Miracle-dz/UniFAR-FTUs huggingface dataset:
Field modulation of the electronic structure of trilayer graphene

Bandgaps play a central role in modern device physics, and a tunable bandgap can provide great flexibility in device design. Herein, an investigation of trilayer graphene modulated using an external electric field is presented. The calculations for trilayer graphene with hexagonal, Bernal, and rhombohedral stacking have been carried out. It was found that the bandgap of rhombohedral trilayer graphene can be tuned using an electric field. Symmetry plays a crucial role in the modulation the bandgap of trilayer graphene through an electric field.
Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation

Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial networks (GANs), which typically suffer from notorious mode collapse and unstable training, thus making it difficult to learn accurate audio-gesture joint distributions. In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation. Specifically, we first establish the diffusion-conditional generation process on clips of skeleton sequences and audio to enable the whole framework. Then, a novel Diffusion Audio-Gesture Transformer is devised to better attend to the information from multiple modalities and model the long-term temporal dependency. Moreover, to eliminate temporal inconsistency, we propose an effective Diffusion Gesture Stabilizer with an annealed noise sampling strategy. Benefiting from the architectural advantages of diffusion models, we further incorporate implicit classifier-free guidance to trade off between diversity and gesture quality. Extensive experiments demonstrate that DiffGesture achieves state-of-the-art performance, which renders coherent gestures with better mode coverage and stronger audio correlations. Code is available at https://github.com/Advocate99/DiffGesture.
Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel methods. Our scheme is an approximate realization of the power method, where supervised learning is used to learn the next step of the power iteration. We show that the ground state properties of arbitrary gapped quantum hamiltonians can be reached with polynomial resources under the assumption that the supervised learning is efficient. Using kernel ridge regression, we provide numerical evidence that the learning assumption is verified by applying our scheme to find the ground states of several prototypical interacting many-body quantum systems, both in one and two dimensions, showing the flexibility of our approach.
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model

Wav2vec 2.0 (W2V2) has shown impressive performance in automatic speech recognition (ASR). However, the large model size and the non-streaming architecture make it hard to be used under low-resource or streaming scenarios. In this work, we propose a two-stage knowledge distillation method to solve these two problems: the first step is to make the big and non-streaming teacher model smaller, and the second step is to make it streaming. Specially, we adopt the MSE loss for the distillation of hidden layers and the modified LF-MMI loss for the distillation of the prediction layer. Experiments are conducted on Gigaspeech, Librispeech, and an in-house dataset. The results show that the distilled student model (DistillW2V2) we finally get is 8x faster and 12x smaller than the original teacher model. For the 480ms latency setup, the DistillW2V2's relative word error rate (WER) degradation varies from 9% to 23.4% on test sets, which reveals a promising way to extend the W2V2's application scope.
MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks

Deep learning based methods provide efficient solutions to medical image registration, including the challenging problem of diffeomorphic image registration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. We desire an efficient solution to jointly account for spatial deformations and appearance changes in the pathological regions where the correspondences are missing, i.e., finding a solution to metamorphic image registration. Some approaches are proposed to tackle this problem, but they cannot properly handle large pathological regions and deformations around pathologies. In this paper, we propose a deep metamorphic image registration network (MetaRegNet), which adopts time-varying flows to drive spatial diffeomorphic deformations and generate intensity variations. We evaluate MetaRegNet on two datasets, i.e., BraTS 2021 with brain tumors and 3D-IRCADb-01 with liver tumors, showing promising results in registering a healthy and tumor image pair. The source code is available online.
Rethinking Model Ensemble in Transfer-based Adversarial Attacks

It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different models, which enables black-box attacks without any knowledge of the victim model. An effective strategy to improve the transferability is attacking an ensemble of models. However, previous works simply average the outputs of different models, lacking an in-depth analysis on how and why model ensemble methods can strongly improve the transferability. In this paper, we rethink the ensemble in adversarial attacks and define the common weakness of model ensemble with two properties: 1) the flatness of loss landscape; and 2) the closeness to the local optimum of each model. We empirically and theoretically show that both properties are strongly correlated with the transferability and propose a Common Weakness Attack (CWA) to generate more transferable adversarial examples by promoting these two properties. Experimental results on both image classification and object detection tasks validate the effectiveness of our approach to improving the adversarial transferability, especially when attacking adversarially trained models. We also successfully apply our method to attack a black-box large vision-language model -- Google's Bard, showing the practical effectiveness. Code is available at \url{https://github.com/huanranchen/AdversarialAttacks}.
Learning Cross-Lingual Visual Speech Representations

