Miracle-dz/UniFAR-FTUs huggingface dataset:
NEDS-SLAM: A Novel Neural Explicit Dense Semantic SLAM Framework using 3D Gaussian Splatting

We propose NEDS-SLAM, an Explicit Dense semantic SLAM system based on 3D Gaussian representation, that enables robust 3D semantic mapping, accurate camera tracking, and high-quality rendering in real-time. In the system, we propose a Spatially Consistent Feature Fusion model to reduce the effect of erroneous estimates from pre-trained segmentation head on semantic reconstruction, achieving robust 3D semantic Gaussian mapping. Additionally, we employ a lightweight encoder-decoder to compress the high-dimensional semantic features into a compact 3D Gaussian representation, mitigating the burden of excessive memory consumption. Furthermore, we leverage the advantage of 3D Gaussian splatting, which enables efficient and differentiable novel view rendering, and propose a Virtual Camera View Pruning method to eliminate outlier GS points, thereby effectively enhancing the quality of scene representations. Our NEDS-SLAM method demonstrates competitive performance over existing dense semantic SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets, while also showing excellent capabilities in 3D dense semantic mapping.
Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units

This paper introduces a groundbreaking digital neuromorphic architecture that innovatively integrates Brain Code Unit (BCU) and Fundamental Code Unit (FCU) using mixedsignal design methodologies. Leveraging open-source datasets and the latest advances in materials science, our research focuses on enhancing the computational efficiency, accuracy, and adaptability of neuromorphic systems. The core of our approach lies in harmonizing the precision and scalability of digital systems with the robustness and energy efficiency of analog processing. Through experimentation, we demonstrate the effectiveness of our system across various metrics. The BCU achieved an accuracy of 88.0% and a power efficiency of 20.0 GOP/s/W, while the FCU recorded an accuracy of 86.5% and a power efficiency of 18.5 GOP/s/W. Our mixed-signal design approach significantly improved latency and throughput, achieving a latency as low as 0.75 ms and throughput up to 213 TOP/s. These results firmly establish the potential of our architecture in neuromorphic computing, providing a solid foundation for future developments in this domain. Our study underscores the feasibility of mixedsignal neuromorphic systems and their promise in advancing the field, particularly in applications requiring high efficiency and adaptability
Optimizing 1-Nearest Prototype Classifiers

The development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision. However, the trend towards large scale datasets revived the interest in simpler classifiers to reduce runtime. Simple nearest neighbor classifiers have several beneficial properties, such as low complexity and inherent multi-class handling, however, they have a runtime linear in the size of the database. Recent related work represents data samples by assigning them to a set of prototypes that partition the input feature space and afterwards applies linear classifiers on top of this representation to approximate decision boundaries locally linear. In this paper, we go a step beyond these approaches and purely focus on 1-nearest prototype classification, where we propose a novel algorithm for deriving optimal prototypes in a discriminative manner from the training samples. Our method is implicitly multi-class capable, parameter free, avoids noise over fitting and, since during testing only comparisons to the derived prototypes are required, highly efficient. Experiments demonstrate that we are able to outperform related locally linear methods, while even getting close to the results of more complex classifiers.
Non-negative Contrastive Learning

Deep representations have shown promising performance when transferred to downstream tasks in a black-box manner. Yet, their inherent lack of interpretability remains a significant challenge, as these features are often opaque to human understanding. In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Factorization (NMF) aimed at deriving interpretable features. The power of NCL lies in its enforcement of non-negativity constraints on features, reminiscent of NMF's capability to extract features that align closely with sample clusters. NCL not only aligns mathematically well with an NMF objective but also preserves NMF's interpretability attributes, resulting in a more sparse and disentangled representation compared to standard contrastive learning (CL). Theoretically, we establish guarantees on the identifiability and downstream generalization of NCL. Empirically, we show that these advantages enable NCL to outperform CL significantly on feature disentanglement, feature selection, as well as downstream classification tasks. At last, we show that NCL can be easily extended to other learning scenarios and benefit supervised learning as well. Code is available at https://github.com/PKU-ML/non_neg.
Prompt-based Graph Model for Joint Liberal Event Extraction and Event Schema Induction

