Miracle-dz/UniFAR-FTUs huggingface dataset:
A lightweight 3D dense facial landmark estimation model from position map data

The incorporation of 3D data in facial analysis tasks has gained popularity in recent years. Though it provides a more accurate and detailed representation of the human face, accruing 3D face data is more complex and expensive than 2D face images. Either one has to rely on expensive 3D scanners or depth sensors which are prone to noise. An alternative option is the reconstruction of 3D faces from uncalibrated 2D images in an unsupervised way without any ground truth 3D data. However, such approaches are computationally expensive and the learned model size is not suitable for mobile or other edge device applications. Predicting dense 3D landmarks over the whole face can overcome this issue. As there is no public dataset available containing dense landmarks, we propose a pipeline to create a dense keypoint training dataset containing 520 key points across the whole face from an existing facial position map data. We train a lightweight MobileNet-based regressor model with the generated data. As we do not have access to any evaluation dataset with dense landmarks in it we evaluate our model against the 68 keypoint detection task. Experimental results show that our trained model outperforms many of the existing methods in spite of its lower model size and minimal computational cost. Also, the qualitative evaluation shows the efficiency of our trained models in extreme head pose angles as well as other facial variations and occlusions.
Probing the global 21-cm signal via the integrated Sachs-Wolfe effect on the 21-cm background

We propose a novel method to probe the global 21-cm background. This background experiences the integrated Sachs-Wolfe effect (ISW) as the cosmic microwave background does. The 21-cm ISW is modulated by the spectral shape of the global 21-cm signal, and thus the measure of the 21-cm ISW will be a probe of the evolution of the global signal. This strategy naturally mitigates the impact of the Milky Way foreground, which is a common and most significant challenge in conventional 21-cm background probes. With the phase-1 SKA telescope, probing the global 21-cm background would be feasible with a few 1000 hours of observation, enabling consistency checks with existing measures of the global 21-cm signal by EDGES and SARAS that are conflicting with each other.
C2G2: Controllable Co-speech Gesture Generation with Latent Diffusion Model

Co-speech gesture generation is crucial for automatic digital avatar animation. However, existing methods suffer from issues such as unstable training and temporal inconsistency, particularly in generating high-fidelity and comprehensive gestures. Additionally, these methods lack effective control over speaker identity and temporal editing of the generated gestures. Focusing on capturing temporal latent information and applying practical controlling, we propose a Controllable Co-speech Gesture Generation framework, named C2G2. Specifically, we propose a two-stage temporal dependency enhancement strategy motivated by latent diffusion models. We further introduce two key features to C2G2, namely a speaker-specific decoder to generate speaker-related real-length skeletons and a repainting strategy for flexible gesture generation/editing. Extensive experiments on benchmark gesture datasets verify the effectiveness of our proposed C2G2 compared with several state-of-the-art baselines. The link of the project demo page can be found at https://c2g2-gesture.github. io/c2_gesture
Comprehensive Interactome Analysis Reveals that STT3B is Required for the N-Glycosylation of Lassa Virus Glycoprotein

Comprehensive Interactome Analysis Reveals that STT3B is Required for 1 the N-Glycosylation of Lassa Virus Glycoprotein 2 3 Shenglin Zhu, Weiwei Wan, Yanjun Zhang, Weijuan Shang, Xiaoyan 4 Pan, Lei-Ke Zhang,# Gengfu Xiao# 5 6 1 State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese 7 Academy of Sciences, Wuhan, China 8 2 University of Chinese Academy of Sciences, Beijing, China 9 10 Running title: STT3B-dependent N-glycosylation of LASV glycoprotein 11 12 # Address correspondence to Lei-Ke Zhang, zhangleike@wh.iov. cn, and 13 Gengfu Xiao, xiaogf@wh.iov. cn 14 15
Stochastic Graph Bandit Learning with Side-Observations

In this paper, we investigate the stochastic contextual bandit with general function space and graph feedback. We propose an algorithm that addresses this problem by adapting to both the underlying graph structures and reward gaps. To the best of our knowledge, our algorithm is the first to provide a gap-dependent upper bound in this stochastic setting, bridging the research gap left by the work in [35]. In comparison to [31,33,35], our method offers improved regret upper bounds and does not require knowledge of graphical quantities. We conduct numerical experiments to demonstrate the computational efficiency and effectiveness of our approach in terms of regret upper bounds. These findings highlight the significance of our algorithm in advancing the field of stochastic contextual bandits with graph feedback, opening up avenues for practical applications in various domains.
An Efficient Construction of Yao-Graph in Data-Distributed Settings

