Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing objective quality metrics for JPEG and MPEG point cloud coding

As applications using immersive media gain increasing attention from both academia and industry, research in the field of point cloud compression has greatly intensified in recent years, leading to the development of the MPEG compression standards V-PCC and G-PCC, as well as the more recent JPEG Pleno learning-based point cloud coding. Each of the above-mentioned standards is based on a different algorithm, introducing distinct types of degradation that may impair the quality of experience when high lossy compression is applied. Although the impact on perceptual quality could be accurately evaluated during subjective quality assessment experiments, objective quality metrics serve as predictors of the visually perceived quality and provide similarity scores without human intervention. Nevertheless, their accuracy can be susceptible to the characteristics of the evaluated media as well as to the type and intensity of the added distortion. While the performance of multiple state-of-the-art objective quality metrics has already been evaluated through their correlation with subjective scores obtained in the presence of artifacts produced by the MPEG standards, no study has evaluated how metrics perform with the more recent JPEG Pleno point cloud coding. In this paper, a study is conducted to benchmark the performance of a large set of objective quality metrics in a subjective dataset including distortions produced by JPEG and MPEG codecs. The dataset also contains three different trade-offs between color and geometry compression for each codec, adding another dimension to the analysis. Performance indexes are computed over the entire dataset but also after splitting according to the codec and to the original model, resulting in detailed insights about the overall performance of each visual quality predictor as well as their cross-content and cross-codec generalization ability.
CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions

Writing a scientific article is a challenging task as it is a highly codified and specific genre, consequently proficiency in written communication is essential for effectively conveying research findings and ideas. In this article, we propose an original textual resource on the revision step of the writing process of scientific articles. This new dataset, called CASIMIR, contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. Pairs of consecutive versions of an article are aligned at sentence-level while keeping paragraph location information as metadata for supporting future revision studies at the discourse level. Each pair of revised sentences is enriched with automatically extracted edits and associated revision intention. To assess the initial quality on the dataset, we conducted a qualitative study of several state-of-the-art text revision approaches and compared various evaluation metrics. Our experiments led us to question the relevance of the current evaluation methods for the text revision task.
Set-Valued Control Functions

The control function approach allows the researcher to identify various causal effects of interest. While powerful, it requires a strong invertibility assumption, which limits its applicability. This paper expands the scope of the nonparametric control function approach by allowing the control function to be set-valued and derive sharp bounds on structural parameters. The proposed generalization accommodates a wide range of selection processes involving discrete endogenous variables, random coefficients, treatment selections with interference, and dynamic treatment selections.
ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting

In recent years, text-image joint pre-training techniques have shown promising results in various tasks. However, in Optical Character Recognition (OCR) tasks, aligning text instances with their corresponding text regions in images poses a challenge, as it requires effective alignment between text and OCR-Text (referring to the text in images as OCR-Text to distinguish from the text in natural language) rather than a holistic understanding of the overall image content. In this paper, we propose a new pre-training method called OCR-Text Destylization Modeling (ODM) that transfers diverse styles of text found in images to a uniform style based on the text prompt. With ODM, we achieve better alignment between text and OCR-Text and enable pre-trained models to adapt to the complex and diverse styles of scene text detection and spotting tasks. Additionally, we have designed a new labeling generation method specifically for ODM and combined it with our proposed Text-Controller module to address the challenge of annotation costs in OCR tasks, allowing a larger amount of unlabeled data to participate in pre-training. Extensive experiments on multiple public datasets demonstrate that our method significantly improves performance and outperforms current pre-training methods in scene text detection and spotting tasks. Code is available at {https://github.com/PriNing/ODM}.
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview

End-to-end (E2E) approach is gradually replacing hybrid models for automatic speech recognition (ASR) tasks. However, the optimization of E2E models lacks an intuitive method for handling decoding shifts, especially in scenarios with a large number of domain-specific rare words that hold specific important meanings. Furthermore, the absence of knowledge-intensive speech datasets in academia has been a significant limiting factor, and the commonly used speech corpora exhibit significant disparities with realistic conversation. To address these challenges, we present Medical Interview (MED-IT), a multi-turn consultation speech dataset that contains a substantial number of knowledge-intensive named entities. We also explore methods to enhance the recognition performance of rare words for E2E models. We propose a novel approach, post-decoder biasing, which constructs a transform probability matrix based on the distribution of training transcriptions. This guides the model to prioritize recognizing words in the biasing list. In our experiments, for subsets of rare words appearing in the training speech between 10 and 20 times, and between 1 and 5 times, the proposed method achieves a relative improvement of 9.3% and 5.1%, respectively.
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning

In representation learning, a disentangled representation is highly desirable as it encodes generative factors of data in a separable and compact pattern. Researchers have advocated leveraging disentangled representations to complete downstream tasks with encouraging empirical evidence. This paper further investigates the necessity of disentangled representation in downstream applications. Specifically, we show that dimension-wise disentangled representations are unnecessary on a fundamental downstream task, abstract visual reasoning. We provide extensive empirical evidence against the necessity of disentanglement, covering multiple datasets, representation learning methods, and downstream network architectures. Furthermore, our findings suggest that the informativeness of representations is a better indicator of downstream performance than disentanglement. Finally, the positive correlation between informativeness and disentanglement explains the claimed usefulness of disentangled representations in previous works. The source code is available at https://github.com/Richard-coder-Nai/disentanglement-lib-necessity.git
Analyzing Divergence for Nondeterministic Probabilistic Models

Branching and weak probabilistic bisimilarities are two well-known notions capturing behavioral equivalence between nondeterministic probabilistic systems. For probabilistic systems, divergence is of major concern. Recently several divergence-sensitive refinements of branching and weak probabilistic bisimilarities have been proposed in the literature. Both the definitions of these equivalences and the techniques to investigate them differ significantly. This paper presents a comprehensive comparative study on divergence-sensitive behavioral equivalence relations that refine the branching and weak probabilistic bisimilarities. Additionally, these equivalence relations are shown to have efficient checking algorithms. The techniques of this paper might be of independent interest in a more general setting.
Never-Ending Embodied Robot Learning

Relying on large language models (LLMs), embodied robots could perform complex multimodal robot manipulation tasks from visual observations with powerful generalization ability. However, most visual behavior-cloning agents suffer from manipulation performance degradation and skill knowledge forgetting when adapting into a series of challenging unseen tasks. We here investigate the above challenge with NBCagent in embodied robots, a pioneering language-conditioned Never-ending Behavior-Cloning agent, which can continually learn observation knowledge of novel robot manipulation skills from skill-specific and skill-shared attributes. Specifically, we establish a skill-specific evolving planner to perform knowledge decoupling, which can continually embed novel skill-specific knowledge in our NBCagent agent from latent and low-rank space. Meanwhile, we propose a skill-shared semantics rendering module and a skill-shared representation distillation module to effectively transfer anti-forgetting skill-shared knowledge, further tackling catastrophic forgetting on old skills from semantics and representation aspects. Finally, we design a continual embodied robot manipulation benchmark, and several expensive experiments demonstrate the significant performance of our method. Visual results, code, and dataset are provided at: https://neragent.github. io.
Yodel: A Layer 3.5 Name-Based Multicast Network Architecture For The Future Internet

Multicasting refers to the ability of transmitting data to multiple recipients without data sources needing to provide more than one copy of the data to the network. The network takes responsibility to route and deliver a copy of each data to every intended recipient. Multicasting has the potential to improve the network efficiency and performance (e.g., throughput and latency) through transferring fewer bits in communicating the same data to multiple recipients compared with unicast transmissions, reduce the amount of networking resources needed for communication, lower the network energy footprint, and alleviate the occurrence of congestion in the network. Over the past few decades, providing multicast services has been a real challenge for ISPs, especially to support home users and multi-domain network applications, leading to the emergence of complex application-level solutions. These solutions like Content Delivery and Peer-to-Peer networks take advantage of complex caching, routing, transport, and topology management systems which put heavy strains on the underlying Internet infrastructures to offer multicasting services. In reality, the main motivation behind the design of these systems is rather sharing content than offering efficient multicast services. In this paper, we propound Yodel, a name-based multicast network architecture that can provide multi-domain multicast services for current and future Internet applications. Compared to the wider array of other name-based network architectures with clean-slate infrastructure requirements, Yodel is designed to provide multicast services over the current Internet infrastructure. Hence, Yodel puts forward several design goals that distinguish it from other name-based network architectures with inherent multicast capabilities. This paper is prepared to discuss the Yodel architecture, its design goals, and architectural functions.
Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale

We study reinforcement learning for global decision-making in the presence of many local agents, where the global decision-maker makes decisions affecting all local agents, and the objective is to learn a policy that maximizes the rewards of both the global and the local agents. Such problems find many applications, e.g. demand response, EV charging, queueing, etc. In this setting, scalability has been a long-standing challenge due to the size of the state/action space which can be exponential in the number of agents. This work proposes the SUB-SAMPLE-Q algorithm where the global agent subsamples $k\leq n$ local agents to compute an optimal policy in time that is only exponential in $k$, providing an exponential speedup from standard methods that are exponential in $n$. We show that the learned policy converges to the optimal policy in the order of $\tilde{O}(1/\sqrt{k}+\epsilon_{k,m})$ as the number of sub-sampled agents $k$ increases, where $\epsilon_{k,m}$ is the Bellman noise. We also conduct numerical simulations in a demand-response setting and a queueing setting.
Hierarchical Indexing for Retrieval-Augmented Opinion Summarization

We propose a method for unsupervised abstractive opinion summarization, that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs). Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews. Then, we use a pretrained LLM to generate a readable summary that is grounded in these extracted evidential clusters. The modularity of our approach allows us to evaluate its efficacy at each stage. We show that HIRO learns an encoding space that is more semantically structured than prior work, and generates summaries that are more representative of the opinions in the input reviews. Human evaluation confirms that HIRO generates more coherent, detailed and accurate summaries that are significantly preferred by annotators compared to prior work.
A foreground-marginalized 'BK-lite' likelihood for the tensor-to-scalar ratio

The current limit on the tensor-to-scalar ratio from the BICEP/Keck Collaboration (with r<0.036 at 95% confidence) puts pressure on early universe models, with less than 10% of the error on r attributed to uncertainty in Galactic foregrounds. We use the BICEP/Keck BK18 public multi-frequency likelihood to test some further assumptions made in the foreground modeling, finding little impact on the estimate for r. We then estimate foreground-marginalized cosmic microwave background (CMB) B-mode polarization bandpowers. We fit them with a multivariate offset-lognormal distribution and construct a marginalized 'BK-lite' likelihood for the CMB B-mode spectrum with no nuisance parameters, serving as a method demonstration for future analyses of small sky regions, for example from the South Pole Observatory or CMB-S4.
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behavior and Adversarial Style Sampling for Assistive Tasks

Autonomous assistance of people with motor impairments is one of the most promising applications of autonomous robotic systems. Recent studies have reported encouraging results using deep reinforcement learning (RL) in the healthcare domain. Previous studies showed that assistive tasks can be formulated as multi-agent RL, wherein there are two agents: a caregiver and a care-receiver. However, policies trained in multi-agent RL are often sensitive to the policies of other agents. In such a case, a trained caregiver's policy may not work for different care-receivers. To alleviate this issue, we propose a framework that learns a robust caregiver's policy by training it for diverse care-receiver responses. In our framework, diverse care-receiver responses are autonomously learned through trials and errors. In addition, to robustify the care-giver's policy, we propose a strategy for sampling a care-receiver's response in an adversarial manner during the training. We evaluated the proposed method using tasks in an Assistive Gym. We demonstrate that policies trained with a popular deep RL method are vulnerable to changes in policies of other agents and that the proposed framework improves the robustness against such changes.
PROC2PDDL: Open-Domain Planning Representations from Texts

Planning in a text-based environment continues to be a major challenge for AI systems. Recent approaches have used language models to predict a planning domain definition (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL , the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. Using this dataset, we evaluate state-of-the-art models on defining the preconditions and effects of actions. We show that Proc2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%. Our analysis shows both syntactic and semantic errors, indicating LMs' deficiency in both generating domain-specific prgorams and reasoning about events. We hope this analysis and dataset helps future progress towards integrating the best of LMs and formal planning.
A Survey of Route Recommendations: Methods, Applications, and Opportunities

