Miracle-dz/UniFAR-FTUs huggingface dataset:
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization

6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a nonlinear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses. Furthermore, to improve the robustness to occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance.
The US Naval Observatory VLBI Spectroscopic Catalog

Despite their importance for astrometry and navigation, the active galactic nuclei (AGNs) that comprise the International Celestial Reference Frame (ICRF) are relatively poorly understood, with key information such as their spectroscopic redshifts, AGN spectral type, and emission-/absorption-line properties generally missing from the literature. Using an updated, publicly available, state-of-the-art spectroscopic fitting code optimized for the spectra of AGNs from low to high redshift, we present a catalog of emission-line and spectral continuum parameters for 1014 unique ICRF3 objects with single-fiber spectra from the Sloan Digital Sky Survey DR16. We additionally present black hole virial mass scaling relationships that use Hα-, Hβ-, Mg ii-, and C iv-based line widths, all consistent with each other, which can be used in studies of radio-loud objects across a wide range of redshifts, and we use these scaling relationships to provide derived properties such as black hole masses and bolometric luminosities for the catalog. We briefly comment on these properties for the ICRF objects, as well as their overall spectroscopic characteristics.
SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel Modeling

We present a self-supervised speech restoration method without paired speech corpora. Because the previous general speech restoration method uses artificial paired data created by applying various distortions to high-quality speech corpora, it cannot sufficiently represent acoustic distortions of real data, limiting the applicability. Our model consists of analysis, synthesis, and channel modules that simulate the recording process of degraded speech and is trained with real degraded speech data in a self-supervised manner. The analysis module extracts distortionless speech features and distortion features from degraded speech, while the synthesis module synthesizes the restored speech waveform, and the channel module adds distortions to the speech waveform. Our model also enables audio effect transfer, in which only acoustic distortions are extracted from degraded speech and added to arbitrary high-quality audio. Experimental evaluations with both simulated and real data show that our method achieves significantly higher-quality speech restoration than the previous supervised method, suggesting its applicability to real degraded speech materials.
Conjugate Plateau constructions in product spaces

This survey paper investigates, from a purely geometric point of view, Daniel’s isometric conjugation between minimal and constant mean curvature surfaces immersed in homogeneous Riemannian three-manifolds with isometry group of dimension four. On the one hand, we collect the results and strategies in the literature that have been developed so far to deal with the analysis of conjugate surfaces and their embeddedness. On the other hand, we revisit some constructions of constant mean curvature surfaces in the homogeneous product spaces S2 ×R, H2 ×R and R3 having different topologies and geometric properties depending on the value of the mean curvature. Finally, we also provide some numerical pictures using Surface Evolver.
HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and ﬂow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vocoders, we propose a novel HiFi++ general framework for neural vocoding, band-width extension, and speech enhancement. We show that with the improved generator architecture and simpliﬁed multi-discriminator training, HiFi++ performs on par with the state-of-the-art in these tasks while spending signiﬁcantly less memory and computational resources. The effectiveness of our approach is validated through a series of extensive experiments.
Token Dropping for Efficient BERT Pretraining

Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective “token dropping” method to accelerate the pretraining of transformer models, such as BERT, without degrading its performance on downstream tasks. In particular, we drop unimportant tokens starting from an intermediate layer in the model to make the model focus on important tokens more efficiently if with limited computational resource. The dropped tokens are later picked up by the last layer of the model so that the model still produces full-length sequences. We leverage the already built-in masked language modeling (MLM) loss to identify unimportant tokens with practically no computational overhead. In our experiments, this simple approach reduces the pretraining cost of BERT by 25% while achieving similar overall fine-tuning performance on standard downstream tasks.
Determination of the gluon condensate with DIS experiments using holographic approach

We study the deep inelastic scattering (DIS) of a proton-targeted lepton in the presence of gluon condensation using gauge/gravity duality. We use a modiﬁed AdS 5 background where the modiﬁcation parameter c corresponds to the gluon condensation in the boundary theory. First, when examining the electromagnetic ﬁeld, we ﬁnd that non-zero c can increase the magnitude of the ﬁeld. In the next step, we calculate wave function equations for baryonic states where the mass of the proton target requires a value contribution of c as c = 0 . 0120 GeV 4 . Proceeding by electromagnetic ﬁeld and baryonic states, we derive the holographic interaction action related to the amplitude of the scattering. Finally, we compute the corresponding structure functions numerically as functions of x and q , which are Bj¨orken variables and the lepton momentum transfers, respectively. Comparing the Jlab Hall C data with our theoretical calculations, our results are acceptable.
Using Orientation to Distinguish Overlapping Chromosomes