Events are essential components of speech and texts, describing the changes in the state of entities. The event extraction task aims to identify and classify events and find their participants according to event schemas. Manually predefined event schemas have limited coverage and are hard to migrate across domains. Therefore, the researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously. However, existing LEE models rely heavily on external language knowledge bases and require the manual development of numerous rules for noise removal and knowledge alignment, which is complex and laborious. To this end, we propose a Prompt-based Graph Model for Liberal Event Extraction (PGLEE). Specifically, we use a prompt-based model to obtain candidate triggers and arguments, and then build heterogeneous event graphs to encode the structures within and between events. Experimental results prove that our approach achieves excellent performance with or without predefined event schemas, while the automatically detected event schemas are proven high quality.
Empirical Analysis on CI/CD Pipeline Evolution in Machine Learning Projects

The growing popularity of machine learning (ML) and the integration of ML components with other software artifacts has led to the use of continuous integration and delivery (CI/CD) tools, such as Travis CI, GitHub Actions, etc. that enable faster integration and testing for ML projects. Such CI/CD configurations and services require synchronization during the life cycle of the projects. Several works discussed how CI/CD configuration and services change during their usage in traditional software systems. However, there is very limited knowledge of how CI/CD configuration and services change in ML projects. To fill this knowledge gap, this work presents the first empirical analysis of how CI/CD configuration evolves for ML software systems. We manually analyzed 343 commits collected from 508 open-source ML projects to identify common CI/CD configuration change categories in ML projects and devised a taxonomy of 14 co-changes in CI/CD and ML components. Moreover, we developed a CI/CD configuration change clustering tool that identified frequent CI/CD configuration change patterns in 15,634 commits. Furthermore, we measured the expertise of ML developers who modify CI/CD configurations. Based on this analysis, we found that 61.8% of commits include a change to the build policy and minimal changes related to performance and maintainability compared to general open-source projects. Additionally, the co-evolution analysis identified that CI/CD configurations, in many cases, changed unnecessarily due to bad practices such as the direct inclusion of dependencies and a lack of usage of standardized testing frameworks. More practices were found through the change patterns analysis consisting of using deprecated settings and reliance on a generic build language. Finally, our developer's expertise analysis suggests that experienced developers are more inclined to modify CI/CD configurations.
vTZ: Virtualizing ARM TrustZone

ARM TrustZone, a security extension that provides a secure world, a trusted execution environment (TEE), to run security-sensitive code, has been widely adopted in mobile platforms. With the increasing momentum of ARM64 being adopted in server markets like cloud, it is likely to see TrustZone being adopted as a key pillar for cloud security. Unfortunately, TrustZone is not designed to be virtualizable as there is only one TEE provided by the hardware, which prevents it from being securely shared by multiple virtual machines (VMs). This paper conducts a study on variable approaches to virtualizing TrustZone in virtualized environments and then presents vTZ, a solution that securely provides each guest VM with a virtualized guest TEE using existing hardware. vTZ leverages the idea of separating functionality from protection by maintaining a secure co-running VM to serve as a guest TEE, while using the hardware TrustZone to enforce strong isolation among guest TEEs and the untrusted hypervisor. Specifically, vTZ uses a tiny monitor running within the physical TrustZone that securely interposes and virtualizes memory mapping and world switching. vTZ further leverages a few pieces of protected, self-contained code running in a Constrained Isolated Execution Environment (CIEE) to provide secure virtualization and isolation among multiple guest TEEs. We have implemented vTZ on Xen 4.8 on both ARMv7 and ARMv8 development boards. Evaluation using two common TEE-kernels (secure kernel running in TEE) such as seL4 1 and OP-TEE shows that vTZ provides strong security with small performance overhead.
Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning

The utilization of social media in epidemic surveillance has been well established. Nonetheless, bias is often introduced when pre-defined lexicons are used to retrieve relevant corpus. This study introduces a framework aimed at curating extensive dictionaries of medical colloquialisms and Unified Medical Language System (UMLS) concepts. The framework comprises three modules: a BERT-based Named Entity Recognition (NER) model that identifies medical entities from social media content, a deep-learning powered normalization module that standardizes the extracted entities, and a semi-supervised clustering module that assigns the most probable UMLS concept to each standardized entity. We applied this framework to COVID-19-related tweets from February 1, 2020, to April 30, 2022, generating a symptom dictionary (available at https://github.com/ningkko/UMLS_colloquialism/) composed of 9,249 standardized entities mapped to 876 UMLS concepts and 38,175 colloquial expressions. This framework demonstrates encouraging potential in addressing the constraints of keyword matching information retrieval in social media-based public health research.
Reference-based Metrics Disprove Themselves in Question Generation