Cross-lingual self-supervised learning has been a growing research topic in the last few years. However, current works only explored the use of audio signals to create representations. In this work, we study cross-lingual self-supervised visual representation learning. We use the recently-proposed Raw Audio-Visual Speech Encoders (RAVEn) framework to pre-train an audio-visual model with unlabelled multilingual data, and then fine-tune the visual model on labelled transcriptions. Our experiments show that: (1) multi-lingual models with more data outperform monolingual ones, but, when keeping the amount of data fixed, monolingual models tend to reach better performance; (2) multi-lingual outperforms English-only pre-training; (3) using languages which are more similar yields better results; and (4) fine-tuning on unseen languages is competitive to using the target language in the pre-training set. We hope our study inspires future research on non-English-only speech representation learning.
Octahedral distortions in SrNbO$_3$: Unraveling the structure-property relation

Strontium niobate has triggered a lot of interest as a transparent conductor and as a possible realization of a correlated Dirac semi-metal. Using the lattice parameters as a tunable knob, the energy landscape of octahedral tilting was mapped using density functional theory calculations. We find that biaxial compressive strain induces tilting around the out-of-plane axis, while tensile strain induces tilting around the two in-plane axes. The two competing distorted structures for compressive strain show semi-Dirac dispersions above the Fermi level in their electronic structure. Our density functional theory calculations combined with dynamical mean field theory (DFT+DMFT) reveals that dynamical correlations downshift these semi-Dirac like cones towards the Fermi energy. More generally, our study reveals that the competition between the in-phase and out-of-phase tilting in SrNbO$_3$ provides a new degree of freedom which allows for tuning the thermoelectric and optical properties. We show how the tilt angle and mode is reflected in the behavior of the Seebeck coefficient and the plasma frequency, due to changes in the band structure.
Deep Metric Learning for Unsupervised Remote Sensing Change Detection

Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cover, land use, human development analysis, and disaster response. The performance of existing RS-CD methods is attributed to training on large annotated datasets. Furthermore, most of these models are less transferable in the sense that the trained model often performs very poorly when there is a domain gap between training and test datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues. Given an MT-RSI, the proposed method generates corresponding change probability map by iteratively optimizing an unsupervised CD loss without training it on a large dataset. Our unsupervised CD method consists of two interconnected deep networks, namely Deep-Change Probability Generator (D-CPG) and Deep-Feature Extractor (D-FE). The D-CPG is designed to predict change and no change probability maps for a given MT-RSI, while D-FE is used to extract deep features of MT-RSI that will be further used in the proposed unsupervised CD loss. We use transfer learning capability to initialize the parameters of D-FE. We iteratively optimize the parameters of D-CPG and D-FE for a given MT-RSI by minimizing the proposed unsupervised ``similarity-dissimilarity loss''. This loss is motivated by the principle of metric learning where we simultaneously maximize the distance between change pair-wise pixels while minimizing the distance between no-change pair-wise pixels in bi-temporal image domain and their deep feature domain. The experiments conducted on three CD datasets show that our unsupervised CD method achieves significant improvements over the state-of-the-art supervised and unsupervised CD methods. Code available at https://github.com/wgcban/Metric-CD
Thermal Feedback for Transparency in Human-Robot Interaction

Robots can support humans in tedious tasks, as well as provide social support. However, the decision-making and behavior of robots is not always clear to the human interaction partner. In this work, we discuss the opportunity of using thermal feedback as an additional modality to create transparent interactions. We then present scenarios where thermal feedback is incorporated into the interaction e.g. to unobtrusively communicate the behavior of the robot. We highlight the limitations and challenges of temperature-based feedback, which can be explored in future research.
The magnificent ACT of flavor-specific neutrino self-interaction