A sparse graph that preserves an approximation of the shortest paths between all pairs of points in a plane is called a geometric spanner. Using range trees of sublinear size, we design an algorithm in massively parallel computation (MPC) model for constructing a geometric spanner known as Yao-graph. This improves the total time and the total memory of existing algorithms for geometric spanners from subquadratic to near-linear.
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning

Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers' activities. HAR has many applications in human-robot interaction research to enable robots' understanding of human counterparts' activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner which is crucial for the widespread deployment of collaborative robots in construction.
CAPS: A Practical Partition Index for Filtered Similarity Search

With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest. While the community has recently proposed several algorithms for constrained ANNS, almost all of these methods focus on integration with graph-based indexes, the predominant class of algorithms achieving state-of-the-art performance in latency-recall tradeoffs. In this work, we take a different approach and focus on developing a constrained ANNS algorithm via space partitioning as opposed to graphs. To that end, we introduce Constrained Approximate Partitioned Search (CAPS), an index for ANNS with filters via space partitions that not only retains the benefits of a partition-based algorithm but also outperforms state-of-the-art graph-based constrained search techniques in recall-latency tradeoffs, with only 10% of the index size.
Optometrist’s Algorithm for Personalizing Robot-Human Handovers

With an increasing interest in human-robot collaboration, there is a need to develop robot behavior while keeping the human user’s preferences in mind. Highly skilled human users doing delicate tasks require their robot partners to behave according to their work habits and task constraints. To achieve this, we present the use of the Optometrist’s Algorithm (OA) to interactively and intuitively personalize robot-human handovers. Using this algorithm, we tune controller parameters for speed, location, and effort. We study the differences in the fluency of the handovers before and after tuning and the subjective perception of this process in a study of N = 30 non-expert users of mixed background – evaluating the OA. The users evaluate the interaction on trust, safety, and workload scales, amongst other measures. They assess our tuning process to be engaging and easy to use. Personalization leads to an increase in the fluency of the interaction. Our participants utilize the wide range of parameters ending up with their unique personalized handover.
High-order quasi-Helmholtz Projectors: Definition, Analyses, Algorithms

The accuracy of the electric field integral equation (EFIE) can be substantially improved using high-order discretizations. However, this equation suffers from ill-conditioning and deleterious numerical effects in the low-frequency regime, often jeopardizing its solution. This can be fixed using quasi-Helmholtz decompositions, in which the source and testing elements are separated into their solenoidal and non-solenoidal contributions, then rescaled in order to avoid both the low-frequency conditioning breakdown and the loss of numerical accuracy. However, standard quasi-Helmholtz decompositions require handling discretized differential operators that often worsen the mesh-refinement ill-conditioning and require the finding of the topological cycles of the geometry, which can be expensive when modeling complex scatterers, especially in high-order. This paper solves these drawbacks by presenting the first extension of the quasi-Helmholtz projectors to high-order discretizations and their application to the stabilization of the EFIE when discretized with high-order basis functions. Our strategy will not require the identification of the cycles and will provide constant condition numbers for decreasing frequencies. Theoretical considerations will be accompanied by numerical results showing the effectiveness of our method in complex scenarios.
Symbolic LTLf Best-Effort Synthesis

We consider an agent acting to fulfil tasks in a nondeterministic environment. When a strategy that fulfills the task regardless of how the environment acts does not exist, the agent should at least avoid adopting strategies that prevent from fulfilling its task. Best-effort synthesis captures this intuition. In this paper, we devise and compare various symbolic approaches for best-effort synthesis in Linear Temporal Logic on finite traces (LTLf). These approaches are based on the same basic components, however they change in how these components are combined, and this has a significant impact on the performance of the approaches as confirmed by our empirical evaluations.
Particle-field duality in QFT measurements