Reference-based metrics such as BLEU and BERTScore are widely used to evaluate question generation (QG). In this study, on QG benchmarks such as SQuAD and HotpotQA, we find that using human-written references cannot guarantee the effectiveness of the reference-based metrics. Most QG benchmarks have only one reference; we replicated the annotation process and collect another reference. A good metric was expected to grade a human-validated question no worse than generated questions. However, the results of reference-based metrics on our newly collected reference disproved the metrics themselves. We propose a reference-free metric consisted of multi-dimensional criteria such as naturalness, answerability, and complexity, utilizing large language models. These criteria are not constrained to the syntactic or semantic of a single reference question, and the metric does not require a diverse set of references. Experiments reveal that our metric accurately distinguishes between high-quality questions and flawed ones, and achieves state-of-the-art alignment with human judgment.
Beyond Beats: A Recipe to Song Popularity? A machine learning approach

Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least Squares (OLS) regression analysis reveals genre as the primary influencer of popularity, with notable trends over time. MARS modelling highlights the complex relationship between variables, particularly with features like instrumentalness and duration. Random Forest and XGBoost models underscore the importance of genre, especially EDM, in predicting popularity. Despite variations in performance, Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores. Despite the importance of genre, predicting song popularity remains challenging, as observed variations in music-related features suggest complex interactions between genre and other factors. Consequently, while certain characteristics like loudness and song duration may impact popularity scores, accurately predicting song success remains elusive.
TON-VIO: Online Time Offset Modeling Networks for Robust Temporal Alignment in High Dynamic Motion VIO

Temporal misalignment (time offset) between sensors is common in low cost visual-inertial odometry (VIO) systems. Such temporal misalignment introduces inconsistent constraints for state estimation, leading to a significant positioning drift especially in high dynamic motion scenarios. In this article, we focus on online temporal calibration to reduce the positioning drift caused by the time offset for high dynamic motion VIO. For the time offset observation model, most existing methods rely on accurate state estimation or stable visual tracking. For the prediction model, current methods oversimplify the time offset as a constant value with white Gaussian noise. However, these ideal conditions are seldom satisfied in real high dynamic scenarios, resulting in the poor performance. In this paper, we introduce online time offset modeling networks (TON) to enhance real-time temporal calibration. TON improves the accuracy of time offset observation and prediction modeling. Specifically, for observation modeling, we propose feature velocity observation networks to enhance velocity computation for features in unstable visual tracking conditions. For prediction modeling, we present time offset prediction networks to learn its evolution pattern. To highlight the effectiveness of our method, we integrate the proposed TON into both optimization-based and filter-based VIO systems. Simulation and real-world experiments are conducted to demonstrate the enhanced performance of our approach. Additionally, to contribute to the VIO community, we will open-source the code of our method on: https://github.com/Franky-X/FVON-TPN.
Tangent space generators of matrix product states and exact Floquet quantum scars

The advancement of quantum simulators motivates the development of a theoretical framework to assist with efficient state preparation in quantum many-body systems. Generally, preparing a target entangled state via unitary evolution with time-dependent couplings is a challenging task and very little is known about the existence of solutions and their properties. In this work we develop a constructive approach for preparing matrix product states (MPS) via continuous unitary evolution. We provide an explicit construction of the operator which exactly implements the evolution of a given MPS along a specified direction in its tangent space. This operator can be written as a sum of local terms of finite range, yet it is in general non-Hermitian. Relying on the explicit construction of the non-Hermitian generator of the dynamics, we demonstrate the existence of a Hermitian sequence of operators that implements the desired MPS evolution with the error which decreases exponentially with the operator range. The construction is benchmarked on an explicit periodic trajectory in a translationally invariant MPS manifold. We demonstrate that the Floquet unitary generating the dynamics over one period of the trajectory features an approximate MPS-like eigenstate embedded among a sea of thermalizing eigenstates. These results show that our construction is useful not only for state preparation and control of many-body systems, but also provides a generic route towards Floquet scars -- periodically driven models with quasi-local generators of dynamics that have exact MPS eigenstates in their spectrum.
Object Segmentation-Assisted Inter Prediction for Versatile Video Coding