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens` travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: 1) Methodology-wise. We categorize a large volume of traditional machine learning and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. 2) Application\-wise. We present numerous novel applications related to route commendation within urban computing scenarios. 3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends.
Marginal Fermi liquid behavior at the onset of $\boldsymbol{2k_F}$ density wave order in two-dimensional metals with flat hot spots

We analyze quantum fluctuation effects at the onset of incommensurate $2k_F$ charge- or spin-density wave order in two-dimensional metals, for a model where the ordering wave vector $\boldsymbol{Q}$ connects a single pair of hot spots on the Fermi surface with a vanishing Fermi surface curvature. The tangential momentum dependence of the bare dispersion near the hot spots is proportional to $|k_t|^\alpha$ with $\alpha>2$. We first compute the order parameter susceptibility and the fermion self-energy in random phase approximation (RPA). Logarithmic divergences are subsequently treated by a renormalization group analysis. The coupling between the order parameter fluctuations and the fermions vanishes logarithmically in the low-energy limit. As a consequence, the logarithmic divergences found in RPA do not sum up to anomalous power laws. Instead, only logarithmic corrections to Fermi liquid behavior are obtained. In particular, the quasiparticle weight and the Fermi velocity vanish logarithmically at the hot spots.
On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations

There is substantial interest in the use of machine learning (ML)-based techniques throughout the electronic computer-aided design (CAD) flow, particularly methods based on deep learning. However, while deep learning methods have achieved state-of-the-art performance in several applications, recent work has demonstrated that neural networks are generally vulnerable to small, carefully chosen perturbations of their input (e.g. a single pixel change in an image). In this work, we investigate robustness in the context of ML-based EDA tools -- particularly for congestion prediction. As far as we are aware, we are the first to explore this concept in the context of ML-based EDA. We first describe a novel notion of imperceptibility designed specifically for VLSI layout problems defined on netlists and cell placements. Our definition of imperceptibility is characterized by a guarantee that a perturbation to a layout will not alter its global routing. We then demonstrate that state-of-the-art CNN and GNN-based congestion models exhibit brittleness to imperceptible perturbations. Namely, we show that when a small number of cells (e.g. 1%-5% of cells) have their positions shifted such that a measure of global congestion is guaranteed to remain unaffected (e.g. 1% of the design adversarially shifted by 0.001% of the layout space results in a predicted decrease in congestion of up to 90%, while no change in congestion is implied by the perturbation). In other words, the quality of a predictor can be made arbitrarily poor (i.e. can be made to predict that a design is"congestion-free") for an arbitrary input layout. Next, we describe a simple technique to train predictors that improves robustness to these perturbations. Our work indicates that CAD engineers should be cautious when integrating neural network-based mechanisms in EDA flows to ensure robust and high-quality results.
Extending the paleogeographic range and our understanding of the Neoarchean Monteville impact spherule layer (Transvaal Supergroup, South Africa)

The Monteville spherule layer (MSL) was deposited in the Griqualand West Basin (GWB) on the Kaapvaal Craton approximately 2.63 Ga. The spherules were generated by a large impact and reworked by impact‐generated waves and/or currents. The MSL has been intersected in three previously undescribed cores. Two of the cores, GKF‐1 and GKP‐1, were drilled ~30 km west of the southernmost outcrop of the MSL. The third core, BH‐47, was drilled ~250 km south and east of the GWB. The MSL contains medium to coarse sand‐size spherules like those described previously in all three cores but each one was emplaced in a different way. In GKF‐1, the MSL is 90 cm thick and contains large rip‐up clasts of basinal carbonate and early diagenetic pyrite. In GKP‐1, the MSL is only 1.5 cm thick and consists largely of fine carbonate sand, yet it contains pyrite intraclasts up to ~1 cm long. In BH‐47, the MSL consists of a lower coarse sandy zone ~37 cm thick rich in spherules, carbonate peloids/ooids, pyrite intraclasts, and quartzose sand and an upper, finer sandy zone ~46 cm thick; neither zone contains any large intraclasts. The new occurrences triple the known extent of the MSL from ~15,000 to ~46,000 km2, support the oceanic impact model for the formation of the MSL, demonstrate that it is a persistent regional time‐stratigraphic marker, place new constraints on the Kaapvaal paleoshoreline at the time of impact, and support the existence of Vaalbara.