A diﬃcult step in the process of karyotyping is segmenting chromosomes that touch or overlap. In an attempt to automate the process, previous studies turned to Deep Learning methods, with some formulating the task as a semantic segmentation problem. These models treat separate chromosome instances as semantic classes, which we show to be problematic, since it is uncertain which chromosome should be classed as #1 and #2. Assigning class labels based on comparison rules, such as the shorter/longer chromosome alleviates, but does not fully re-solve the issue. Instead, we separate the chromosome instances in a second stage, predict-ing the orientation of the chromosomes by the model and use it as one of the key distinguishing factors of the chromosomes. We demonstrate this method to be eﬀective. Furthermore, we introduce a novel Double-Angle representation that a neural network can use to predict the orientation. The representation maps any direction and its reverse to the same point. Lastly, we present a new expanded synthetic dataset, which is based on Pommier’s dataset, but ad-dresses its issues with insuﬃcient separation between its training and testing sets.
Linearizing Transformer with Key-Value Memory Bank

Transformer has brought great success to a wide range of natural language processing tasks. Nevertheless, the computational overhead of the vanilla transformer scales quadratically with sequence length. Many efforts have been made to develop more efﬁcient transformer variants. A line of work (e.g., Linformer) projects the input sequence into a low-rank space, achieving linear time complexity. However, Linformer does not suit well for text generation tasks as the sequence length must be pre-speciﬁed. We propose MemSizer, an approach also projects the source sequence into lower dimension representation but can take input with dynamic length, with a different perspective of the attention mechanism. MemSizer not only achieves the same linear time complexity but also enjoys efﬁcient recurrent-style autoregressive generation, which yields constant memory complexity and reduced computation at inference. We demonstrate that MemSizer provides an improved tradeoff between efﬁciency and accuracy over the vanilla transformer and other linear variants in language modeling and machine translation tasks, revealing a viable direction to-wards further inference efﬁciency improve-ment.
Distributionally Robust Optimization via Ball Oracle Acceleration

We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses. In particular, we consider group-structured and bounded $f$-divergence uncertainty sets. Our approach relies on an accelerated method that queries a ball optimization oracle, i.e., a subroutine that minimizes the objective within a small ball around the query point. Our main contribution is efficient implementations of this oracle for DRO objectives. For DRO with $N$ non-smooth loss functions, the resulting algorithms find an $\epsilon$-accurate solution with $\widetilde{O}\left(N\epsilon^{-2/3} + \epsilon^{-2}\right)$ first-order oracle queries to individual loss functions. Compared to existing algorithms for this problem, we improve complexity by a factor of up to $\epsilon^{-4/3}$.
Two-Timescale Resource Allocation for Automated Networks in IIoT

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.
Enhancing Classifier Conservativeness and Robustness by Polynomiality

We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among others, leads purposefully to random-level performance in the tails, away from the bulk of the training data. A directly related, simple, yet important technical novelty we subsequently present is softRmax: a reasoned alternative to the standard softmax function employed in contemporary (deep) neural networks. It is derived through linking the standard softmax to Gaussian class-conditional models, as employed in LDA, and replacing those by a polynomial alternative. We show that two aspects of softRmax, conservativeness and inherent gradient regularization, lead to robustness against adversarial attacks without gradient obfuscation.
Towards Efficient and Elastic Visual Question Answering with Doubly Slimmable Transformer

—Transformer-based approaches have shown great success in visual question answering (VQA). However, they usually require deep and wide models to guarantee good performance, making it difﬁcult to deploy on capacity-restricted platforms. It is a challenging yet valuable task to design an elastic VQA model that supports adaptive pruning at runtime to meet the efﬁciency constraints of diverse platforms. In this paper, we present the Doubly Slimmable Transformer (DST), a general framework that can be seamlessly integrated into arbitrary Transformer-based VQA models to train one single model once and obtain various slimmed submodels of different widths and depths. Taking two typical Transformer-based VQA approaches, i.e. , MCAN [1] and UNITER [2], as the reference models, the obtained slimmable MCAN DST and UNITER DST models outperform the state-of-the-art methods trained independently on two benchmark datasets. In particular, one slimmed MCAN DST submodel achieves a comparable accuracy on VQA-v2, while being 0.38 × smaller in model size and having 0.27 × fewer FLOPs than the reference MCAN model. The smallest MCAN DST submodel has 9M parameters and 0.16G FLOPs in the inference stage, making it possible to be deployed on edge devices.
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals

A visual counterfactual explanation replaces image regions in a query image with regions from a distractor image such that the system's decision on the transformed image changes to the distractor class. In this work, we present a novel framework for computing visual counterfactual explanations based on two key ideas. First, we enforce that the replaced and replacer regions contain the same semantic part, resulting in more semantically consistent explanations. Second, we use multiple distractor images in a computationally efficient way and obtain more discriminative explanations with fewer region replacements. Our approach is 27 % more semantically consistent and an order of magnitude faster than a competing method on three fine-grained image recognition datasets. We highlight the utility of our counterfactuals over existing works through machine teaching experiments where we teach humans to classify different bird species. We also complement our explanations with the vocabulary of parts and attributes that contributed the most to the system's decision. In this task as well, we obtain state-of-the-art results when using our counterfactual explanations relative to existing works, reinforcing the importance of semantically consistent explanations. Source code is available at https://github.com/facebookresearch/visual-counterfactuals.
6G Wireless Communications: From Far-Field Beam Steering to Near-Field Beam Focusing