In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to compactly represent. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrary shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively.
InBox: Recommendation with Knowledge Graph using Interest Box Embedding

Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim to identify user interests based on historical interactions and recommend suitable items. However, existing works overlook two key challenges: (1) an interest corresponds to a potentially large set of related items, and (2) the lack of explicit, fine-grained exploitation of KG information and interest connectivity. This leads to an inability to reflect distinctions between entities and interests when modeling them in a single way. Additionally, the granularity of concepts in the knowledge graphs used for recommendations tends to be coarse, failing to match the fine-grained nature of user interests. This homogenization limits the precise exploitation of knowledge graph data and interest connectivity. To address these limitations, we introduce a novel embedding-based model called InBox. Specifically, various knowledge graph entities and relations are embedded as points or boxes, while user interests are modeled as boxes encompassing interaction history. Representing interests as boxes enables containing collections of item points related to that interest. We further propose that an interest comprises diverse basic concepts, and box intersection naturally supports concept combination. Across three training steps, InBox significantly outperforms state-of-the-art methods like HAKG and KGIN on recommendation tasks. Further analysis provides meaningful insights into the variable value of different KG data for recommendations. In summary, InBox advances recommender systems through box-based interest and concept modeling for sophisticated knowledge graph exploitation.
Vibrational Polaritons with Broken In-Plane Translational Symmetry

For the calculation of polariton dispersion relations in planar Fabry-P\'erot microcavities, the single-mode approximation is usually applied. This approximation becomes invalid when the molecular distribution along the cavity mirror plane breaks the in-plane translational symmetry. Herein, both perturbative theory and numerical calculations have been performed to study polariton dispersion relations with various in-plane molecular distribution patterns under vibrational strong coupling conditions. If a homogeneous in-plane molecular distribution is modulated by sinusoidal fluctuations, in addition to a pair of upper and lower polariton branches, a discrete number of side polariton branches may emerge in the polariton dispersion relation. Moreover, for a Gaussian molecular density distribution, only two, yet significantly broadened polariton branches exist in the spectra. This polariton linewidth broadening is due to the breakdown of the single-mode approximation and the scattering between cavity modes at different in-plane frequencies, which is distinguished from known causes of polariton broadening such as the homogeneous broadening of molecules and the cavity loss. Associated with the broadened polariton branches, under the Gaussian in-plane inhomogeneity, a significant amount of the VSC eigenstates contain a non-zero contribution from the cavity photon mode at zero in-plane frequency, blurring the distinction between the bright and the dark modes. Looking forward, our theoretical investigation should facilitate the experimental exploration of vibrational polaritons with patterned in-plane molecular density distributions.
THE QUANTITATIVE DETERMINATION OF INFLUENZA VIRUS AND ANTIBODIES BY MEANS OF RED CELL AGGLUTINATION

1. The agglutination titer for chicken red cells of freshly prepared or carefully stored suspensions of PR8 influenza virus, that is to say virus of maximum pathogenicity, was found to be proportional to the mouse lethal titer of the same preparations. 2. The agglutination titer of infected allantoic fluid procured in a standard way is relatively constant, regardless of the influenza strain used and its pathogenicity for mice. 3. Virus preparations inactivated by heat or storage may retain their agglutinating power. 4. Certain animal sera contain a partially heat-labile factor which, in low dilution, inhibits the agglutination of chicken red cells by influenza A and influenza B viruses. 5. The agglutination inhibition test, using ferret and human sera, gives qualitative data regarding influenza antibodies which are similar to the information obtained on the same sera by means of the virus neutralization test. 6. There is a definite relationship between the agglutination inhibition titer and the virus neutralization titer of a serum. On a logarithmic scale of both variables, this relationship is essentially linear within the range investigated. 7. The agglutination inhibition titer of immune ferret serum is inversely proportional to the amount of virus used in the test.