We revisit the cosmology of neutrino self-interaction and use the latest cosmic microwave background data from the Atacama Cosmology Telescope (ACT) and the Planck experiment to constrain the interaction strength. In both flavor-universal and nonuniversal coupling scenarios, we find that the ACT data prefers strong neutrino self-interaction that delays neutrino free streaming until just before the matter-radiation equality. When combined with the Planck 2018 data, the preference for strong interaction decreases due to the Planck polarization data. For the combined dataset, the flavor-specific interaction still provides a better fit to the CMB data than ΛCDM. This trend persists even when neutrino mass is taken into account and extra radiation is added. We also study the prospect of constraining such strong interaction by future terrestrial and space telescopes, and find that the upcoming CMB-S4 experiment will improve the upper limit on neutrino self-interaction by about a factor of three.
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by considering both parties’ capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams—compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when they are aware that an AI model delegated task instances to them. In an experimental study with 196 participants, we show that task performance and task satisfaction improve through AI delegation, regardless of whether humans are aware of the delegation. Additionally, we identify humans’ increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces.
Knowledge Distillation for Adaptive MRI Prostate Segmentation Based on Limit-Trained Multi-Teacher Models

With numerous medical tasks, the performance of deep models has recently experienced considerable improvements. These models are often adept learners. Yet, their intricate architectural design and high computational complexity make deploying them in clinical settings challenging, particularly with devices with limited resources. To deal with this issue, Knowledge Distillation (KD) has been proposed as a compression method and an acceleration technology. KD is an efficient learning strategy that can transfer knowledge from a burdensome model (i.e., teacher model) to a lightweight model (i.e., student model). Hence we can obtain a compact model with low parameters with preserving the teacher's performance. Therefore, we develop a KD-based deep model for prostate MRI segmentation in this work by combining features-based distillation with Kullback-Leibler divergence, Lovasz, and Dice losses. We further demonstrate its effectiveness by applying two compression procedures: 1) distilling knowledge to a student model from a single well-trained teacher, and 2) since most of the medical applications have a small dataset, we train multiple teachers that each one trained with a small set of images to learn an adaptive student model as close to the teachers as possible considering the desired accuracy and fast inference time. Extensive experiments were conducted on a public multi-site prostate tumor dataset, showing that the proposed adaptation KD strategy improves the dice similarity score by 9%, outperforming all tested well-established baseline models.
Novel Algorithms for Efficient Mining of Connected Induced Subgraphs of a Given Cardinality

Mining subgraphs with interesting structural properties from networks (or graphs) is a computationally challenging task. In this paper, we propose two algorithms for enumerating all connected induced subgraphs of a given cardinality from networks (or connected undirected graphs in networks). The first algorithm is a variant of a previous well-known algorithm. The algorithm enumerates all connected induced subgraphs of cardinality $k$ in a bottom-up manner. The data structures that lead to unit time element checking and linear space are presented. Different from previous algorithms that either work in a bottom-up manner or a reverse search manner, an algorithm that enumerates all connected induced subgraphs of cardinality $k$ in a top-down manner is proposed. The correctness and complexity of the top-down algorithm are theoretically analyzed and proven. In the experiments, we evaluate the efficiency of the algorithms using a set of real-world networks from various fields. Experimental results show that the variant bottom-up algorithm outperforms the state-of-the-art algorithms for enumerating connected induced subgraphs of small cardinality, and the top-down algorithm can achieve an order of magnitude speedup over the state-of-the-art algorithms for enumerating connected induced subgraphs of large cardinality.
FedPH: Privacy-enhanced Heterogeneous Federated Learning