Pointlike systems coupled to quantum fields are often employed as toy models for measurements in quantum field theory. In this paper, we identify the field observables recorded by such models. We show that in models that work in the strong coupling regime, the apparatus is correlated with smeared field amplitudes, while in models that work in weak coupling the apparatus records particle aspects of the field, such as the existence of a particle-like time of arrival and resonant absorption. Then, we develop an improved field-detector interaction model, adapting the formalism of Quantum Brownian motion, that is exactly solvable. This model confirms the association of field and particle properties in the strong and weak coupling regimes, respectively. Further, it can also describe the intermediate regime, in which the field-particle characteristics `merge'. In contrast to standard perturbation techniques, this model also recovers the relativistic Breit-Wigner resonant behavior in the weak coupling regime. The modulation of field-particle-duality by a single tunable parameter is a novel feature that is, in principle, experimentally accessible.
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams

How to get insights from relational data streams in a timely manner is a hot research topic. Data streams can present unique challenges, such as distribution drifts, outliers, emerging classes, and changing features, which have recently been described as open environment challenges for machine learning. While existing studies have been done on incremental learning for data streams, their evaluations are mostly conducted with synthetic datasets. Thus, a natural question is how those open environment challenges look like and how existing incremental learning algorithms perform on real-world relational data streams. To fill this gap, we develop an Open Environment Benchmark named OEBench to evaluate open environment challenges in real-world relational data streams. Specifically, we investigate 55 real-world relational data streams and establish that open environment scenarios are indeed widespread, which presents significant challenges for stream learning algorithms. Through benchmarks with existing incremental learning algorithms, we find that increased data quantity may not consistently enhance the model accuracy when applied in open environment scenarios, where machine learning models can be significantly compromised by missing values, distribution drifts, or anomalies in real-world data streams. The current techniques are insufficient in effectively mitigating these challenges brought by open environments. More researches are needed to address real-world open environment challenges. All datasets and code are open-sourced in https://github.com/sjtudyq/OEBench.
Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing

Face parsing infers a pixel-wise label map for each semantic facial component. Previous methods generally work well for uncovered faces, however overlook the facial occlusion and ignore some contextual area outside a single face, especially when facial occlusion has become a common situation during the COVID-19 epidemic. Inspired by the illumination theory of image, we propose a novel homogeneous tanh-transforms for image preprocessing, which made up of four tanh-transforms, that fuse the central vision and the peripheral vision together. Our proposed method addresses the dilemma of face parsing under occlusion and compresses more information of surrounding context. Based on homogeneous tanh-transforms, we propose an occlusion-aware convolutional neural network for occluded face parsing. It combines the information both in Tanh-polar space and Tanh-Cartesian space, capable of enhancing receptive fields. Furthermore, we introduce an occlusion-aware loss to focus on the boundaries of occluded regions. The network is simple and flexible, and can be trained end-to-end. To facilitate future research of occluded face parsing, we also contribute a new cleaned face parsing dataset, which is manually purified from several academic or industrial datasets, including CelebAMask-HQ, Short-video Face Parsing as well as Helen dataset and will make it public. Experiments demonstrate that our method surpasses state-of-art methods of face parsing under occlusion.
Embracing Sex-specific Differences in Engineered Kidney Models for Enhanced Biological Understanding

In vitro models play a crucial role in advancing our understanding of biological processes, disease mechanisms, and developing screening platforms for drug discovery. Kidneys play an instrumental role in transport and elimination of drugs and toxins. However, despite the well-established patient-to-patient differences in kidney function and disease manifestation, progression and prognostic, few studies take this variability into consideration. In particular, the discrepancies between female and male biology warrants a better representation within kidney in vitro models. The omission of sex as a biological variable poses the significant risk of overlooking sex-specific mechanisms in health and disease and potential differences in drug efficacy and toxicity between males and females. This review aims to highlight the importance of incorporating sex dimorphism in kidney in vitro models by examining the sexual characteristics in the context of the current state-of-the-art. Furthermore, this review underscores opportunities for improving kidney models by incorporating sex-specific traits. Ultimately, this roadmap to incorporating sex-dimorphism in kidney in vitro models will facilitate the creation of better models for studying sex-specific mechanisms in the kidney and their impact on drug efficacy and safety.
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations

Emotion recognition in conversations is essential for ensuring advanced human-machine interactions. However, creating robust and accurate emotion recognition systems in real life is challenging, mainly due to the scarcity of emotion datasets collected in the wild and the inability to take into account the dialogue context. The CEMO dataset, composed of conversations between agents and patients during emergency calls to a French call center, fills this gap. The nature of these interactions highlights the role of the conversation’s emotional flow in predicting patient emotions, as context can often make a difference in understanding observed emotional expressions. This paper presents a multi-scale conversational context learning approach for speech emotion recognition, which takes advantage of this hypothesis. We investigated this approach on both speech transcriptions and acoustic segments. Experimentally, our method uses the previous or next information of the targeted segment. In the text domain, we tested the context window using a wide range of tokens (from 10 to 100) and at the speech turns level, considering inputs from both the same and opposing speakers. According to our tests, the context derived from previous tokens has a more significant influence on accurate prediction than the following tokens. Furthermore, taking the last speech turn of the same speaker in the conversation seems useful. In the acoustic domain, we conducted an in-depth analysis of the impact of the surrounding emotions on the prediction. While multi-scale conversational context learning using Transformers can enhance performance in the textual modality for emergency call recordings, incorporating acoustic context is more challenging.
An investigation of the"10 keV feature"in the spectra of Accretion Powered X-ray Pulsars with NuSTAR

Some of the accreting X-ray pulsars are reported to exhibit a peculiar spectral feature at $\sim$10 keV, known as the"10 keV feature". The feature has been characterized as either an emission line or an absorption line, and its origin is unknown. It has been found in multiple observations of the same source by different observatories, but not all the observations of any particular source consistently showed the presence of it. In this work, we have carried out a systematic investigation for the presence of the"10 keV feature"using data from NuSTAR, a low background spectroscopic observatory having uninterrupted wide band coverage on either side of 10 keV. We performed a systematic spectral analysis on 58 archival NuSTAR observations of 30 bright X-ray pulsars. The 3$-$79 keV spectral continua of these selected sources were fitted with a model chosen on the basis of its fitting quality in 3$-$15 keV and model simplicity, and then inspected for the presence of the"10 keV feature". Our analysis indicates the presence of such a feature in 16 out of 58 the NuSTAR observations of 11 different sources and is fitted with a Gaussian absorption model centered around 10 keV. Our analysis also suggests that such a feature could be wrongly detected if flare data is not analyzed separately from persistent emission.
A Review of Differentiable Digital Signal Processing for Music & Speech Synthesis

The term"differentiable digital signal processing"describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music&speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably. Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research.
Longest-chain Attacks: Difficulty Adjustment and Timestamp Verifiability

We study an adversary who attacks a Proof-of-Work (POW) blockchain by selfishly constructing an alternative longest chain. We characterize optimal strategies employed by the adversary when a difficulty adjustment rule al\`a Bitcoin applies. As time (namely the times-tamp specified in each block) in most permissionless POW blockchains is somewhat subjective, we focus on two extreme scenarios: when time is completely verifiable, and when it is completely unverifiable. We conclude that an adversary who faces a difficulty adjustment rule will find a longest-chain attack very challenging when timestamps are verifiable. POW blockchains with frequent difficulty adjustments relative to time reporting flexibility will be substantially more vulnerable to longest-chain attacks. Our main fining provides guidance on the design of difficulty adjustment rules and demonstrates the importance of timestamp verifiability.
A Framework for Responsible Development of Automated Student Feedback with Generative AI

Providing rich feedback to students is essential for supporting student learning. Recent advances in generative AI, particularly within large language modelling (LLM), provide the opportunity to deliver repeatable, scalable and instant automatically generated feedback to students, making abundant a previously scarce and expensive learning resource. Such an approach is feasible from a technical perspective due to these recent advances in Artificial Intelligence (AI) and Natural Language Processing (NLP); while the potential upside is a strong motivator, doing so introduces a range of potential ethical issues that must be considered as we apply these technologies. The attractiveness of AI systems is that they can effectively automate the most mundane tasks; but this risks introducing a"tyranny of the majority", where the needs of minorities in the long tail are overlooked because they are difficult to automate. Developing machine learning models that can generate valuable and authentic feedback requires the input of human domain experts. The choices we make in capturing this expertise -- whose, which, when, and how -- will have significant consequences for the nature of the resulting feedback. How we maintain our models will affect how that feedback remains relevant given temporal changes in context, theory, and prior learning profiles of student cohorts. These questions are important from an ethical perspective; but they are also important from an operational perspective. Unless they can be answered, our AI generated systems will lack the trust necessary for them to be useful features in the contemporary learning environment. This article will outline the frontiers of automated feedback, identify the ethical issues involved in the provision of automated feedback and present a framework to assist academics to develop such systems responsibly.