NVM can be used to improve ESR so that it can scale to future exascale systems such as Aurora and provide enhanced resilience. We propose the non-volatile memory ESR ( NVM-ESR ) mechanism. NVM-ESR demonstrates how NVM can be utilized in supercom-puters for enabling efficient recovery from faults while requiring significantly smaller memory footprint and time overheads in comparison to ESR. We focus on the preconditioned conjugate gradient ( PCG ) iterative solver also studied in prior ESR research, because it is employed by the representative HPCG scientific benchmark. The source code used by this work, as well as the benchmarks and other relevant sources, are available at: https://github.com/ Scientific-Computing-Lab-NRCN/NVM-ESR.git.