Federated Learning is a distributed machine-learning environment that allows clients to learn collaboratively without sharing private data. This is accomplished by exchanging parameters. However, the differences in data distributions and computing resources among clients make related studies difficult. To address these heterogeneous problems, we propose a novel Federated Learning method. Our method utilizes a pre-trained model as the backbone of the local model, with fully connected layers comprising the head. The backbone extracts features for the head, and the embedding vector of classes is shared between clients to improve the head and enhance the performance of the local model. By sharing the embedding vector of classes instead of gradient-based parameters, clients can better adapt to private data, and communication between the server and clients is more effective. To protect privacy, we propose a privacy-preserving hybrid method that adds noise to the embedding vector of classes. This method has a minimal effect on the performance of the local model when differential privacy is met. We conduct a comprehensive evaluation of our approach on a self-built vehicle dataset, comparing it with other Federated Learning methods under non-independent identically distributed(Non-IID).
ProCC: Progressive Cross-Primitive Compatibility for Open-World Compositional Zero-Shot Learning

Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions. Existing works either learn the joint compositional state-object embedding or predict simple primitives with separate classifiers. However, the former method heavily relies on external word embedding methods, and the latter ignores the interactions of interdependent primitives, respectively. In this paper, we revisit the primitive prediction approach and propose a novel method, termed Progressive Cross-primitive Compatibility (ProCC), to mimic the human learning process for OW-CZSL tasks. Specifically, the cross-primitive compatibility module explicitly learns to model the interactions of state and object features with the trainable memory units, which efficiently acquires cross-primitive visual attention to reason high-feasibility compositions, without the aid of external knowledge. Moreover, to alleviate the invalid cross-primitive interactions, especially for partial-supervision conditions (pCZSL), we design a progressive training paradigm to optimize the primitive classifiers conditioned on pre-trained features in an easy-to-hard manner. Extensive experiments on three widely used benchmark datasets demonstrate that our method outperforms other representative methods on both OW-CZSL and pCZSL settings by large margins.
Distinct microbial communities across a climatically versatile summit in the Lesotho highlands

Abstract Most studies investigating the effects of climatological factors on microbial community composition and diversity focus on comparisons of geographically distinct environments (e.g., cold vs hot deserts) or across various temporal scales. Mountain regions provide unique environments to explore relationships between various environmental factors and soil microorganisms given their range of microclimatic conditions and vegetation types. This study investigated micro‐topographically (i.e., north‐/south‐facing slope aspects and flat plateau between them) controlled microbial diversity and community structures across a Lesotho mountain summit. Amplicon sequence analysis revealed that the north‐ and south‐facing slopes were dominated by more Proteobacteria and Bacteroidetes, while the plateau was dominated by more Acidobacteria. Fungi from the phylum Chytridiomycota more strongly dominated the plateau and the north‐facing slope than the south‐facing slope. Slope aspect, through its direct influence on air and soil micro‐climatology and plant diversity, significantly affects bacterial and fungal community structures at this location. These results provide original insight into soil microbial diversity in the Lesotho highlands and offer an opportunity to project the likely response of soil microorganisms to future climate warming in highly variable mountain environments such as the Lesotho highlands.
“All‐on‐4” and “All‐on‐6” treatment concept applied using computer‐guided surgery in a patient: Case report with a 2‐year follow‐up

This case aimed to assess the clinical and radiographic outcomes of the All‐on‐4 and the All‐on‐6 concept using three‐dimensional computer‐assisted treatment planning, and computer‐guided surgery. Two years after the treatment, the “All‐on‐4” and “All‐on‐6” concepts provided effective treatment for immediate restoration and showed predictable outcomes in a completely edentulous patient.
Biomimetic tissue phantoms for neurosurgical near-infrared fluorescence imaging

Abstract. Significance Neurosurgical fluorescence imaging is a well-established clinical approach with a growing range of indications for use. However, this technology lacks effective phantom-based tools for development, performance testing, and clinician training. Aim Our primary aim was to develop and evaluate 3D-printed phantoms capable of optically and morphologically simulating neurovasculature under fluorescence angiography. Approach Volumetric digital maps of the circle of Willis with basilar and posterior communicator artery aneurysms, along with surrounding cerebral tissue, were generated. Phantoms were fabricated with a stereolithography printer using custom photopolymer composites, then visualized under white light and near-infrared fluorescence imaging. Results Feature sizes of printed components were found to be within 13% of digital models. Phantoms exhibited realistic optical properties and convincingly recapitulated fluorescence angiography scenes. Conclusions Methods identified in this study can facilitate the development of realistic phantoms as powerful new tools for fluorescence imaging.