6G networks will be required to support higher data rates, improved energy efficiency, lower latency, and more diverse users compared with 5G systems. To meet these requirements, electrically extremely large-scale antenna arrays are envisioned to be key physical-layer technologies. As a consequence, it is expected that some portion of future 6G wireless communications may take place in the radiating near-field (Fresnel) region, in addition to the far-field operation as in current wireless technologies. In this article, we discuss the opportunities and challenges that arise in radiating near-field communications. We begin by discussing the key physical characteristics of near-field communications, where the standard plane-wave propagation assumption no longer holds, and clarifying its implication on the modelling of wireless channels. Then, we elaborate on the ability to leverage spherical wavefronts via beam focusing, highlighting its advantages for 6G systems. We point out several appealing application scenarios which, with proper design, can benefit from near-field operation, including interference mitigation in multi-user communications, accurate localization and focused sensing, as well as wireless power transfer with minimal energy pollution. We conclude by discussing some of the design challenges and research directions that are yet to be explored to fully harness the potential of near-field operation.
Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

Conversational recommender systems (CRSs) imitate human advisors to assist users in finding items through conversations and have recently gained increasing attention in domains such as media and e-commerce. Like in human communication, building trust in human-agent communication is essential given its significant influence on user behavior. However, inspiring user trust in CRSs with a “one-size-fits-all” design is difficult, as individual users may have their own expectations for conversational interactions (e.g., who, user or system, takes the initiative), which are potentially related to their personal characteristics. In this study, we investigated the impacts of three personal characteristics, namely personality traits, trust propensity, and domain knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative and mixed-initiative. Our between-subjects user study (N=148) revealed that users’ trust propensity and domain knowledge positively influenced their trust in CRSs, and that users with high conscientiousness tended to trust the mixed-initiative system.
GraphCoCo: Graph Complementary Contrastive Learning

Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embeddings by maximizing the Mutual Information (MI) between different augmented views of the same graph (positive pairs). However, we identify a obstacle that the optimization of InfoNCE loss only concentrates on a few embeddings dimensions, limiting the distinguishability of embeddings in downstream graph classiﬁcation tasks. This paper proposes an effective graph co mplementary co ntrastive learning approach named GraphCoCo to tackle the above issue. Speciﬁcally, we set the embedding of the ﬁrst augmented view as the anchor embedding to localize “highlighted” dimensions (i.e., the dimensions contribute most in similarity measurement). Then remove these dimensions in the embeddings of the second augmented view to discover neglected complementary representations. Therefore, the combination of anchor and complementary embeddings signiﬁcantly improves the performance in downstream tasks. Comprehensive experiments on various benchmark datasets are conducted to demonstrate the effectiveness of GraphCoCo, and the results show that our model outperforms the state-of-the-art methods.
Relative Effectiveness of Four Doses Compared to Three Dose of the BNT162b2 Vaccine in Israel

Objectives: The rapid spread of the Omicron variant (B.1.1.529) alongside evidence of a relatively rapid waning of the third dose prompted Israel to administer a fourth dose of the BNT162b2 vaccine on January 2022. Thus far, sufficient real-world evidence demonstrating the effectiveness of a fourth dose against infection and severe COVID-19 are lacking. This study examined the short-term effectiveness of a fourth dose compared to three doses over the span of 10 weeks. Design: A retrospective test-negative case-control study, performing both a matched analysis and an unmatched multiple-tests analysis. Setting: Nationally centralized database of Maccabi Healthcare Services (MHS), an Israeli national health fund that covers 2.5 million people. Participants: The study population included 97,499 MHS members aged 60 or older who were eligible to receive a fourth vaccine dose and performed at least one PCR test during the study period. Of them, 27,876 received the fourth dose and 69,623 received only three doses. Main outcomes and measures: Analyses focused on the period from January 10, 2022 (7 days after the fourth dose was first administered to eligible individuals) to March 13, 2022, an Omicron-dominant period in Israel. We evaluated two SARS-CoV-2-related outcomes: (1) breakthrough infection, defined as a positive PCR test performed 7 or more days after inoculation with the BNT162b2 vaccine; and (2) breakthrough infection resulting in a severe disease, defined as COVID-19-related hospitalization or COVID-19 associated mortality. Results: A fourth dose provided considerable additional protection against both SARS-CoV-2 infection and severe disease relative to three doses of the vaccine. However, vaccine effectiveness against infection varied over time, peaking during the third week with a VE of 64% (95% CI: 62.0%-65.9%) and declining to 29.2% (95% CI: 17.7%-39.1%) by the end of the 10-week follow-up period. Unlike VE against infection, the relative effectiveness of a fourth dose against severe COVID-19 was maintained at high level (>73%) throughout the 9-week follow-up period. Importantly, severe disease was a relatively rare event, occurring in <1% of both fourth dose and third dose only recipients. Conclusions: A fourth dose of the BNT162b2 vaccine provided considerable additional protection against both SARS-CoV-2 infection and severe disease relative to three doses of the vaccine. However, effectiveness of the fourth dose against infection wanes sooner than that of the third dose.